JP2016198873A - Optimum control device, optimum control method, and optimum control program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To quickly determine an optimum solution to an optimization problem in a model prediction control.SOLUTION: An optimum control device comprises: contact point planning means setting a contact point plan of moving means of a moving robot; and trajectory generation means. The trajectory generation means performs a model prediction control of calculating a state variable of a center of gravity using an evaluation criterion in a prediction zone, and generating a gravity trajectory on the basis of the calculated state variable of the center of gravity. The evaluation criterion minimizes an evaluation function including a square of a quantity based on a contact force at each contact point in the prediction zone. An equation constraint conditional optimization problem including the evaluation criterion, a state equation, and an equation constraint condition expressed by a moving robot linear equation is converted into a non-constraint conditional optimization problem that does not include the equation constraint condition using an orthogonal complementary space. The trajectory generation means determines an optimum solution to this non-constraint conditional optimization problem using a recursive calculation method, and calculates the state variable of the center of gravity on the basis of the determined optimum solution.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an optimal control apparatus, an optimal control method, and a control program for performing model predictive control of a mobile robot.

  For example, an optimal control device that performs model predictive control (seeding horizon control) on a mechanical link system of a robot is known (see Patent Document 1).

JP 2000-330609 A

  In the model predictive control, a physical constraint condition of the robot is set. Then, the optimization problem with constraints is solved for each control cycle, and the center of gravity trajectory of the robot is generated based on the obtained optimum solution. However, conventionally, there has been a problem that much time is required for finding the optimum solution.

  The present invention has been made to solve such problems, and an optimal control apparatus, an optimal control method, and a control program capable of generating an optimal solution of an optimization problem in model predictive control at high speed and generating a center-of-gravity trajectory. The main purpose is to provide

One aspect of the present invention for achieving the above object is that a position of a contact point where the moving means of the mobile robot that moves while alternately grounding two or more moving means contacts the position of the moving means when contacting the ground. Contact point planning means for setting a contact point plan with posture as time series data, and based on the contact point plan set by the contact point plan setting means, the moving means contacts the contact point while A trajectory generating means for generating a center-of-gravity trajectory for moving the mobile robot, wherein the trajectory generating means is configured to input an amount based on a contact force when the moving means is grounded A model is constructed to represent the state variable of the center of gravity of the mobile robot in the prediction interval of a predetermined time width by the prediction model, and the state of the center of gravity is determined using a predetermined evaluation criterion in the prediction interval. The model is subjected to model predictive control for generating a center-of-gravity trajectory of the mobile robot based on the calculated center-of-gravity state variable, and the evaluation criterion includes the square of the amount based on the contact force at each contact point The evaluation function is minimized within the prediction interval, and the evaluation criterion, the linear state equation indicating the relationship between the input based on the contact force and the state variable of the center of gravity, and the linear equation of the mobile robot An equality constraint optimization problem including an equality constraint expressed is converted into an unconstrained optimization problem that does not include the equality constraint using orthogonal complement space, and the trajectory generation The means solves the converted unconstrained optimization problem using the recursive calculation method in the prediction interval, and calculates a state variable of the center of gravity based on the obtained optimal solution. That It is optimal controller for the symptoms.
In this aspect, the trajectory generating means constructs a prediction model that receives a differential value of a contact force when the moving means is grounded, and the evaluation criterion is set corresponding to each contact point. A criterion for allocating the contact force and a differential value of the contact force to each contact point based on a weight is included, and an evaluation function including a sum of squares of the contact force and the differential value of the contact force is included in a prediction interval. In the equation, the evaluation criterion, the state equation indicating the relationship between the input of the differential value of the contact force and the state variable of the center of gravity, and the equation constraint indicating the constraint of the balance of the force of the mobile robot And an equality constraint optimization problem including the above may be converted into an unconstrained optimization problem that does not include the equality constraint condition using orthogonal complement space.
In this aspect, a state equation showing the relation between the input of the differential value of the contact force and the state variable of the center of gravity is derived by performing QR decomposition on the equation showing the equation constraint QR conversion is performed on the expression derived from the above, and an input conversion expression is derived. Based on the state equation, the state variable conversion expression, the input conversion expression, and the state variable conversion expression The state equation conversion formula is derived, and the evaluation function conversion formula is derived based on the derived state variable conversion formula, the input conversion formula, and the evaluation function of the optimization problem with equality constraints. And
The unconstrained optimization problem may include a conversion equation for the derived evaluation function and a conversion equation for the state equation.
In this aspect, the trajectory generating means solves an optimal solution using a recursive calculation method with respect to an optimal solution condition of an expression expressing the unconstrained optimization problem as a matrix, and the calculated optimal solution Further, the time series data of the state variable of the center of gravity may be calculated based on a state variable conversion expression derived by QR decomposition of an equation indicating the equation constraint.
In this aspect, the equality constraint condition may include an input set so that the contact force does not change only within a predetermined interval.
In this aspect, the trajectory generating means performs an equality constraint including the equality constraint and an inequality constraint indicating a stability constraint of the contact point and an optimization problem with an inequality constraint in an orthogonal complement space. An unconstrained optimization problem that has been converted by using an optimal solution may be obtained using a recursive calculation method, and time series data of the state variable of the center of gravity may be calculated based on the obtained optimal solution.
In this aspect, the trajectory generating means applies a Newton method to an optimal solution condition of an expression expressing the unconstrained optimization problem as a matrix, and the recursive calculation is performed in a convergence operation of the Newton method. A Newton direction may be calculated using a method, and an optimal solution may be calculated based on the calculated Newton direction.
In this aspect, the trajectory generating means may apply an interior point method or an active set method to an optimal solution condition of an expression expressing the unconstrained optimization problem as a matrix.
In this aspect, the inequality constraint condition may include an input set to limit the contact force only within a predetermined interval.
In this aspect, the state equation of the optimization problem may include a linear time-varying control parameter.
In this aspect, the apparatus may further include a control unit that controls the moving unit based on the center of gravity trajectory generated by the trajectory generating unit.
One aspect of the present invention for achieving the above object is that a position of a contact point where the moving means of the mobile robot that moves while alternately grounding two or more moving means contacts the position of the moving means when contacting the ground. A step of setting a contact point plan in which the posture is time-series data, and a center of gravity trajectory for moving the mobile robot while the moving means contacts the contact point based on the set contact point plan Generating a prediction model having an input based on a contact force when the moving means is grounded, and using the prediction model in a prediction section having a predetermined time width. The state variable of the center of gravity of the mobile robot is represented, and the state variable of the center of gravity is calculated using a predetermined evaluation criterion in the prediction section, and the state variable of the center of gravity is calculated based on the calculated state variable of the center of gravity. The model predictive control for generating the center of gravity trajectory of the mobile robot is performed, and the evaluation criterion is to minimize an evaluation function including a square of an amount based on the contact force at each contact point within the prediction interval, and the evaluation Optimal with equality constraints including a criterion, a linear state equation indicating a relationship between the input based on the contact force and the state variable of the center of gravity, and an equality constraint expressed by a linear equation of the mobile robot The conversion problem is converted into an unconstrained optimization problem that does not include the equality constraint condition using orthogonal complement space, and the converted unconstrained optimization problem is recursively calculated in the prediction interval. The optimal control method may be characterized in that an optimal solution is obtained using a method and a state variable of the center of gravity is calculated based on the obtained optimal solution.
One aspect of the present invention for achieving the above object is that a position of a contact point where the moving means of the mobile robot that moves while alternately grounding two or more moving means contacts the position of the moving means when contacting the ground. A process for setting a contact point plan with the posture as time-series data, and a center-of-gravity trajectory for moving the mobile robot while the moving means contacts the contact point based on the set contact point plan An optimal control program for causing a computer to execute a process of generating a prediction model that inputs an amount based on a contact force when the moving means is grounded, and has a predetermined time width by the prediction model. The state variable of the center of gravity of the mobile robot in the prediction section is represented, the state variable of the center of gravity is calculated using a predetermined evaluation criterion in the prediction section, and the state change of the calculated center of gravity is calculated. The model predictive control for generating the center-of-gravity trajectory of the mobile robot is performed based on the evaluation criteria. And including the evaluation criteria, a linear state equation indicating the relationship between the input based on the contact force and the state variable of the center of gravity, and an equation constraint expressed by a linear equation of the mobile robot, etc. An optimization problem with an expression constraint is converted into an unconstraint optimization problem that does not include the equality constraint using an orthogonal complement space, and the converted unconstraint optimization problem in the prediction interval May be an optimal control program that calculates an optimal solution using a recursive calculation method and calculates the state variable of the center of gravity based on the determined optimal solution.

  ADVANTAGE OF THE INVENTION According to this invention, the optimal control apparatus, the optimal control method, and control program which can obtain | require the optimal solution of the optimization problem in model predictive control at high speed, and can produce | generate a gravity center locus | trajectory can be provided.

The figure which shows an example of operation | movement of a mobile robot. It is. It is a figure which shows an example of the machine structure of a mobile robot. It is a functional block diagram of a mobile robot. It is a figure which shows 6 axial force. It is a functional block diagram of an optimal control apparatus. It is a figure which shows an example of the outline | summary of a contact point plan. It is a figure which shows an example of a contact point plan. It is a figure which shows the example of a prediction area. It is a figure showing the motion in a prediction area. It is a figure for demonstrating the shift of a prediction area. It is a figure which shows the model of the mobile robot used for prediction. It is a figure for demonstrating the discretization of a prediction area. It is an image figure of orthogonal complement space. It is a figure which shows the flow at the time of converting into the LQ optimization problem of an unconstrained condition. It is a flowchart which shows the optimal control method. It is a figure which shows the coordinate system and contact polygon of a contact point. It is the figure which illustrated the case where a contact point became unstable. It is a flowchart which shows the solution flow of the optimal solution of an LQ optimization problem with an equality constraint condition and an inequality constraint condition.

An embodiment of the present invention will be illustrated and described with reference to reference numerals attached to elements in the drawing.
(First embodiment)
This embodiment is characterized by an optimal control device for a mobile robot. Specifically, the present embodiment has a feature in generating a trajectory for controlling the movement operation (FIG. 1) of the mobile robot. Before describing (generation), the hardware configuration of the mobile robot to be controlled will be described in advance.

FIG. 2 is a diagram illustrating an example of the mechanical configuration of the mobile robot.
The mobile robot 100 has three axes for the hip joint, one axis for the knee joint, two axes for the ankle joint, three axes for the shoulder joint (shoulder pitch, shoulder roll, shoulder yaw), one axis for the elbow joint (elbow pitch), The wrist joint is composed of three axes (wrist yaw, wrist pitch, wrist roll).

(The mechanical configuration of the mobile robot is not limited to this, but the degree of freedom of the hand (arm) is 6 or more and the degree of freedom of the foot (leg) is 6 or more.)
The mobile robot 100 has motors 1, 2,..., 28 with encoders at each joint.
The joint motors 1a, 2a,..., 28a (FIG. 3) can adjust the joint angles θ1, θ2,.
On the other hand, the encoders 1b, 2b,..., 28b of each joint can measure the joint angles θ1, θ2,.

In addition, the mobile robot 100 has a contact force sensor 25 at a foot tip (foot portion) and a hand tip (palm portion).
Here, the contact force is a six-axis force. As shown in FIG. 4, a set of forces f in the x-axis, y-axis, and z-axis directions (f _x , f _y , f _z ) ^T , A set of forces τ around the y-axis and the z-axis (τ _x , τ _y , τ _z ) ^T.
(The x-axis and the y-axis are axes orthogonal to each other in a plane perpendicular to the z-axis, which is the vertical direction.)

The mobile robot moves while contacting one or more of the right foot, left foot, right hand, and left hand with the floor, wall, table, or the like.
Therefore, in the following description of the present specification, the right foot, the left foot, the right hand, and the left hand may be referred to as contact point candidates. The hand and foot are specific examples of the moving means.

FIG. 3 is a functional block diagram of the mobile robot 100.
The mobile robot 100 includes motors 1 a to 24 a and encoders 1 b to 24 b of each joint, a contact force sensor 25, and an optimal control device 210.

  Sensor detection values are input to the optimal control device 210 from the encoders 1b to 24b and the contact force sensor 25 of each joint. Moreover, the optimal control apparatus 210 outputs a drive signal with respect to the motors 1a-24a of each joint.

  The optimal control device 210 includes a CPU (Central Processing Unit) 210a that performs control processing, arithmetic processing, and the like as a main hardware configuration, and a ROM (Read Only Memory) that stores control programs, arithmetic programs, and the like executed by the CPU 210a. ) 210b and a microcomputer having RAM (Random Access Memory) 210c for temporarily storing processing data and the like. The CPU 210a, ROM 210b, and RAM 210c are connected to each other by a data bus 210d. Necessary programs may be recorded on a non-volatile recording medium and installed as necessary.

  FIG. 5 is a functional block diagram of the optimal control apparatus 210 according to an embodiment of the present invention. The optimal control apparatus 210 according to the present embodiment includes a contact point plan setting unit (one specific example of contact point planning means) 221 that sets a contact point plan of the mobile robot 100 and a center of gravity trajectory of the mobile robot 100 that can be stably executed. A trajectory generation unit (a specific example of the trajectory generation unit) 222 to be generated, and an operation control unit (a specific example of the control unit) 223 that executes the whole body motion of the mobile robot 100 according to the generated center-of-gravity trajectory.

Here, the trajectory generation unit 222 generates a center of gravity trajectory that can execute an operation according to the contact point plan, and this center of gravity trajectory generation includes a change of the contact point as necessary.
Specific processing operations of these functional units will be described later.

(Orbit generation method for multi-point contact movement)
The trajectory generation unit 222 according to the present embodiment generates (1) a center-of-gravity trajectory that can realize multipoint contact movement, and (2) changes the contact point as necessary. Here, (1) a method for generating a center of gravity trajectory capable of realizing multipoint contact movement will be described. The present applicant has applied for this method in Japanese Patent Application No. 2013-254989 (filed on Dec. 10, 2013).

  In the first place, the future target center-of-gravity position cannot be known in advance, and it is impossible for the user to set the future center-of-gravity position that should be unknown as the control target value. The user gives the robot only the contact point plan information, and then the mobile robot automatically generates a stable center of gravity trajectory based on the set contact point plan information and moves autonomously. Is desirable.

  Now, in order to make the mobile robot perform multi-point contact movements stably, a technology that distributes contact force smoothly and appropriately according to contact points that change from moment to moment, and generates a stable center of gravity trajectory. is necessary.

For this purpose, the trajectory generation unit 222 according to the present embodiment generates a barycentric trajectory using model predictive control (so-called receiving horizon control).
First, an outline of model predictive control will be described.

(Overview of model predictive control)
For example, assume that the mobile robot wants to perform a moving operation as illustrated in FIG.
Here, a series of operations is assumed in which a humanoid mobile robot having two arms and two legs grips a bottle on the back side of the table.

In this case, the contact point plan setting unit 221 creates a contact point plan that can execute this series of operations (tasks) based on the contact point plan information instructed by the user.
In other words, the contact point plan setting unit 221 creates a plan as to, for example, as shown in FIG. 6, in which order, where and how to get the hands and feet.
In FIG. 6, the floor, the wall, and the table are marked at locations where the feet and the hands are brought into contact.

This contact point plan is specifically as shown in FIG.
The contact point plan is time-series data regarding which order, where, and how to arrive for the left hand (LH), right hand (RH), left foot (LF), and right foot (RF).

The correspondence between FIGS. 1, 6 and 7 will be briefly described.
Initially (t0) Stand with only one left foot, swing out the right foot, which is a free leg, and land the right foot (t1).
In order to cause the mobile robot 100 to execute the contact point plan according to this movement, the coordinates P _LF1 of the contact point on the floor on which the left foot first lands, the posture r _LF1 of the left foot at that time, and the right foot landing It is necessary to specify the coordinates P _RF1 of the contact point on the floor to be performed and the posture r _RF1 of the right foot at that time.

Here, the coordinates of the contact point are represented as a set of P = (P _x , P _y , P _z ) as spatial coordinates.
Further, the posture is the direction of the back surface of the foot when landing on the contact point, and is represented as, for example, r = (r _x , r _y , r _z ) as a set of Euler angles.
(Ie, r _x , r _y and r _z represent roll, pitch and yaw angles, respectively)
Since the format for instructing the coordinates of the contact point on the foot and the posture at that time will be the same in the following description, the description will be omitted as appropriate.

After standing with both feet, swing out the left foot (t ₂ ) and land the left foot forward (t ₄ ).
In the meantime, the left hand is put on the wall (t ₃ ).
Here, the coordinates P _LH1 of the contact point on the wall that _wears the left hand and the posture r _LH1 of the left hand at that time are designated.

The coordinates of the contact point are expressed as a set of space coordinates P = (P _x , P _y , P _z ), and the posture is set as a set of Euler angles as a set of Euler angles when reaching the contact point. _{x, r} y, expressed as _{r z).}

Subsequent contact point plans will be omitted if they can be understood by comparing FIGS. 1, 6 and 7. FIG.
In this way, the contact point plan setting unit 221 creates the contact point plan as time series data.

  The trajectory generation unit 222 generates a center of gravity trajectory so as to realize the contact point plan set by the contact point plan setting unit 221 as described above. The motion control unit 223 controls the motors 1a to 24a of the joints so that the mobile robot 100 operates in the whole body according to the center of gravity trajectory generated by the trajectory generation unit 222. Thereby, the mobile robot 100 can move autonomously according to a stable center of gravity trajectory based on the set contact point plan.

At this time, the trajectory generation unit 222 performs model prediction control when generating the trajectory.
That is, the trajectory generation unit 222 generates a trajectory that the mobile robot 100 can stably move within a prediction interval having a certain time width, and sequentially updates the trajectory that can perform a stable operation while shifting the prediction interval by a minute time (Δt). Try to do.

For example, FIG. 8 shows an example of a prediction interval.
The trajectory generation unit 222 sets the future interval ahead of a predetermined time (for example, 1.6 seconds) from the present time as the prediction interval.

Then, the trajectory generator 222 generates a stable trajectory so as not to diverge between the prediction intervals.
FIG. 9 is an image of the motion in the prediction interval.
As described above, the trajectory generation unit 222 generates a stable trajectory in a prediction interval having a certain time width, and uses only the first point as the current input value.

  The trajectory generation unit 222 shifts the prediction interval in the next trajectory update period (after Δt seconds), and similarly generates a stable trajectory in the new prediction interval (see FIG. 10).

Instead of looking only at the present time or only from the present to the next control cycle (Δt seconds), as described above, a certain future is set as the prediction interval, and a trajectory that does not diverge within this prediction interval is generated. Like that.
By repeating this, the mobile robot can move stably.

  Now, the problem here is to generate a stable center of gravity trajectory that distributes the contact force smoothly and appropriately according to the contact points that change from moment to moment in the prediction interval with a certain time width. What should I do?

The present inventors have come up with the idea of reducing the problem of generating stable trajectories in a certain prediction interval to an LQ (Linear Quadratic) optimization problem (Quadratic Programming Problem: QP).
Specifically, the trajectory generation unit 222 minimizes the evaluation function J including the sum of squares of the contact force at each contact point and the sum of squares of the differential values of the six-axis forces (contact forces). The stable trajectory of multipoint contact movement is obtained by solving the optimization problem.

Therefore, next, the derivation of the evaluation function J and its solution (reduction to the LQ optimization problem) will be described.
This solution shows that time series data of the center of gravity position, the center of gravity speed, the contact force, and the differential value of the contact force can be obtained in order to realize stable multipoint contact movement within a certain prediction section. (In the following explanation, first consider only putting your limbs on the position (contact point) as instructed in the contact point plan. In addition, if necessary, introduce slack variables, etc. The contact point may be changed by performing a treatment such as relieving.

A model of the mobile robot used for the prediction is shown again in FIG.
The inertia of the entire mobile robot is represented by one center of gravity G. A 6-axis force is defined for each contact point.

  At this time, if the translational momentum of the center of gravity G is P, the rotational momentum (angular momentum) around the center of gravity is L, and the number of contact points is n, the equation of motion can be written as follows.

The subscript i represents the index of the contact point.
For example, if the contact point candidates are four points of the left hand, right hand, left foot, and right foot, n = 4 (left hand: LH = 1, right hand: RH = 2, left foot: LF = 3, right foot: RF = 4). That's fine. However, for the contact point candidates that are not in contact with the floor or wall, the constraint condition is set so that the contact force is zero. For example, in the example of FIG.

  Differentiating the first expression and the second expression of the expression (1) yields the following expression. (Equation (1) is expressed as a vector, but after decomposing it into x, y, and z, they are called the first equation, the second equation,...

  In this embodiment, these two types are used as a system. Then, Formulas 3 to 5 of Formula (1) are formulated as constraint conditions.

  Further, the prediction interval is divided into N intervals as shown in FIG. 12, and equations (3) and (4) are discretized. The equation (3) is discretized as follows.

  Further, if the constraint of equation (4) always holds at the sampling point, equation (4) is discretized as follows.

  Here, the parameters are set as follows.

θ _i is a vector in which contact forces as six-axis forces are arranged. X is an x coordinate of the center of gravity G, an x-axis direction speed of the center of gravity G, a y-coordinate of the center of gravity G, a y-axis direction speed of the center of gravity G, and a contact force (six-axis force) at each contact point. Is a vector. This x is referred to as the state variable x of the center of gravity. Furthermore, u is a vector in which the differential values of the contact force (six-axis force) are arranged.

  When parameters are set in this way, equation (5) can be described as the next state equation.

  This equation (8) can describe the state variable x at the time of (j + 1) in the previous state. When the state variable x in the prediction interval is calculated in order using the equation (8), it is as follows.

  Therefore, when the state variables x obtained in time series are arranged and represented by capital letters X, the time series data X of the state variables can be represented as follows.

This equation (10) is a prediction model that represents the state transition of the mobile robot within a certain prediction interval with the differential value (u [k]) of the contact force as an input. In the above equation (10), the contact force may be input. In this case, the state variable x includes only the gravity center position and the gravity center speed. Further, the above equation (3) is a relational expression between G (2 dots) (second order differential) and f, and this relational expression is an expression in which the term of f (dot) in the above expression (5) is zero. From this, a linear equation of state like the above equation (8) can be derived.
Now, the present inventors have introduced the following evaluation criteria for the evaluation function J in order to generate a stable trajectory within the prediction interval.

Q _i and R _i are weights set as appropriate. For example, when the force is evenly distributed to all the contact point candidates, Q _i is all 1 and R _i can be set to 1 × 10 ⁻⁶ .

Here, θ _i is a vector in which components of contact force as 6-axis force are arranged. Therefore, the above expression (11) is an expression that means “minimize the sum of squares of the contact force (6-axis force) and the differential value of the contact force within the prediction interval”. The first term of the equation (11) means minimizing the sum of squares of the contact force (six-axis force).

This first term includes the following actions.
(1) Distribute the contact force to each contact point evenly. This has the effect of moving the center of gravity to the most stable position possible.
(2) To cancel unnecessary internal forces.
(3) To improve the grounding stability of the contact point. That is, there is an effect that the reaction force center point in the contact surface is set to the center of the contact surface.

  The second term of the above equation (11) means minimizing the sum of squares of the contact force (6-axis force) differential value (time change rate of 6-axis force).

This second term includes the following actions.
(1) To suppress the divergence of the center of gravity.
(2) Switching contact force smoothly.

Minimizing this evaluation function J by adding these appropriately by the weights of Q and R means that
"Stable center of gravity trajectory and contact force at each contact point are output while satisfying conditions such as high contact stability, smooth contact force transition, and minimum internal force"
It means that.

When the above equation (11) is discretized and rewritten into a general form, the following evaluation function (12) is obtained.

Next, an equation constraint condition (constraint constraint condition) indicating a constraint of balance of force of the mobile robot is considered.
As an equation constraint,
(1) Constraint that the 6-axis force is 0 with respect to the non-contact contact point candidate of the mobile robot,
(2) Vertical force balance constraint of the mobile robot, and
(3) Constraint on balance of moment force around xy axis of mobile robot,
Must hold over all sampling points in the prediction interval.

Here, for example, it is assumed that the i-th and i + 2th contact points are non-contact at a certain sampling point k.
At this time, the equality constraints (1) to (3) can be described as the following equation (13).

Note that the components of the coefficient matrices C _k and d _k differ depending on the sampling points, and information such as contact / non-contact of contact point candidates and contact point positions are set by the contact point plan setting unit 221. For example, p _ix [k], p _iy [k], and p _iz [k] in the above equation (13) are set by the contact point plan setting unit 221.

なお、上記（１４）式において、１行目の式（ｍｉｎＪ＝・・）は、上述の如く、予測区間内において、接触力と接触力の微分値との２乗和を最小化するという意味の式である。２行目の式（ｘ［ｋ＋１］＝・・）は、接触力の微分値の入力と重心の状態変数と関係を示す状態方程式である。３行目の式（Ｃ_ｋｘ［ｋ］＝ｄ_ｋ）は、移動ロボットの力の釣合いの拘束を示す等式制約条件である。 From the above, when the amount present state (initial value of the state variable) and _{x 0,} equation (8) and (12), and (13), the trajectory generating unit 222 of the optimal controller 210, the following The LQ optimization problem with equality constraints shown in the equation (14) is solved to generate the center of gravity trajectory.

In the above formula (14), the formula (minJ = ...) in the first row means that the sum of squares of the contact force and the differential value of the contact force is minimized within the prediction interval as described above. It is a formula. The expression (x [k + 1] =...) In the second row is a state equation indicating the relationship between the input of the differential value of the contact force and the state variable of the center of gravity. The expression (C _k x [k] = d _k ) in the third row is an equality constraint condition indicating the constraint of the balance of forces of the mobile robot.

  By the way, as described above, the optimal control device for a mobile robot performs model predictive control in order to generate a stable motion trajectory with multipoint contact. In this model predictive control, a physical constraint condition (the above-described equality constraint condition) of the mobile robot is set. Then, the optimal control device solves the LQ optimization problem for each control cycle, and performs control based on the obtained optimal solution. However, the solution of the optimum solution has conventionally required a lot of time, causing a delay in the cycle of model predictive control (orbit update cycle), resulting in a problem that the control performance cannot be improved.

In contrast, in the present embodiment, an LQ optimization problem with equality constraints is converted into an unconstrained LQ optimization problem using orthogonal complement space. Then, the trajectory generation unit 222 of the optimal controller 210 solves the converted unconstrained LQ optimization problem using the Riccati recursive calculation method (Riccati recursion) to find the optimal solution. Then, the trajectory generation unit 222 calculates the state variable of the center of gravity based on the obtained optimal solution, and generates the center of gravity trajectory based on the calculated state variable of the center of gravity.
By converting to an unconstrained LQ optimization problem using the orthogonal complement space, a fast and stable riccat type recursive calculation method can be used for the solution. As a result, the optimal solution of the LQ optimization problem can be obtained at high speed in model predictive control, and the center of gravity trajectory can be generated.

  The above Riccati recursion is a recursive calculation for an expression that shows the optimal solution condition (KKT (Karush-Kuhn-Tucker) condition) of the converted matrix expression. By doing this, the optimal solution of the optimization problem is obtained at high speed. The detailed calculation method has already been disclosed in a non-patent document (Parallel Implementation of Riccati Recursion for Solving Linear-Quadratic, Gianluca Frison John Bagterp Jorgensen) and the like, and this can be used.

Here, first, the above-described orthogonal complement space will be described in detail. The orthogonal complementary space is defined as (1)-(3) below.
(1) When the vectors included in the bases {v _i } ^k _{i = 1} and {u} ^m _{i = 1} of the two subspaces V and U are linearly independent, the base {v _i εR ⁿ } ^k _{i = 1 The} subspace spanned by ∪ is called the direct sum of V and U, and is expressed as U (+) V. Hereinafter, + is described as (+) in ○. In particular, when R ⁿ = R ^{k + m} = V (+) U holds, U is referred to as a V complement space.
(2) a subspace V⊂R ⁿ and subspace U⊂R ⁿ _{is, v T u = 0 for all} v ∈ V, when satisfying all u ∈ U, 2 two subspace that is orthogonal.
(3) When the subspace V and its complement space U are orthogonal, U is referred to as an orthogonal complement space of V and denoted as V ^⊥ .
The following propositions (4) to (5) are established based on the above definition.
(4) Vector set α = {xεR ⁿ | y ^T ₁ x = y ^T ₂ x =... = Y orthogonal to m (<n) linearly independent vectors {y _i } ^m _{i = 1} ^T _m x = 0} is an nm dimension subspace.
(5) Non-orthogonal basis {u _i ∈ R ⁿ } ⁿ _{i = 1} orthogonal subspace of subspace V = <u ₁ , u ₂ ,..., U _m > spanned by m basis vectors selected from ₁ by its biorthogonal basis ^{_{{v i ∈R n} n i}} = 1, V ⊥ = <v m + 1, v m + 2, ···, v n> represented by.

By way simply the proposition, _{C k} The ∈R ^{mk × nx} orthogonal complement C ^⊥ _k ∈R ^{nx ×} of ^(nx-mk), among the linear space of _{n x} × _{n x,} the remaining _{C k} in space (complement) of a space orthogonal to C _k. By converting the (14) equation LQ optimization problem using the orthogonal complement, as shown in FIG. 13, the x present on equality constraints C _{k x} = d _k, parallel to the C _k Can be represented by a constant vector σ that is orthogonal to the vector ζ and terminates at C _k . In other words, the equation constraint condition C _k x = d _k is always satisfied by changing the variable x to ζ using the orthogonal complement space, no matter how ζ is moved. Therefore, the LQ optimization problem can be solved under unconstrained conditions without considering this equality constraint condition.

Next, a conversion method using the above-described orthogonal complementary space (hereinafter referred to as orthogonal complementary space conversion) will be described in detail.
In the present embodiment, for example, orthogonal complementary space transformation can be performed using QR decomposition (decomposition into the product of the orthogonal matrix Q and the upper triangular matrix R) shown in the following equation (15).

From the above, by projecting the LQ optimization problem with equality constraints onto the orthogonal complement space, the orthogonal complement space transformation is performed and the unconstrained LQ optimization problem is derived as follows.
First, the following equation (16), which is a conversion equation for the state variable x, is derived by performing QR decomposition on the above equation (13) (C _k x [k] = d _k ) indicating the equality constraint condition.

Next, when the state equation (8) is multiplied by C _{k + 1} from the left, the following equation (17) is derived.
C _{k + 1} x [k + 1] = C _{k + 1} Ax [k] + C _{k + 1} Bu [k] (17)
Further, the following equation (18) is derived by substituting the above equation (13) into the above equation (17).
C _{k + 1} Ax [k] + C _{k + 1} Bu [k] = d _{k + 1} (18)
(K = 0, 1, ..., N-1)

Similar to the above transformation, variable transformation is performed using an orthogonal complementary space of C _{k + 1} B.
QR decomposition is performed on C _{k + 1} B as shown in the following equation (19).

但し、上記（２０）式における各パラメータを下記（２１）式に示すように設定する。

ｋ＝０のときは、上記（２０）式における各パラメータを下記（２２）式に示すように設定する。

The above equation (18) is converted using the above equation (19) (QR decomposition is performed), and the following equation (20), which is a conversion equation for the input u, is derived.

However, each parameter in the above equation (20) is set as shown in the following equation (21).

When k = 0, each parameter in the above equation (20) is set as shown in the following equation (22).

但し、上記（２３）式において、正規直交性から下記（２４）式が成立する。

The following equation (23) is derived by multiplying D ^T _k from the left of the above equation (16).

However, in the above equation (23), the following equation (24) is established from orthonormality.

但し、上記（２５）式における各パラメータを下記（２６）式に示すように設定する。

ｋ＝０のときは、上記（２５）式における各パラメータを下記（２７）式に示すように設定する。

From the above, the above equation (8) is transformed using the above equations (16), (20), and (23), and the following equation (25), which is a conversion equation of the state equation, is derived.

However, each parameter in the above equation (25) is set as shown in the following equation (26).

When k = 0, each parameter in the above equation (25) is set as shown in the following equation (27).

但し、ｋ＝０のときは、下記（２９）式が成立する。

また、ｋ＝Ｎのときは、下記（３０）式が成立する。

In addition, using the above equations (16) and (20), the Σ term of the evaluation function J shown in the above equation (12) can be modified as shown in the following equation (28).

However, when k = 0, the following equation (29) is established.

When k = N, the following equation (30) is established.

但し、上記（３１）式における各パラメータを下記（３２）式に示すように設定する。

The evaluation function J shown in the above equation (12) is transformed using the above equations (16) and (20), and the following equation (31), which is a conversion equation of the evaluation function, is derived.

However, each parameter in the above equation (31) is set as shown in the following equation (32).

As described above, the orthogonal complementary space transformation is performed on the LQ optimization problem with equality constraints, and the unconstrained LQ optimization problem expressed by the following equation (33) can be derived. That is, by performing orthogonal complementary space transformation, the LQ optimization problem with equality constraints shown in the above equation (14) can be converted into an unconstrained LQ optimization problem shown in the following equation (33). The trajectory generation unit 222 according to the present embodiment can find an optimal solution for the unconstrained LQ optimization problem expressed by the following equation (33) at high speed using the Riccati-type recursive calculation method.

Next, a method for solving the unconstrained LQ optimization problem converted by the orthogonal complementary space transform using the Riccati-type recursive calculation method will be described.
First, when the above equation (33) is expressed as a matrix, it can be expressed as the following equations (34) and (35).

The optimum solution condition (KKT condition) of the above equations (34) and (35) is the following equation (36). However, the following equation (37) is a Lagrange multiplier of the above equation (35).

The trajectory generation unit 222 solves the unconstrained LQ optimization problem at high speed and stably by performing recursive calculation on the expression shown in the above expression (36) as follows.
First, the trajectory generation unit 222 represents the following equation (38) by changing the order of the parameters in the matrix of the equation (36).

Then, the trajectory generation unit 222 repeats the recursive calculation shown in the following equation (39) with respect to the above equation (38).

By repeating the recursive calculation, the equation (38) is transformed into the following equation (40).

Furthermore, the trajectory generation unit 222 performs a recursive calculation shown in the following equation (41) on the above equation (40), thereby obtaining the optimum solution ζ of the LQ optimization problem shown in the above equation (33) at high speed. To do.

Finally, the trajectory generation unit 222 uses the calculated optimal solution ζ and the above-described equations (16) and (20) (the following two equations) with the equation constraint condition shown in the above-mentioned equation (14). The parameters of the LQ optimization problem are restored, and x [k] and u [k] are calculated.
x [k] = D _k ζ [k] + e _k
u [k] = N _k ζ [k] + M _k v [k] + l _k

  The trajectory generation unit 222 calculates the calculated x [k] (the x coordinate of the center of gravity G, the x-axis direction speed of the center of gravity G, the y-coordinate of the center of gravity G, the y-axis direction speed of the center of gravity G, and the contact force at each contact point ( A center-of-gravity trajectory is generated based on time-series data of 6-axis force))). In this way, the center-of-gravity trajectory that satisfies the equality constraint condition and minimizes the evaluation function J is generated at high speed within the prediction interval. That is, it is possible to generate a center-of-gravity trajectory that realizes stable movement of the mobile robot within the prediction interval at high speed.

FIG. 14 is a diagram showing a flow when converting to the unconstrained LQ optimization problem shown in the above equation (33), which is obtained by performing the above-described orthogonal complementary space conversion.
QR decomposition is performed on the above equation (13) of the equation constraint, and the above equation (16), which is a state variable conversion equation, is derived (step S101).

  QR decomposition is performed on the equation (18) derived from the equation of state (8) indicating the relationship between the input (differential value of the contact force (6-axis force)) u and the state variable x of the center of gravity, and the input u The above equation (20) which is a conversion equation is derived (step S102).

  The state equation (8) is derived from the derived equation (16) for the state variable x, equation (20) for the input u, and equation (16) for the state variable x (23). The equation (25), which is a transformation equation of the state equation, is derived using the equation (step S103).

  Based on the derived conversion equation (16) of the state variable x and the conversion equation (20) of the input u, the evaluation function shown in the above equation (12) is modified to convert the evaluation function ( 31) is derived (step S104). As described above, the unconstrained LQ optimization problem after conversion includes the equation (31) for the derived evaluation function and the equation (25) for the state equation.

FIG. 15 is a flowchart showing an optimal control method by the optimal control apparatus according to the present embodiment.
The contact point plan setting unit 221 sets a contact point plan (equals constraint conditions C _k and d _k ) (step S201).

  Based on the contact point plan set by the contact point plan setting unit 221, the trajectory generation unit 222 represents the LQ optimization problem of the above equation (33) as a matrix and performs recursive calculation on the optimal solution condition. Thus, the optimum solution ζ of the LQ optimization problem is obtained (step S202).

The trajectory generation unit 222 restores the parameters of the LQ optimization problem with the equation constraint shown in the above equation (14) using the obtained optimal solution ζ and the above equations (16) and (20). The state variable x [k] and the input u [k] of the center of gravity are calculated (step S203).
The trajectory generation unit 222 generates a gravity center trajectory based on the calculated time series data of x [k] (step S204).

  The motion control unit 223 controls the motors 1a to 24a of the respective joints so that the mobile robot 100 operates in the whole body according to the gravity center trajectory generated by the trajectory generation unit 222 (step S205).

  As described above, in the present embodiment, the trajectory generation unit 222 solves the unconstrained optimization problem obtained by converting the optimization problem with equality constraints using the orthogonal complement space, and finds the optimal solution using the recursive calculation method. Then, a state variable of the center of gravity is calculated based on the obtained optimum solution, and a center of gravity trajectory is generated based on the calculated state variable of the center of gravity. As a result, the optimal solution of the optimization problem can be obtained at high speed in model predictive control, and the center of gravity trajectory can be generated.

(Second Embodiment)
In this embodiment, the trajectory generation unit 222 finds an LQ optimization problem in which an inequality constraint is added to the above equation constraint. Here, the inequality constraint condition indicating the constraint on the stability of the contact point will be described.

Introducing inequality constraints to keep the contact points of mobile robots stable.
FIG. 16 shows the coordinate system of the contact point (with the superscript l (el)) and the contact polygon (support polygon of the contact point).

Coordinate system of the touch point, the contact point as the origin, and a are defined in accordance with the orientation r _i of the contact surface.
Here, the contact force (six-axis force) θ _i ^l defined in the coordinate system of the contact point is expressed as follows.
^{_{θ i l = [f ix l}} , f iy l, f iz l, τ ix l, τ iy l, τ iz l] T

Then, the contact force (six-axis force) θ _i ^l can be expressed by the following equation (42) using the contact surface posture matrix Φ _i = rot (r _i ).
Note that rot is a function that converts Euler angles into a posture matrix.

In order for the contact point to maintain stable contact,
(1) The contact point must not be separated,
(2) The contact point does not slip,
(3) The contact point does not peel off,
It is necessary to satisfy the following three constraint conditions.
In order to facilitate understanding of the above three constraints, FIG. 17 illustrates a case where the contact point becomes unstable.

(1) In order not to leave the contact point, the vertical force on the contact surface may be positive. That is, it is necessary to satisfy the following formula (43).

(2) To prevent the contact point from slipping, the biaxial force parallel to the contact surface may be equal to or less than the friction force. That is, the following equation (44) is the condition. However, the friction coefficient of the contact surface is μ _i .

(3) The condition for the contact point not to be peeled off is that the vertex coordinates (x _i1 ^l , y _i1 ^l ) of the contact polygon (x _ih ^l , y _ih ^l )
Is expressed as the following equation (45).
(However, the vertices of the contact polygon are given in order counterclockwise).

The expressions (43), (44), and (45) are summarized as follows.

Substituting equation (42) into equation (46), an index is added as the kth sampling point. That is, the following expression (47) is a conditional expression for maintaining a stable contact at the k-th contact point. Therefore, in order to realize a stable multipoint contact operation, the following equation (47) needs to be satisfied at all contact points of all sampling points.

The condition for all the contact points to satisfy the equation (47) at the k-th sampling point can be expressed as the following equation (48).

(Inequality constraints)
(P _k x [x] ≦ q _k ) (49)
If the right side q _k of the general formula (49) of the inequality constraint condition is set as q _k = O, it matches the derived formula (48). By adding the above (48) to the LQ optimization problem with equality constraints shown in the above equation (14), the LQ optimization problem with equality constraints and inequality constraints shown in the following equation (50) is derived. .

In the present embodiment, in addition to the orthogonal complementary space transformation performed in the first embodiment, the inequality constraint condition shown in the above equation (49) is further transformed. Specifically, the following equation (51) is derived by substituting the above equation (16) into the above equation (49).

From the above, in the present embodiment, by performing orthogonal complementary space transformation, the LQ optimization problem with the equality constraint condition and the inequality constraint condition shown in the above equation (50) can be solved by the unconstraint condition shown in the following equation (52). It can be converted into an LQ optimization problem. The trajectory generation unit 222 according to the present embodiment finds an optimal solution for the unconstrained LQ optimization problem expressed by the following equation (52) using the Riccati-type recursive calculation method.

Next, a method for solving the unconstrained LQ optimization problem converted by the orthogonal complementary space transform using the Riccati-type recursive calculation method will be described.
First, as in the first embodiment, when the above equation (52) is expressed in a matrix, it can be expressed as the following equations (53) to (55).

The Lagrange multipliers of the above equations (54) and (55) shown in the following equation (56) are introduced.

Subsequently, as shown in the following formula (57), slack variables are introduced into the formula (55).

By introducing the above formulas (56) and (57), the optimal solution condition (KKT) for the LQ optimization problem shown in the above formulas (53) to (55) can be expressed by the following formula (58).

  The above formulas (53) to (55) are LQ optimization problems called convex quadratic programming problems. An efficient solution can be obtained by using a known solution method (convergence calculation) such as an interior point method or an active set method. These solution methods are solution methods based on the Newton method, in which Newton's direction is calculated during the convergence operation of the Newton method, and simultaneous linear equations are solved by the Riccati-type recursive calculation method. Further, since the calculation of the simultaneous one equation accounts for most of the calculation amount of the convex quadratic programming problem, it is very effective to speed up the calculation.

  As described above, by performing orthogonal complementary space transformation, the LQ optimization problem with equality and inequality constraints shown in the above equation (50) is changed into the unconstrained LQ optimization problem shown in the above equation (52). Convert to As a result, a stable and high-speed Riccati-type recursive calculation method can be used to solve the simultaneous linear equations of the LQ optimization problem. Therefore, the optimal solution of the LQ optimization problem can be obtained at high speed.

  In the present embodiment, the trajectory generation unit 222 performs the solution of the simultaneous linear equations by the Riccati-type recursive calculation method in, for example, a convergence operation such as an interior point method or an active set method. Hereinafter, in the present embodiment, a solution method using the interior point method will be described, but the present invention is not limited to this. The solution method using the active set method can also be solved by the same method as the interior point method.

  The interior point method is a method for finding an optimal solution by efficiently solving the above equation (58) by Newton's method and line search. In the present embodiment, the case of using the most standard principal dual interior point method among interior point methods will be described, but the present invention is not limited to this.

First, when the Newton method is applied to the above equation (58), the following equations (59) and (60) are derived. However, in the following equation (60), (× in ○) means a product of elements, and Λ≈diag (λ) and Z≈diag (z).

Then it introduced Complementary its measure mu and step size alpha _p as shown in the following (61) and (62) below.

Here, it is easy to understand that complementary measure μ is the total of the residuals of the convergence calculation, and step width α _p is the maximum step width in the Newton direction within a range satisfying λ ≧ 0 and z ≧ 0.

The algorithm for the principal dual interior point method is simply described as follows.

  The trajectory generation unit 222 performs a convergence operation using the main dual interior point method to find an optimal solution ζ. Similar to the first embodiment, the trajectory generating unit 222 uses the calculated optimal solution ζ and the above equations (16) and (20), and the equation constraints and The parameters of the inequality conditional LQ optimization problem are restored, and the optimal solutions x [k] and u [k] are obtained.

Here, the solution of the equation (59) shown in the algorithm of the main dual interior point method will be described in detail. First, the following equation (63) is established.

Using the above equation (63), the above equation (59) can be expressed as the following equation (64).

さらに、上記行列（６５）式の各係数を並べ替えると、下記（６６）式のように表現できる。

Here, when Θ≈Z ⁻¹ Λ and r _z ′ ≈r _z −Z ⁻¹ r _λ , the above expression (64) can be expressed as the following expression (65).

Further, by rearranging the coefficients of the matrix (65), the coefficients can be expressed as the following (66).

Here, the following equation (67) is established.

但し、上記（６８）式において、パラメータを下記（６９）式のように設定している。

Using the above expression (67), the above expression (66) can be expressed as the following expression (68).

However, in the above equation (68), the parameters are set as in the following equation (69).

  The above equation (68) has the same form as the equation (38) shown in the Riccati-type recursive calculation method of the first embodiment described above. Therefore, the trajectory generating unit 222 can solve the simultaneous linear equations shown in the above equation (68) at high speed and stably using the Riccati-type recursive calculation method as in the first embodiment.

That is, the trajectory generation unit 222 repeats the recursive calculation shown in the following equation (70) with respect to the above equation (68).

軌道生成部２２２は、算出したΔζ_ｋを上記（６７）式に代入することで、Δｚ_ｋを算出する。軌道生成部２２２は、算出したΔｚ_ｋ＝[Δｚ_１ ^Ｔ、Δｚ_２ ^Ｔ、・・・Δｚ_Ｎ ^Ｔ]^Ｔを上記（６３）式に代入することで、Δλを算出する。以上により、上記（５９）式の求解が完了する。 Further, the trajectory generation unit 222 calculates (Δv _k , Δζ _k , Δy _k ) by performing a recursive calculation shown in the following equation (71).

The trajectory generation unit 222 calculates Δz _k by substituting the calculated Δζ _k into the above equation (67). The trajectory generation unit 222 calculates Δλ by substituting the calculated Δz _k = [Δz ₁ ^T , Δz ₂ ^T ,... Δz _N ^T ] ^T into the equation (63). Thus, the solution of the above equation (59) is completed.

FIG. 18 is a flowchart showing a solution flow of an optimal solution of the LQ optimization problem with equality constraints and inequality constraints described above.
First, the trajectory generation unit 222 performs an initial solution (η = η ₀ , y = y ₀ , z = z ₀ , λ = λ ₀ ) of the solution vector (step S301).

The trajectory generation unit 222 sets the repetition parameter n = 0 (step S302).
The trajectory generation unit 222 calculates residuals [r _η , r _y , r _z , r _λ ] using the above equation (60) (step S303).

The trajectory generation unit 222 calculates the Newton directions [Δη, Δy, Δz, Δλ] by the Riccati-type recursive calculation method (step S304).
The trajectory generation unit 222 calculates the step width α _p using the above equation (62) (step S305).

Trajectory generation unit 222, the calculated Newtonian direction [Δη, Δy, Δz, Δλ ] and based on the step size alpha _p, and updates the solution vector using the following equation (step S306).
[Η, y, z, λ] = [η, y, z, λ] + βα _p [Δη, Δy, Δz, Δλ]

The trajectory generation unit 222 calculates complementary measure μ using the above equation (61) (step S307).
The trajectory generation unit 222 determines whether or not the condition (μ> μ _min and n <n _max ) is satisfied (step S308).

When the trajectory generation unit 222 determines that the conditions (μ> μ _min and n <n _max ) are satisfied (YES in step S308), n = n + 1 is set, and the process returns to the above (step S303).

When the trajectory generation unit 222 determines that the conditions (μ> μ _min and n <n _max ) are not satisfied (NO in step S308), the trajectory generation unit 222 performs the above (53) to (55) based on η when converged The optimal solution ζ is calculated from the equation. Then, the trajectory generation unit 222 uses this optimal solution ζ and the above equations (16) and (20) to solve the LQ optimization problem with the equality and inequality constraints shown in the above equation (52). Are restored, and x [k] and u [k] are calculated (step S309).

(Third embodiment)
The trajectory generation unit 222 according to the first embodiment solves an optimization problem with linear invariant equality constraints, but the trajectory generation unit 222 according to the third embodiment has linear time-varying equality constraints. Solve a problem with optimization.

For example, considering the case where the sampling interval is such as to change, Delta] t is not fixed, so that the changes for each sampling point as Delta] t _k. In this case, the above equation (8) can be expressed as a linear time-varying control system as the following equation (72).

Here, the state equation of the linear time-varying optimization problem holds, for example, the relationship shown in the following equation (73). That is, the state equation shown in the equation (73) includes linear time-varying control parameters A _k and B _k .

但し、上記（７４）式において、下記（７５）式が成立するものとする。

From the above equations (73), (12), and (13), the trajectory generator 222 solves the LQ optimization problem with equality constraints shown in the following equation (74) and generates the center of gravity trajectory. It becomes.

However, in the above equation (74), the following equation (75) is assumed to hold.

From the above, as in the first embodiment, by projecting the LQ optimization problem with equality constraints onto the orthogonal complement space, the orthogonal complement space transformation is performed to derive the unconstrained LQ optimization problem.
First, from the above equation (13) (equal constraint: C _k x [k] = d _k ), the following equation (76), which is a conversion equation for the state variable x, is derived in the same manner as in the first embodiment.

Variable transformation is performed using the orthogonal complement space of C _{k + 1} B. QR decomposition is performed on C _{k + 1} B as shown in the following equation (77).

但し、上記（７８）式における各パラメータを下記（７９）式に示すように設定する。

ｋ＝０のときは、上記（７８）式における各パラメータを下記（８０）式に示すように設定する。

The following equation (78), which is a conversion equation for the input u, is derived using the above equation (77) in the same manner as the above equation (20).

However, each parameter in the above equation (78) is set as shown in the following equation (79).

When k = 0, each parameter in the above equation (78) is set as shown in the following equation (80).

但し、上記（８１）式における各パラメータを下記（８２）式に示すように設定する。

ｋ＝０のときは、上記（８１）式における各パラメータを下記（８３）式に示すように設定する。

From the above, the above equation (8) is transformed using the above equations (73), (76), and (78), and the following equation (81), which is a conversion equation of the state equation, is derived.

However, each parameter in the above equation (81) is set as shown in the following equation (82).

When k = 0, each parameter in the above equation (81) is set as shown in the following equation (83).

但し、上記（８４）式における各パラメータを下記（８５）式に示すように設定する。

As in the first embodiment, the evaluation function J shown in the above equation (12) is modified to derive the following equation (84) that is a conversion equation of the evaluation function.

However, each parameter in the above equation (84) is set as shown in the following equation (85).

As described above, the trajectory generation unit 222 according to the present embodiment, as in the first embodiment, converts the unconstrained LQ optimization problem expressed by the following equation (86) converted using the orthogonal complement space into the Riccati-type recursion. The optimal solution ζ can be obtained at high speed using a genetic calculation method.

  Finally, the trajectory generation unit 222 uses the optimum solution ζ obtained above and the above equations (16) and (20) to solve the LQ optimization problem with the equation constraint shown in the above equation (74). The parameters are restored and x [k] and u [k] are calculated.

(Fourth embodiment)
The trajectory generation unit 222 according to the fourth embodiment solves an optimization problem with equality constraints including input, which is set so that the contact force does not change only within a predetermined interval, which is linear and time-varying.

For example, assume that there is a section in the future sampling section where the mobile robot does not want to change the contact force, such as pushing and moving an object with a constant force. More specifically, when the contact force is not changed only within a certain section between the second contact point and the second contact point from the end, the equality constraint condition in the section is expressed by the following equation (87). Will include the input E _k (u).

Note that C _k can be set as in the following equation (88) in a section other than the section where the contact force is not desired to be varied.

但し、下記（９０）式が成立する。

From the above formula, the trajectory generation unit 222 solves the LQ optimization problem with an equality constraint including the input shown in the following formula (89), and generates a barycentric trajectory.

However, the following equation (90) is established.

First, when the transposed matrix of the coefficient E _k with respect to the input u within the equality constraint condition is subjected to QR decomposition, the following equation (91) is derived.

Using the above equation (91), the equation (87) of the equation constraint including the above input can be converted as shown in the following equation (92).

以降の式変換の方法は、上記実施形態３と同一であるため、省略して説明する。以上から、線形時変な、入力を含む等式制約条件付き最適化問題を直交補空間変換を行い、下記（９４）に示す無制約条件のＬＱ最適化問題を導出する。

By substituting the equation (92) into the equation (8), the following equation (93) is derived.

Since the subsequent formula conversion method is the same as that of the third embodiment, the description will be omitted. From the above, the linear time-varying optimization problem with equality constraints including input is subjected to orthogonal complementary space transformation to derive the unconstrained LQ optimization problem shown in (94) below.

  The trajectory generation unit 222 can solve the unconstrained LQ optimization problem shown in the derived equation (94) at high speed using the Riccati-type recursive calculation method. Finally, the trajectory generation unit 222 uses the optimum solution ζ obtained above and the above equations (16) and (20) to perform LQ optimum with equality constraints including the input shown in the above equation (89). The parameters of the conversion problem are restored, and x [k] and u [k] are calculated. As described above, the trajectory generation unit 222 may solve an optimization problem with an equation constraint including an input that is linear invariant.

(Fifth embodiment)
The trajectory generation unit 222 according to the fourth embodiment is linear time-varying, an equality constraint condition including an input set so that the contact force does not change only within a predetermined section, and the contact force is limited only within the predetermined section. Solve an inequality-constrained optimization problem that includes inputs set to apply.

For example, a case is assumed where it is desired to limit the change in contact force so as to prevent a sudden change in contact force of the hand or foot of the mobile robot. For example, if you want to limit the amount of increase in contact force at the second contact point and the amount of decrease in contact force at the penultimate contact point only within a certain section, the inequality constraint condition in the section you want to limit is As shown in the following equation (95), the input Γ _k u [k] is included.

However, in the above equation (95), Δf _lm is a vector in which limit values of the six-axis forces at each contact point are arranged vertically. In addition, P _k can be set as in the following equation (96) in a section other than the section where the restriction is desired.

但し、下記（９８）式が成立する。

From the above equation, the trajectory generation unit 222 solves the LQ optimization problem with equality constraints including the input shown in the following equation (97), and generates a centroid trajectory.

However, the following equation (98) is established.

但し、上記（９９）式のパラメータを下記（１００）式のように設定する。

By substituting the above equation (92) into the above equation (95) indicating the inequality constraint condition, the inequality constraint condition is converted into the following equation (99).

However, the parameter of the above equation (99) is set as the following equation (100).

以降に示す、上記無制約条件のＬＱ最適化問題に対するリカッチ型再帰的計算法による最適解ζの求解方法は、上記実施形態２において説明した求解方法と略同一であるため、相違点のみを説明する。 From the above equation (33) and the above equation (99), the trajectory generator 222 solves the unconstrained LQ optimization problem shown in the following equation (101) using the Riccati-type recursive calculation method. Will be.

Since the solution method of the optimal solution ζ by the Riccati-type recursive calculation method for the unconstrained LQ optimization problem described below is substantially the same as the solution method described in the second embodiment, only the differences will be described. To do.

The coefficient matrix P in the equations (53) to (55) is replaced with the following equation (102).

Therefore, the above equation (66) is replaced with the following equation (103).

Here, the following equation (104) is established.

Using the above equation (104), the above equation (103) can be transformed into the following equation (105).

実施形態２の上記（６８）式と上記（１０５）式との相違は、Ｓ_ｋ（ハット）がＳ′_ｋ（ハット）となっているだけで、その他のパラメータは同一である。したがって、軌道生成部２２２は、以降の計算について、上記実施形態２と同一の計算を行い、最適解ζを高速に求解できる。最後に、軌道生成部２２２は、上記求解した最適解ζと、上記（１６）式及び（２０）式と、を用いて、入力を含む等式制約条件及び不等式制約条件付きＬＱ最適化問題のパラメータを復元し、ｘ［ｋ］及びｕ［ｋ］を算出する。なお、軌道生成部２２２は、上記同様に、線形不変な、入力を含む等式制約条件及び不等式制約条件付き最適化問題を求解してもよい。また、軌道生成部２２２は、線形不変あるいは線形時変な、等式制約条件及び入力を含む不等式制約条件付き最適化問題を求解してもよい。 However, each parameter of the above equation (105) is set as the following equation (106).

The difference between the expression (68) and the expression (105) in the second embodiment is that S _k (hat) is S ′ _k (hat), and other parameters are the same. Therefore, the trajectory generation unit 222 performs the same calculation as that in the second embodiment for the subsequent calculations, and can obtain the optimum solution ζ at high speed. Finally, the trajectory generation unit 222 uses the optimum solution ζ obtained above and the above equations (16) and (20) to solve the LQ optimization problem with equality constraints and inequality constraints including input. The parameters are restored and x [k] and u [k] are calculated. As described above, the trajectory generation unit 222 may solve linearly invariant equality constraints including inputs and optimization problems with inequality constraints. Further, the trajectory generation unit 222 may solve an optimization problem with an inequality constraint condition including an equation constraint condition and an input that is linear invariant or linear time varying.

Note that the present invention is not limited to the above-described embodiment, and can be changed as appropriate without departing from the spirit of the present invention.
For example, the present invention can be realized by causing the CPU 210a to execute the processing shown in FIG. 15 and FIG. 18.

  The program may be stored using various types of non-transitory computer readable media and supplied to a computer. Non-transitory computer readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (for example, flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (for example, magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / W and semiconductor memory (for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (random access memory)) are included.

  The program may also be supplied to the computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire and an optical fiber, or a wireless communication path.

  DESCRIPTION OF SYMBOLS 1a-28a ... Motor, 1b-28b ... Encoder, 25 ... Contact force sensor, 100 ... Mobile robot, 210 ... Optimal control apparatus, 221 ... Contact point plan setting part, 222 ... Trajectory generation part, 223 ... Operation control part.

Claims (13)

A contact point plan in which the position of the contact point where the moving means touches the moving robot that moves while grounding two or more moving means alternately and the attitude of the moving means when touching the ground is time-series data. Contact point planning means to be set;
Based on the contact point plan set by the contact point plan setting means, a trajectory generating means for generating a center of gravity trajectory for the mobile robot to move while the moving means contacts the contact point;
An optimal control device comprising:
The trajectory generating means constructs a prediction model that receives an amount based on a contact force when the moving means is grounded, and uses the prediction model to calculate a state variable of the center of gravity of the mobile robot in a prediction section having a predetermined time width. Representing the state variable of the center of gravity using a predetermined evaluation criterion in the prediction section, and performing model prediction control for generating the center of gravity trajectory of the mobile robot based on the calculated state variable of the center of gravity.
The evaluation criterion is to minimize an evaluation function including a square of an amount based on the contact force at each contact point within a prediction interval,
An equation constraint including the evaluation criteria, a linear state equation indicating a relationship between the input based on the contact force and the state variable of the center of gravity, and an equation constraint expressed by a linear equation of the mobile robot With the orthogonal complement space is converted into an unconstrained optimization problem that does not include the equality constraints,
The trajectory generating means solves the converted unconstrained optimization problem in the prediction interval by using a recursive calculation method to find an optimal solution, and determines the state variable of the center of gravity based on the calculated optimal solution. calculate,
An optimal control device characterized by that. The optimal control device according to claim 1,
The trajectory generating means constructs a prediction model that receives a differential value of contact force when the moving means is grounded,
The evaluation criterion includes a criterion of allocating the contact force and a differential value of the contact force to each contact point based on a weight set corresponding to each contact point, and the contact force and An evaluation function including the sum of squares of the derivative of the contact force is minimized within the prediction interval,
An equation constraint including the evaluation criteria, a state equation indicating the relationship between the input of the differential value of the contact force and the state variable of the center of gravity, and an equation constraint indicating a constraint of the balance of force of the mobile robot The constrained optimization problem is converted into an unconstrained optimization problem that does not include the equality constraint condition using orthogonal complement space.
An optimal control device characterized by that. The optimal control device according to claim 1 or 2,
A QR variable decomposition is performed on the equation representing the equation constraint to derive a state variable conversion equation, and the equation derived from the state equation indicating the relationship between the input of the differential value of the contact force and the state variable of the center of gravity is obtained. QR conversion is performed on the input, and an input conversion equation is derived, and the state equation is converted based on the state equation, the state variable conversion equation, the input conversion equation, and the state variable conversion equation. An expression is derived, and an evaluation function conversion expression is derived based on the derived state variable conversion expression, the input conversion expression, and the evaluation function of the optimization problem with equality constraints,
The unconstrained optimization problem includes a conversion equation of the derived evaluation function and a conversion equation of the state equation,
Optimal control device characterized by that. The optimal control device according to any one of claims 1 to 3,
The trajectory generating means includes
For the optimal solution condition of the expression expressing the unconstrained optimization problem in a matrix form, the optimal solution is solved using a recursive calculation method, and the calculated optimal solution and the equation indicating the equality constraint condition are Calculating time series data of the state variable of the center of gravity based on a state variable conversion formula derived by QR decomposition;
An optimal control device characterized by that. The optimal control device according to any one of claims 1 to 4,
The equation constraint includes an input set so that the contact force does not change only within a predetermined interval.
An optimal control device characterized by that. The optimal control device according to any one of claims 1 to 5,
The trajectory generating means includes
An unconstrained optimization problem obtained by transforming an equality constraint condition including the equality constraint condition and an inequality constraint condition indicating the stability constraint of the contact point and an optimization problem with the inequality constraint condition using an orthogonal complement space A recursive calculation method to find an optimal solution, and calculate time series data of the state variable of the center of gravity based on the obtained optimal solution,
An optimal control device characterized by that. The optimal control device according to claim 6,
The trajectory generating means includes
Applying Newton's method to the optimal solution condition of the expression expressing the unconstrained optimization problem as a matrix, calculating the Newton direction using the recursive calculation method in the convergence operation of the Newton method, Calculate the optimal solution based on the calculated Newton direction.
An optimal control device characterized by that. The optimal control device according to claim 7,
The trajectory generating means applies an interior point method or an active set method to an optimal solution condition of an expression that represents the optimization problem of the unconstrained condition in a matrix form. The optimal control device according to any one of claims 6 to 8,
The inequality constraint condition includes an input set to limit the contact force only within a predetermined interval.
An optimal control device characterized by that. The optimal control device according to any one of claims 1 to 9,
The optimization control apparatus, wherein the state equation of the optimization problem includes a linear time-varying control parameter. The optimal control device according to any one of claims 1 to 10,
An optimum control apparatus, further comprising: a control unit that controls the moving unit based on the center-of-gravity trajectory generated by the trajectory generating unit. A contact point plan in which the position of the contact point where the moving means touches the moving robot that moves while grounding two or more moving means alternately and the attitude of the moving means when touching the ground is time-series data. Steps to set,
Based on the set contact point plan, generating a center of gravity trajectory for the mobile robot to move while the moving means contacts the contact point;
An optimal control method including
By constructing a prediction model that receives an amount based on contact force when the moving means is grounded, the prediction model represents a state variable of the center of gravity of the mobile robot in a prediction interval of a predetermined time width, and in the prediction interval Calculating a state variable of the center of gravity using a predetermined evaluation criterion, and performing model predictive control for generating a center of gravity trajectory of the mobile robot based on the calculated state variable of the center of gravity,
The evaluation criterion is to minimize an evaluation function including a square of an amount based on the contact force at each contact point within a prediction interval,
An equation constraint including the evaluation criteria, a linear state equation indicating a relationship between the input based on the contact force and the state variable of the center of gravity, and an equation constraint expressed by a linear equation of the mobile robot With the orthogonal complement space is converted into an unconstrained optimization problem that does not include the equality constraints,
In the prediction interval, the converted unconstrained optimization problem is solved by using a recursive calculation method to find an optimal solution, and the state variable of the center of gravity is calculated based on the obtained optimal solution.
An optimal control method characterized by that. A contact point plan in which the position of the contact point where the moving means touches the moving robot that moves while grounding two or more moving means alternately and the attitude of the moving means when touching the ground is time-series data. Process to set,
Based on the set contact point plan, a process of generating a center-of-gravity trajectory for the mobile robot to move while the moving means contacts the contact point;
An optimal control program for causing a computer to execute
By constructing a prediction model that receives an amount based on contact force when the moving means is grounded, the prediction model represents a state variable of the center of gravity of the mobile robot in a prediction interval of a predetermined time width, and in the prediction interval Calculating a state variable of the center of gravity using a predetermined evaluation criterion, and performing model predictive control for generating a center of gravity trajectory of the mobile robot based on the calculated state variable of the center of gravity,
The evaluation criterion is to minimize an evaluation function including a square of an amount based on the contact force at each contact point within a prediction interval,
An equation constraint including the evaluation criteria, a linear state equation indicating a relationship between the input based on the contact force and the state variable of the center of gravity, and an equation constraint expressed by a linear equation of the mobile robot With the orthogonal complement space is converted into an unconstrained optimization problem that does not include the equality constraints,
In the prediction interval, the converted unconstrained optimization problem is solved by using a recursive calculation method to find an optimal solution, and the state variable of the center of gravity is calculated based on the obtained optimal solution.
An optimal control program characterized by that.

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