CN109491389B - Robot trajectory tracking method with speed constraint - Google Patents
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Abstract
The invention discloses a robot track tracking method with speed constraint, which comprises the steps of (1) setting a human motion area, a starting point and an obstacle; (2) planning a path, firstly obtaining the path by using an improved rapid exploration random tree algorithm, and then optimizing and curve fitting the path to obtain a smooth path; (3) establishing speed constraint according to the parameters of the robot; (4) establishing a system prediction model, and predicting the optimal input speed at the next moment according to the path nodes; (5) repeatedly executing the step (4) to obtain the optimal input speed; (6) and controlling the robot to move according to the optimal speed input. The method can realize that the robot moves quickly and stably along the path, can very accurately fit the planned path, and has small error and strong robustness.
Description
Technical Field
The invention relates to a robot track tracking method, in particular to a track tracking method for controlling the speed of an omnidirectional mobile robot.
Background
Mobile robots are now widely studied in various fields, and have been applied not only to industries, aerospace, utilities, etc., but also to the fields of service and living. Compared with a traditional non-completely constrained double-drive wheeled robot, the wheel of an omni-directional mobile robot (OMR) with complete constraint can rotate simultaneously and separately to realize translation in any direction and complex arc motion, so that the wheel has attracted great attention of researchers. The omnidirectional mobile robot has the advantages of flexibility, simple modeling, good maneuverability and the like, and mainly focuses on the aspects of point-to-point stability, path planning, trajectory tracking, speed control and the like in the research aspect.
Model Predictive Control (MPC) algorithms were first a computer control algorithm in the field of industrial process control and are now widely discussed as a feedback control strategy. The MPC algorithm mainly aims at multivariable and constrained objects, and the principle is as follows: at each sampling moment, a finite time domain open loop optimization problem is solved on line according to the measurement information of the system, the current moment and the subsequent control sequence can be obtained, but only the first control variable is taken to control the current moment, at the next moment, the process is repeated, and a new optimization problem is processed by using new measurement information. The MPC algorithm is widely applied by the characteristics that the MPC algorithm only pays attention to the function of the model and does not pay attention to the form of the model, the uncertainty is reduced by applying rolling optimization, the MPC algorithm has feedback correction, the algorithm is easy to expand and the like. At present, the MPC algorithm is also effectively applied to robot motion control. However, in tracking the trajectory of the robot by using the model predictive control algorithm, it is important to control the speed of the robot. If the speed is not restricted, the robot will be stationary at each point of tracking and then obtain the speed at the next moment, so that its speed will change all the time, and after the speed control, the robot can achieve the stability of the speed and meet the actual requirement.
Disclosure of Invention
The invention discloses an omnidirectional mobile robot track tracking method with speed constraint, aiming at solving the problem of track tracking of an omnidirectional mobile robot in the background technology. The method comprises the steps of firstly obtaining a collision-free feasible path connecting the starting points by a fast exploration random tree algorithm before the robot moves, and inputting path information into a model predictive control algorithm under the assumption that the starting points and the end points of the robot movement and obstacles existing between the starting points are known. And establishing a prediction model according to the actual information of the robot and the speed constraint information to be applied. And predicting the optimal speed of the next moment according to the established prediction model and the path information at the moment, and moving the robot according to the predicted speed. At the next moment, the prediction step is repeated, thereby obtaining the speed of the robot at each moment. Because the velocity constraint is added to the model, the stabilization of the velocity can be achieved.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the following technical scheme: a robot track tracking method with speed constraint comprises the following steps:
(1) setting a human motion area, a starting point and an obstacle;
(2) planning a path, firstly obtaining the path by using an improved rapid exploration random tree algorithm, and then optimizing and curve fitting the path to obtain a smooth path;
(3) establishing speed constraint according to speed limits of three wheels of the robot and the motion range of the robot;
(4) establishing a system prediction model, and predicting the optimal input speed at the next moment according to the path nodes;
(5) repeatedly executing the step (4) to obtain the optimal input speed at each moment;
(6) and controlling the robot to move according to the optimal speed input.
The fast-search random tree (RRT) algorithm is a path planning method for building a tree with a mechanism by sampling in space, and has probability completeness. The tree is generated by randomly taking points in an unobstructed space, and the tree grows to most of undetected areas in principle, so that most of free space can be occupied, and a feasible path from a starting point to an end point is found out finally. The invention uses its improved algorithm-RRT, the improved fast search random tree algorithm in the above step (2) is as follows:
defining a global environment X, wherein an obstacle space xobstalce, an obstacle-free space Xfree ═ X/xobstalce, assuming that the obstacles of the obstacle space are known in advance and stationary; the starting point qinit and the end point qend are positioned in an unobstructed space; the path to be planned is a part of a tree T, the tree is formed by constructing sampling points V and connecting lines E between the sampling points, wherein the sampling points are called leaf nodes, and the connecting lines are called branches;
taking a random point qrand in X, finding a node qnear nearest to qrand in T, and connecting to form a Vector; step is taken from qnear on Vector to form a new node qnew; if the qnew and qnear connecting line is detected to have collision through collision, the growth is abandoned;
establishing a node set near qnew, taking qnew as the center of a circle and taking a preset R as the radius to make a circular space generated by the circle, comparing the accumulated cost of the tree node falling into the circle and the tree after the qnew is connected, selecting the node with the minimum cost as a father node qnear of the qnew, and adding the node into the tree;
assuming the node qnew of the newly added tree as a father node, calculating the cost of the node falling into a circle with the node qnew as the radius; selecting a node qmin with the minimum cost, connecting the node qmin with qnew, and deleting the branch originally connected with qmin; qmin is added into the tree as a child node of qnew;
repeating the steps to obtain a series of leaf nodes; and when the distance between a certain node and the end point qend is less than the step length, connecting the two nodes, and if no collision exists between the two nodes, finding a feasible path from the starting point qinit to the end point qend.
The system prediction model in the step (4) is obtained by the following method:
the kinematic model of the omnidirectional mobile robot is represented as
Wherein
Is the state of the OMR in cartesian coordinates,
is the state of OMR in world coordinates, H is the transformation matrix, θcIs a steering angle;
from the model of the omni wheel, obtain
Where the matrix R is derived from parameters of the omni-wheel, determining the omni-directionality of OMR, [ v [1 v2 v3]TThe speed of the three wheels of the robot;
the inverse kinematics equation of OMR is
Let S be R-1Then, then
Deriving continuous time system equations
Discretizing equation (1)To obtain
Wherein T is the sampling time;
this is converted into a discrete state space model, and X (k +1) ═ ax (k) + bu (k) is obtained, where X (k +1) is the state at the next time, X (k) is the state at that time, u (k) is the amount of change in velocity, and A, B is a coefficient matrix.
The output model is y (k) ═ cx (k) + du (k), where C, D is the coefficient matrix. According to the theory of the rolling optimization, since an input at a certain time cannot affect an output at that time, if matrix D is a 0 matrix, y (k) is cx (k).
So that the prediction model is
The optimal input speed in the step (5) is obtained by the following method:
assume that the control input is Δ u (k)i),Δu(ki+1),...,Δu(ki+Np-1), total of NpThe number of the main components is one,
the state is x (k)i+1|ki),x(ki+2|ki),...,x(ki+Nc|ki),...,x(ki+Np|ki)；
From a state space model
Defining a vector
Y＝[y(ki+1|ki)y(ki+2|ki)y(ki+3|ki)...y(ki+Np|ki)]T
ΔU＝[Δu(ki)Δu(ki+1)Δu(ki+2)...Δu(ki+Nc-1)]T
So the predicted output Y ═ Fx (k)i)+ΦΔU，Wherein
Let us assume a setpoint signal r (k)i) And k isiThe closed loop performance adjusting parameter at the moment is rω(ii) a Order to
And a transformation matrix
So that the cost function
Obtaining an optimal control input by minimizing a cost function, i.e. minimizing the difference between the predicted system output and the desired reference output;
considering the input constraint Deltaumin≤Δu(k)≤ΔumaxAnd output constraint Ymin≤Fx(ki)+ΦΔU≤Ymax；
Due to the existence of constraints, analytical solutions of optimization problems cannot be obtained, and a numerical optimization method is used for solving the problems;
for the MPC algorithm with constraints, the simplified objective function is quadratic, the dynamical equations and the temporal constraints are linear, so it is a Quadratic Programming (QP) problem. The constraint-bearing quadratic programming is in the form of
The quadratic programming problem is solved by calling a function library and then obtaining the optimal control input. We add constraints to the QP algorithm, including upper/lower limit constraints and reference constraints, so that the speed can settle around a value, thereby achieving speed control.
The invention has the following advantages:
(1) by using the RRT algorithm, an unobstructed path connecting the starting point to the end point can be quickly obtained, and the path is close to the optimal path.
(2) Through a model prediction control algorithm, the omnidirectional mobile robot can rapidly move along a path, and the moving track and the path obtained by the RRT algorithm have small errors.
(3) By improving the model and adding speed constraint, the robot can keep stable speed to move, and the robot accords with the actual situation.
Drawings
FIG. 1 is a flow chart of a trajectory tracking method of the present invention.
Fig. 2 is a schematic diagram of the path planning result of the present invention.
Fig. 3 is a velocity curve simulation (without velocity constraints) for simulating robot motion based on the present invention.
Fig. 4 is a velocity curve simulation (with velocity constraints) for simulating robot motion based on the present invention.
Fig. 5 is a motion trajectory simulation of a simulation robot based on the present invention.
Fig. 6 is a simulation of motion errors for a simulated robot based on the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following embodiments, which are illustrative only and not limiting, and the scope of the present invention is not limited thereby.
A robot trajectory tracking method with speed constraint is disclosed, and a flow chart is shown in FIG. 1, and specifically includes the following steps: the method comprises the following steps:
(1) setting a human motion area, a starting point and an obstacle;
(2) planning a path, firstly obtaining the path by using an improved rapid exploration random tree algorithm, and then optimizing and curve fitting the path to obtain a smooth path;
(3) establishing speed constraint according to speed limits of three wheels of the robot and the motion range of the robot;
(4) establishing a system prediction model, and predicting the optimal input speed at the next moment according to the path nodes;
(5) repeatedly executing the step (4) to obtain the optimal input speed at each moment;
(6) and controlling the robot to move according to the optimal speed input.
Obtaining a collision-free feasible path connecting the starting points by an improved rapid exploration random tree algorithm;
a spatial range X (in an embodiment, a movement range is 21m × 21m, but not limited thereto), a start point qinit, and an end point qend (in an embodiment, a start point is (0m,0m), and an end point is (20m ), which are not limited thereto) are set, and the number and size of the obstacles are randomly set, the shape of the obstacles is set to be circular, which is a black area in fig. 2, and a white area is a movable area of the robot. The size and shape of the robot are ignored and considered as particles. The path obtained through the planning of the RRT algorithm is a curve formed by blue nodes in the graph, then optimization is carried out, points which can be connected and have no collision are connected, the length of the path is reduced, and finally a smooth curve which is a red curve in the graph is obtained through curve fitting.
The kinematics model of the omnidirectional mobile robot is
From the model of the omni-wheel, we obtain
The inverse kinematics equation for OMR is therefore
Let S be R-1Then, then
Therefore, we can derive a continuous time system equation
Discretizing the above formula to obtain
Where T is the sampling time (in one embodiment, T is 0.02s, but not limited thereto).
Convert it to a discrete state space model and obtain X (k +1) ═ ax (k) + bu (k), i.e.
Where X (k +1) is the state at the next time, X (k) is the state at that time, and u (k) ═ Δ v1 Δv2 Δv3]TThe variation in speed, r, is the radius of the robot wheel.
Assume that the control input is Δ u (k)i),Δu(ki+1),...,Δu(ki+Np-1) in total NpThe number of the main components is one,
the state is x (k)i+1|ki),x(ki+2|ki),...,x(ki+Nc|ki),...,x(ki+Np|ki)。
From a state space model
Where A, B, C are different coefficient matrices.
Defining a vector
Y＝[y(ki+1|ki)y(ki+2|ki)y(ki+3|ki)...y(ki+Np|ki)]T
ΔU＝[Δu(ki)Δu(ki+1)Δu(ki+2)...Δu(ki+Nc-1)]T
So the predicted output Y ═ Fx (k)i) + Φ Δ U, wherein
Let us assume a setpoint signal r (k)i) And k isiThe closed loop performance adjusting parameter at the moment is rω. Order to
And a transformation matrix
So that the cost function
Thus, by minimizing the cost function, an optimal control input, i.e. minimizing the difference between the predicted system output and the desired reference output, may be obtained.
Considering the input constraint Deltaumin≤Δu(k)≤Δumax(in the implementation, the input constraint is that the rotation speed range of each wheel of the robot is [ -2m/s, 2 m/s)]But not limited to) and output constraint Ymin≤Fx(ki)+ΦΔU≤Ymax(the output constraint in the concrete implementation is the range of motion area [ -20m, 20m)]But is not limited to such).
Next the QP problem is solved by calling a function library and then the optimal control input can be obtained.
The examples of the invention are as follows:
in the present embodiment, in a rectangular area between the simulation areas (0m,0m) and (21m ), the starting point of the robot is (0m,0m), the target end point is (20m ), the center coordinates of the obstacle are (5m,5m), (6m,13m), (10m,8.5m), (15m,14m), (16m,5m), and the radii thereof are 2m, 2.5m, 2m, 3.5m, and 3m, respectively. The path planned by the optimized RRT algorithm is a smooth curve as shown in fig. 2.
Examples trace tracking was performed with the respective input speeds as shown in fig. 3 and 4. The upper subgraph is the actual motion speed of the robot, the middle subgraph is the speed components of the robot in the x and y directions, and the lower subgraph is the angular speed of the robot. Fig. 3 is the result without speed control. It can be seen that the robot gets a certain speed at each node, then slowly converges to zero and gets a new initial speed at the next node. This case can actually only achieve multiple point-to-point stabilizations without global speed control. Moreover, when the robot actually walks, the robot can often stop, which causes damage to the robot and is not practical. Fig. 4 is a result of the velocity control, and it can be seen that the robot obtains an initial velocity at zero time and then maintains a stable motion, and the time of the motion is greatly reduced compared to fig. 3. The velocity profile of the robot in both the x and y directions and its angular velocity profile are smooth curves, which illustrate the effectiveness of the velocity control.
The results of the trajectory tracking performed by the example are shown in fig. 5, in which the thick line is the path planned by the optimized RRT algorithm, and the thin line is the actual walking trajectory of the robot. It can be seen that the motion curve of the robot fits well to the planned path, and the two curves substantially coincide. The error between the path and the actual motion curve is shown in fig. 6, and it can be seen that the error is generally between-0.04 m and 0.04m, and the maximum error does not exceed 0.07 m.
Those skilled in the art can design the invention to be modified or varied without departing from the spirit and scope of the invention. Therefore, if such modifications and variations of the present invention fall within the technical scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (1)
1. A robot track tracking method with speed constraint is characterized by comprising the following steps:
(1) setting a human motion area, a starting point and an obstacle;
(2) planning a path, firstly obtaining the path by using an improved rapid exploration random tree algorithm, and then optimizing and curve fitting the path to obtain a smooth path;
(3) establishing speed constraint according to speed limits of three wheels of the robot and the motion range of the robot;
(4) establishing a system prediction model, and predicting the optimal input speed at the next moment according to the path nodes;
(5) repeatedly executing the step (4) to obtain the optimal input speed at each moment;
(6) inputting and controlling the robot to move according to the optimal speed;
the improved fast exploration random tree algorithm in the step (2) is as follows:
defining a global environment X, wherein an obstacle space xobstalce, an obstacle-free space Xfree ═ X/xobstalce, assuming that the obstacles of the obstacle space are known in advance and stationary; the starting point qinit and the end point qend are positioned in an unobstructed space; the path to be planned is a part of a tree T, the tree is formed by constructing sampling points V and connecting lines E between the sampling points, wherein the sampling points are called leaf nodes, and the connecting lines are called branches;
taking a random point qrand in X, finding a node qnear nearest to qrand in T, and connecting to form a Vector; step is taken from qnear on Vector to form a new node qnew; if the qnew and qnear connecting line is detected to have collision through collision, the growth is abandoned;
establishing a node set near qnew, taking qnew as the center of a circle and taking a preset R as the radius to make a circular space generated by the circle, comparing the accumulated cost of the tree node falling into the circle and the tree after the qnew is connected, selecting the node with the minimum cost as a father node qnear of the qnew, and adding the node into the tree;
assuming the node qnew of the newly added tree as a father node, calculating the cost of the node falling into a circle with the node qnew as the radius; selecting a node qmin with the minimum cost, connecting the node qmin with qnew, and deleting the branch originally connected with qmin; qmin is added into the tree as a child node of qnew;
repeating the steps to obtain a series of leaf nodes; when the distance between a certain node and the end point qend is less than the step length, connecting the two nodes, and if no collision exists between the two nodes, finding a feasible path from the starting point qinit to the end point qend;
the system prediction model in the step (4) is obtained by the following method:
the kinematic model of the omnidirectional mobile robot is represented as
Wherein
Is the state of the OMR in cartesian coordinates,
is the state of the omnidirectional mobile robot in world coordinates, H is a transformation matrix, thetacIs a steering angle;
from the model of the omni wheel, obtain
Wherein the matrix R is obtained by parameters of the omnidirectional wheel, determines the omnidirectional of the omnidirectional mobile robot, [ v [ [ v ]1 v2 v3]TThe speed of the three wheels of the robot;
the inverse kinematics equation of OMR is
Let S be R-1Then, then
Deriving continuous time system equations
Discretizing the formula (1) to obtain
Wherein T is the sampling time;
converting the discrete state space model into a discrete state space model, and obtaining X (k +1) ═ ax (k) + bu (k), where X (k +1) is the state at the next time, X (k) is the state at the time, u (k) is the change amount of the velocity, and A, B is a coefficient matrix;
the output model is y (k) ═ cx (k) + du (k), where C, D is the coefficient matrix; according to the theory of rolling optimization, since an input at a certain time cannot affect an output at that time, if matrix D is a zero matrix, y (k) is cx (k);
so that the prediction model is
The optimal input speed in the step (5) is obtained by the following method:
assume that the control input is Δ u (k)i),Δu(ki+1),...,Δu(ki+Np-1), total of NpThe number of the main components is one,
the state is x (k)i+1|ki),x(ki+2|ki),...,x(ki+Nc|ki),...,x(ki+Np|ki)；
The state space model obtained in the step (4)
To obtain
Defining a vector
Y＝[y(ki+1|ki) y(ki+2|ki) y(ki+3|ki) ... y(ki+Np|ki)]T
ΔU＝[Δu(ki) Δu(ki+1) Δu(ki+2) ... Δu(ki+Nc-1)]T
So the predicted output Y ═ Fx (k)i) + Φ Δ U, wherein
Let us assume a setpoint signal r (k)i) And k isiThe closed loop performance adjusting parameter at the moment is rω(ii) a Order to
And a transformation matrix
So that the cost function
Obtaining an optimal control input by minimizing a cost function, i.e. minimizing the difference between the predicted system output and the desired reference output;
considering the input constraint Deltaumin≤Δu(k)≤ΔumaxAnd output constraint Ymin≤Fx(ki)+ΦΔU≤Ymax；
Due to the existence of constraints, analytical solutions of optimization problems cannot be obtained, and a numerical optimization method is used for solving the problems;
the constraint-bearing quadratic programming is in the form of
The quadratic programming problem is solved by calling a function library and then obtaining the optimal control input.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009020749A (en) * | 2007-07-12 | 2009-01-29 | Toshiba Corp | Robot control apparatus and method |
| CN104317299A (en) * | 2014-11-11 | 2015-01-28 | 东南大学 | Mixed control method based on trace tracking of wheeled mobile robot |
| CN106125728A (en) * | 2016-07-05 | 2016-11-16 | 上海电机学院 | A kind of 4 wheel driven wheeled mobile robot trace tracking and controlling method |
| CN106444769A (en) * | 2016-10-31 | 2017-02-22 | 湖南大学 | Method for planning optimal path for incremental environment information sampling of indoor mobile robot |
| CN106695802A (en) * | 2017-03-19 | 2017-05-24 | 北京工业大学 | Improved RRT<*> obstacle avoidance motion planning method based on multi-degree-of-freedom mechanical arm |
-
2018
- 2018-11-23 CN CN201811405669.3A patent/CN109491389B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009020749A (en) * | 2007-07-12 | 2009-01-29 | Toshiba Corp | Robot control apparatus and method |
| CN104317299A (en) * | 2014-11-11 | 2015-01-28 | 东南大学 | Mixed control method based on trace tracking of wheeled mobile robot |
| CN106125728A (en) * | 2016-07-05 | 2016-11-16 | 上海电机学院 | A kind of 4 wheel driven wheeled mobile robot trace tracking and controlling method |
| CN106444769A (en) * | 2016-10-31 | 2017-02-22 | 湖南大学 | Method for planning optimal path for incremental environment information sampling of indoor mobile robot |
| CN106695802A (en) * | 2017-03-19 | 2017-05-24 | 北京工业大学 | Improved RRT<*> obstacle avoidance motion planning method based on multi-degree-of-freedom mechanical arm |
Non-Patent Citations (2)
| Title |
|---|
| 基于改进RRT*的移动机器人运动规划算法;潘思宇 等;《山西大学学报（自然科学版）》;20171231;第40卷(第2期);第244-254页 * |
| 基于模型预测控制的移动机器人轨迹跟踪;曾志文 等;《控制工程》;20110930;第18卷;第80-85页 * |
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