CN112572453B - Gait planning method, device, equipment and medium for robot walking on slope - Google Patents

Gait planning method, device, equipment and medium for robot walking on slope Download PDF

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CN112572453B
CN112572453B CN202011518724.7A CN202011518724A CN112572453B CN 112572453 B CN112572453 B CN 112572453B CN 202011518724 A CN202011518724 A CN 202011518724A CN 112572453 B CN112572453 B CN 112572453B
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robot
slope
leg
angle
joint
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CN112572453A (en
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陈首彦
张健
黄俊锋
王蕴婷
赵志甲
邹涛
杨晓芬
孙欣琪
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Guangzhou University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/076—Slope angle of the road
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D57/00—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track
    • B62D57/02—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track with ground-engaging propulsion means, e.g. walking members
    • B62D57/032—Vehicles characterised by having other propulsion or other ground- engaging means than wheels or endless track, alone or in addition to wheels or endless track with ground-engaging propulsion means, e.g. walking members with alternately or sequentially lifted supporting base and legs; with alternately or sequentially lifted feet or skid
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation

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  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
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Abstract

The invention discloses a gait planning method, a device, equipment and a medium for a robot walking on a slope, wherein the method comprises the following steps: determining the ramp material and the inclination angle of the target ramp; collecting a bone point data set of a leg part of a human body in the walking process on the target slope, and constructing a slope walking track; constructing a leg model of the robot, and determining the incidence relation among joint angles of the robot; establishing a nonlinear equation set according to the incidence relation and the slope walking track; constructing a control model according to the nonlinear equation set; the control model is used for calculating a joint angle value when the robot walks on a target slope; and controlling the robot to walk on the slope according to the joint angle value. The invention improves the stability of the robot action, enables the robot to walk on the slope, has strong adaptability and can be widely applied to the technical field of robots.

Description

Gait planning method, device, equipment and medium for robot walking on slope
Technical Field
The invention relates to the technical field of robots, in particular to a gait planning method, a gait planning device, gait planning equipment and gait planning medium for walking of a robot on a slope.
Background
The NAO robot is a humanoid robot developed by Aldebaran company, integrates knowledge achievement in the robot field, and has strong expansion capability and performance. NAO robots are widely used: teaching, scientific research, industry and other fields. The entire body of the NAO robot has 25 degrees of freedom, and the NAO robot is provided with more than 100 sensors with different functions and supports 32 national languages in total. The chest of the NAO robot is simultaneously provided with an inertia unit, and the motion speed of the robot body can be estimated through the inertia unit. The leg of robot has pressure sensor, mainly is used for calculating the focus position, prevents that the robot from falling down and damaging self components and parts because of the action is too big.
The humanoid robot belongs to a humanoid robot, and is typically characterized in that lower limbs of the robot are connected through a revolute pair by rigid members to simulate legs, ankles, knee joints and hip joints of a human. And the executing device replaces muscles to realize the support and continuous coordinated movement of the body, and the joints can rotate relatively at a certain angle. The humanoid robot has low requirements on walking environment, can adapt to various grounds, has higher capability of surmounting obstacles, can walk on a plane, can conveniently go up and down steps and pass through uneven, irregular or narrower pavements, and has strong capability of adapting to the real environment; the humanoid robot also has a wide working space, and the manipulator arranged on the humanoid robot has a larger moving space due to the small floor area and the large moving range of the walking system. The humanoid robot can work in cooperation with human beings in human life and work without specially modifying the environment on a large scale. Therefore, the humanoid robot has wide application fields and has irreplaceable effects in the aspects of replacing people to work in extreme environments and the like. The research on the walking problem of the humanoid robot is always a research hotspot in the robot field, and the current main research methods comprise: the gait planning method based on the dynamics analysis, the gait planning method based on the ZMP theory and the inverted pendulum model method. Such methods often require complex operations and have a single motion pattern.
Meanwhile, experts also propose a gait planning method based on human body motion capture, and human walking has high flexibility, adaptability and stability, which are the targets to be achieved by robot walking. The method is based on collected data of human walking, then the data are modified and corrected, and the gait tracks of human walking can be expanded to each joint of the robot to achieve gait planning due to the fact that mechanics have similar performance. However, the data obtained by the method when the human body walks cannot be directly used on the humanoid robot, and the method can only be used for some simple walking motions.
Disclosure of Invention
In view of this, embodiments of the present invention provide a gait planning method, apparatus, device and medium for a robot walking on a slope, which has high stability and high adaptability.
One aspect of the present invention provides a gait planning method for a robot walking on a slope, including:
determining the ramp material and the inclination angle of the target ramp;
collecting a bone point data set of a leg part of a human body in the walking process on the target slope, and constructing a slope walking track;
constructing a leg model of the robot, and determining the incidence relation among joint angles of the robot;
establishing a nonlinear equation set according to the incidence relation and the slope walking track;
constructing a control model according to the nonlinear equation set; the control model is used for calculating a joint angle value when the robot walks on a target slope;
and controlling the robot to walk on the slope according to the joint angle value.
Preferably, the acquiring a bone point data set of a leg of the human body in the walking process on the target slope to construct a slope walking track includes:
acquiring a skeletal point data set of a leg of a human body in the walking process on the target slope through a somatosensory camera;
and fitting the bone point data set through a neural network to obtain a slope walking track.
Preferably, the building a leg model of the robot and determining the association relationship between the joint angles of the robot includes:
constructing a robot leg model;
determining a left leg ankle joint pitch angle, a right leg ankle joint pitch angle, a left leg ankle joint flip angle, a right leg ankle joint flip angle, a left leg hip joint pitch angle and a right leg hip joint pitch angle of the robot according to the robot leg model;
determining a left leg knee joint pitch angle of the robot according to the left leg ankle joint pitch angle and the left leg hip joint pitch angle;
determining a right leg knee joint pitch angle of the robot according to the right leg ankle joint pitch angle and the right hip joint pitch angle;
determining the left leg hip joint flip angle of the robot according to the left leg ankle joint flip angle;
and determining the turnover angle of the hip joint of the right leg of the robot according to the turnover angle of the ankle joint of the right leg.
Preferably, in the step of establishing a nonlinear equation set according to the association relationship and the slope walking trajectory, the nonlinear equation set is:
X＝L1s1(c3c4-c2s3s4)-c1(c2(s3c4-c2c3s4)-s2s4s2))+L2(s3c4+c2c3s4)+L3c2s4
Y＝L3s2-L1(c1(c2s2-c2c3s2)+s1s2s3)-L4+L2c3s2
Z＝-L1(s1(s3+1)s34-c1(s2c4s2+c2((c2-1)c34)))+L2(c2-1)c34+L3c2c4+L4
wherein X, Y, Z represents the distance between the NAO robot and the world coordinate system along x,A walking track data set in the y and z directions; l is1The distance between the sole of the NAO robot and the ankle joint is defined; l is2The length of the shank of the NAO robot is shown; l is3The length of the thigh of the NAO robot; l is4The length from the center of gravity of the NAO robot to the hip joint; c. C1Represents cos alpha1；c2Represents cos alpha2；c3Represents cos alpha3；c4Represents cos alpha4；c34Represents cos (. alpha.) of3+α4)；s1Denotes sin α1；s2Denotes sin α2；s3Denotes sin α3；s4Denotes sin α4；s34Denotes sin (α)3+α4)；α1The pitch angle of the ankle joint of the left leg of the robot is shown; alpha is alpha2The ankle joint turnover angle of the left leg of the robot is shown; alpha is alpha3A knee joint pitch angle of a left leg of the robot; alpha is alpha4Is the hip joint pitch angle of the left leg of the robot; alpha is alpha5Is the hip joint flip angle of the left leg of the robot.
Preferably, the constructing a control model according to the nonlinear equation set includes:
obtaining an accurate solution of the robot joint angle value through a Newton-Raphson iterative algorithm;
and deriving to obtain a control model through the nonlinear equation set according to the accurate solution of the joint angle value.
Preferably, the method further comprises:
acquiring motion data of the robot on the target slope;
optimizing and updating the control model according to the motion data;
and determining the action track of the robot by optimizing the updated control model.
The embodiment of the invention also provides a gait planning device for walking on a slope by a robot, which comprises:
the determining module is used for determining the inclined body material and the inclination angle of the target slope;
the acquisition module is used for acquiring a bone point data set of a leg part of a human body in the walking process on the target slope and constructing a slope walking track;
the first building module is used for building a leg model of the robot and determining the incidence relation among joint angles of the robot;
the second building module is used for building a nonlinear equation set according to the incidence relation and the slope walking track;
the third construction module is used for constructing a control model according to the nonlinear equation set; the control model is used for calculating a joint angle value when the robot walks on a target slope;
and the motion control module is used for controlling the robot to walk on the slope according to the joint angle value.
The embodiment of the invention also provides the electronic equipment, which comprises a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the method described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
The embodiment of the invention first determines the ramp material and the inclination angle of the target ramp; collecting a bone point data set of a leg part of a human body in the walking process on the target slope, and constructing a slope walking track; constructing a leg model of the robot, and determining the incidence relation among joint angles of the robot; establishing a nonlinear equation set according to the incidence relation and the slope walking track; constructing a control model according to the nonlinear equation set; the control model is used for calculating a joint angle value when the robot walks on a target slope; and controlling the robot to walk on the slope according to the joint angle value. The invention improves the stability of the robot action, enables the robot to walk on the slope and has strong adaptability.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of steps provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a model of a robot walking on a slope according to an embodiment of the present invention;
fig. 3 is a ZMP criterion graph of the robot in the walking process according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In view of the problems in the prior art, an embodiment of the present invention provides a gait planning method for a robot walking on a slope, as shown in fig. 1, the method includes:
determining the ramp material and the inclination angle of the target ramp;
collecting a bone point data set of a leg part of a human body in the walking process on the target slope, and constructing a slope walking track;
constructing a leg model of the robot, and determining the incidence relation among joint angles of the robot;
establishing a nonlinear equation set according to the incidence relation and the slope walking track;
constructing a control model according to the nonlinear equation set; the control model is used for calculating a joint angle value when the robot walks on a target slope;
and controlling the robot to walk on the slope according to the joint angle value.
Preferably, the acquiring a bone point data set of a leg of the human body in the walking process on the target slope to construct a slope walking track includes:
acquiring a skeletal point data set of a leg of a human body in the walking process on the target slope through a somatosensory camera;
and fitting the bone point data set through a neural network to obtain a slope walking track.
Preferably, the building a leg model of the robot and determining the association relationship between the joint angles of the robot includes:
constructing a robot leg model;
determining a left leg ankle joint pitch angle, a right leg ankle joint pitch angle, a left leg ankle joint flip angle, a right leg ankle joint flip angle, a left leg hip joint pitch angle and a right leg hip joint pitch angle of the robot according to the robot leg model;
determining a left leg knee joint pitch angle of the robot according to the left leg ankle joint pitch angle and the left leg hip joint pitch angle;
determining a right leg knee joint pitch angle of the robot according to the right leg ankle joint pitch angle and the right hip joint pitch angle;
determining the left leg hip joint flip angle of the robot according to the left leg ankle joint flip angle;
and determining the turnover angle of the hip joint of the right leg of the robot according to the turnover angle of the ankle joint of the right leg.
Preferably, in the step of establishing a nonlinear equation set according to the association relationship and the slope walking trajectory, the nonlinear equation set is:
X＝L1s1(c3c4-c2s3s4)-c1(c2(s3c4-c2c3s4)-s2s4s2))+L2(s3c4+c2c3s4)+L3c2s4
Y＝L3s2-L1(c1(c2s2-c2c3s2)+s1s2s3)-L4+L2c3s2
Z＝-L1(s1(s3+1)s34-c1(s2c4s2+c2((c2-1)c34)))+L2(c2-1)c34+L3c2c4+L4
x, Y, Z is a walking track data set in x, y and z directions in a world coordinate system when the NAO robot walks on a slope; l is1The distance between the sole of the NAO robot and the ankle joint is defined; l is2The length of the shank of the NAO robot is shown; l is3The length of the thigh of the NAO robot; l is4The length from the center of gravity of the NAO robot to the hip joint; c. C1Represents cos alpha1；c2Represents cos alpha2；c3Represents cos alpha3；c4Represents cos alpha4；c34Represents cos (. alpha.) of3+α4)；s1Denotes sin α1；s2Denotes sin α2；s3Denotes sin α3；s4Denotes sin α4；s34Denotes sin (α)3+α4)；α1The pitch angle of the ankle joint of the left leg of the robot is shown; alpha is alpha2The ankle joint turnover angle of the left leg of the robot is shown; alpha is alpha3A knee joint pitch angle of a left leg of the robot; alpha is alpha4Is the hip joint pitch angle of the left leg of the robot; alpha is alpha5Is the hip joint flip angle of the left leg of the robot.
Preferably, the constructing a control model according to the nonlinear equation set includes:
obtaining an accurate solution of the robot joint angle value through a Newton-Raphson iterative algorithm;
and deriving to obtain a control model through the nonlinear equation set according to the accurate solution of the joint angle value.
Preferably, the method further comprises:
acquiring motion data of the robot on the target slope;
optimizing and updating the control model according to the motion data;
and determining the action track of the robot by optimizing the updated control model.
The following describes in detail the implementation process of the gait planning method of the robot walking on a slope by taking the NAO robot as an example.
The invention relates to a gait planning method for an NAO (NAO humanoid robot) to walk on a slope, which is characterized in that an optimal slope walking track is fitted by combining a leg mass center data set obtained by human body teaching with a neural network, a specific leg model of the NAO humanoid robot is constructed based on robot kinematics, the relation among all joint angles is obtained, a nonlinear equation set is established, a control model is further deduced according to the nonlinear equation set, so that a driving joint angle is obtained, and the NAO humanoid robot is driven to walk on the slope. And then, the ZMP criterion is combined to judge the stability of the NAO humanoid robot in the walking process, as shown in fig. 2, fig. 2 is a model sketch of the NAO robot at a certain moment when the NAO robot walks on a slope. At this point, some joint angles on both legs of the NAO robot are as shown on the figure. The optimal slope walking track is obtained by fitting the leg mass center information of the human body when the human body finishes the slope walking action through a neural network, and the optimal slope walking track is input into the gait planning model method, so that the driving joint angle for controlling the NAO humanoid robot when the NAO humanoid robot walks on the slope can be directly obtained. The specific implementation process is as follows:
(1) setting an inclined plane material mu and an inclination angle theta to meet the condition tan (theta) less than or equal to mu;
wherein, mu represents the friction coefficient of the material, and theta represents the slope of the inclined plane;
(2) acquiring a skeletal point data set of legs of a human body in a walking process on a slope by using a somatosensory camera kinect2.0, and fitting an optimal slope walking track in the direction of X, Y, Z by using a neural network, as shown in fig. 3, wherein fig. 3 is a ZMP criterion diagram of an NAO robot in the walking process and is used for judging the stability of the robot in the walking process;
(3) constructing a leg model specific to the NAO humanoid robot based on the robot kinematics, and determining the distance between the sole and the ankle joint to be L1The length of the shank is L2Thigh length L3The length from the center of gravity of the robot to the hip joint is L4Determining a relationship between the joint angles, wherein:
α3＝-a1-α4
α3′＝-a1′-α4′
α5＝-α2
α5′＝-α2′
wherein alpha is1、α1' the pitch angles of the ankle joints of the left leg and the right leg of the NAO robot are respectively; alpha is alpha2、α2' ankle joint flip angles of a left leg and a right leg of the NAO robot are respectively; alpha is alpha3、α3' knee joint pitch angles of the left leg and the right leg of the NAO robot are respectively; alpha is alpha4、α4' hip joint pitch angles of the left leg and the right leg of the NAO robot are respectively; alpha is alpha5、α5' are hip joint flip angles of the left leg and the right leg of the NAO robot respectively.
And fitting optimal slope walking track data in the direction of X, Y, Z by using a neural network, and establishing a nonlinear equation set of the two legs of the NAO humanoid robot, wherein the equation set is as follows:
X＝L1s1(c3c4-c2s3s4)-c1(c2(s3c4-c2c3s4)-s2s4s2))+L2(s3c4+c2c3s4)+L3c2s4；
Y＝L3s2-L1(c1(c2s2-c2c3s2)+s1s2s3)-L4+L2c3s2；
Z＝-L1(s1(s3+1)s34-c1(s2c4s2+c2((c2-1)c34)))+L2(c2-1)c34+L3c2c4+L4；
x, Y, Z is a walking track data set in x, y and z directions in a world coordinate system when the NAO robot walks on a slope; l is1The distance between the sole of the NAO robot and the ankle joint is defined; l is2Is the length, L, of the lower leg of the NAO robot3Length of thigh of NAO robot, L4The length from the center of gravity of the NAO robot to the hip joint; c. C1Represents cos alpha1，c2Represents cos alpha2，c3Represents cos alpha3，c4Represents cos alpha4，c34Represents cos (. alpha.) of3+α4)，s1Denotes sin α1，s2Denotes sin α2，s3Denotes sin α3，s4Denotes sin α4，s34Denotes sin (α)3+α4)。
(4) Deriving a control model according to a nonlinear equation set, approximating joint angle accurate solutions of Hip, Knee and Ankle by using a Newton-Raffson iterative algorithm, deriving the control model according to the NAO humanoid robot nonlinear equation set, and establishing a relation of x (t) ═ f (alpha (t)), wherein
Figure BDA0002848292550000071
Approximating alpha using a Newton-Raphson iterative algorithmhip、αknee、αankleThe joint angle of the NAO humanoid robot is accurately solved, and the method is carried out in the Newton-Raphson algorithm of the joint angles of the two legs of the NAO humanoid robot
Figure BDA0002848292550000072
Can be defined as
Figure BDA0002848292550000073
Wherein xcAs desired coordinates, the goal being to find the joint angle αcAnd make it possible to
Figure BDA0002848292550000074
In x (t) ═ f (α (t)), x (t) represents an input equation, and f (α (t)) represents a function of the angle with time; in that
Figure BDA0002848292550000075
In (1),
Figure BDA0002848292550000076
meaning that the first derivative of the angle with respect to time is taken,
Figure BDA0002848292550000077
it is shown that the derivation is performed on the angle,
Figure BDA0002848292550000078
denotes the angular derivative, J (α) denotes the coordinate jacobian at α; alpha is alphahipAngle of hip joint, alphakneeAngle of knee joint, αankleIs the ankle joint angle;
Figure BDA0002848292550000081
for a given differential equation, xcFor the expected end coordinates, f (α) is the forward kinematic equation.
The initial estimate a is known0≈αcThe NAO humanoid robot inverse kinematics can be written as taylor expansion:
Figure BDA0002848292550000082
wherein
Figure BDA0002848292550000083
Can be expressed as J (alpha)0) Is represented by alpha0The coordinates of (a) are jacobian;
let Δ α be αc-α0Intercepting the first term of the taylor series yields:
J(α0)Δα＝xc-f(α0),
wherein alpha is0Representing an initial estimate of the angle, αcExpressed as the true value of the angle, Δ α represents the deviation of the true value of the angle from the initial estimate.
Because of the existence alpha of each leg of the NAO humanoid roboti(i is 1,2,3,4,5), that is, if three relations continue to exist for αiThe (i-1, 2,3,4,5) formula can uniquely determine αi(i ═ 1,2,3,4,5), X, Y, Z may each be defined by three independent αi(i ═ 1,2,3,4,5), i.e. can be expressed by three independent αi(i-1, 2,3,4,5) expresses X, Y, Z. Thus, J (α)0) Can be a square matrix and reversible, and has the formula of delta alpha-J-1(α0)(xc-f(α0) Solving for Δ α, iterate continuously, to obtain { α [)0,α1,α2,α3… …, a threshold value epsilon is set in advance to 0.01 so that:
Figure BDA0002848292550000084
finally, the angle alpha of the joint of the two legs of the NAO humanoid robotcAnd (4) convergence.
Wherein alpha isiIndicates the joint angle of each leg of the NAO robot, { α0,α1,α2,α3… … represents a series of initial estimates, a, produced after each iteration1,α2,α3… … shows the initial estimate after one, two, three iterations.
(5) And driving the NAO robot to move on the slope by using the obtained joint angle, and then repeating the experiment until the NAO robot walking experiment obtains the optimal track.
In conclusion, the optimal slope walking track is fitted by combining a leg mass center data set obtained through human body teaching with a neural network, a leg model specific to the NAO humanoid robot is constructed based on robot kinematics, the relation among joint angles is obtained, a nonlinear equation set is established, a control model is further deduced according to the nonlinear equation set, so that the driving joint angles are obtained, and the NAO humanoid robot is driven to walk on the slope. And the stability of the NAO humanoid robot in the walking process is judged by combining with ZMP criterion. The optimal slope walking track is obtained by fitting the leg mass center information of the human body when the human body finishes the slope walking action through a neural network, and the optimal slope walking track is input into the gait planning model method, so that the driving joint angle for slope walking control can be directly obtained. The invention greatly simplifies the complexity of the step planning when the NAO humanoid robot walks on the slope
The embodiment of the invention also provides a gait planning device for walking on a slope by a robot, which comprises:
the determining module is used for determining the inclined body material and the inclination angle of the target slope;
the acquisition module is used for acquiring a bone point data set of a leg part of a human body in the walking process on the target slope and constructing a slope walking track;
the first building module is used for building a leg model of the robot and determining the incidence relation among joint angles of the robot;
the second building module is used for building a nonlinear equation set according to the incidence relation and the slope walking track;
the third construction module is used for constructing a control model according to the nonlinear equation set; the control model is used for calculating a joint angle value when the robot walks on a target slope;
and the motion control module is used for controlling the robot to walk on the slope according to the joint angle value.
The embodiment of the invention also provides the electronic equipment, which comprises a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
An embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the method described above.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and the computer instructions executed by the processor cause the computer device to perform the foregoing method.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (8)

1. A gait planning method for walking on a slope by a robot is characterized by comprising the following steps:
determining the ramp material and the inclination angle of the target ramp;
collecting a bone point data set of a leg part of a human body in the walking process on the target slope, and constructing a slope walking track;
constructing a leg model of the robot, and determining the incidence relation among joint angles of the robot;
establishing a nonlinear equation set according to the incidence relation and the slope walking track;
constructing a control model according to the nonlinear equation set; the control model is used for calculating a joint angle value when the robot walks on a target slope;
controlling the robot to walk on a slope according to the joint angle value;
the method for constructing the leg model of the robot and determining the incidence relation among the joint angles of the robot comprises the following steps:
constructing a robot leg model;
determining a left leg ankle joint pitch angle, a right leg ankle joint pitch angle, a left leg ankle joint flip angle, a right leg ankle joint flip angle, a left leg hip joint pitch angle and a right leg hip joint pitch angle of the robot according to the robot leg model;
determining a left leg knee joint pitch angle of the robot according to the left leg ankle joint pitch angle and the left leg hip joint pitch angle;
determining a right leg knee joint pitch angle of the robot according to the right leg ankle joint pitch angle and the right hip joint pitch angle;
determining the left leg hip joint flip angle of the robot according to the left leg ankle joint flip angle;
and determining the turnover angle of the hip joint of the right leg of the robot according to the turnover angle of the ankle joint of the right leg.
2. The gait planning method for walking on a slope by a robot according to claim 1, wherein the collecting the bone point data set of the leg of the human body during the walking on the target slope to construct a slope walking track comprises:
acquiring a skeletal point data set of a leg of a human body in the walking process on the target slope through a somatosensory camera;
and fitting the bone point data set through a neural network to obtain a slope walking track.
3. The gait planning method for walking on a slope by a robot according to claim 1, wherein in the step of establishing a nonlinear equation set according to the correlation and the slope walking trajectory, the nonlinear equation set is:
X＝L1s1(c3c4-c2s3s4)-c1(c2(s3c4-c2c3s4)-s2s4s2))+L2(s3c4+c2c3s4)+L3c2s4
Y＝L3s2-L1(c1(c2s2-c2c3s2)+s1s2s3)-L4+L2c3s2
Z＝-L1(s1(s3+1)s34-c1(s2c4s2+c2((c2-1)c34)))+L2(c2-1)c34+L3c2c4+L4
x, Y, Z is a walking track data set in x, y and z directions in a world coordinate system when the NAO robot walks on a slope; l is1The distance between the sole of the NAO robot and the ankle joint is defined; l is2The length of the shank of the NAO robot is shown; l is3The length of the thigh of the NAO robot; l is4The length from the center of gravity of the NAO robot to the hip joint; c. C1Represents cos alpha1；c2Represents cos alpha2；c3Represents cos alpha3；c4Represents cos alpha4；c34Represents cos (. alpha.) of3+α4)；s1Denotes sin α1；s2Denotes sin α2；s3Denotes sin α3；s4Denotes sin α4；s34Denotes sin (α)3+α4)；α1The pitch angle of the ankle joint of the left leg of the robot is shown; alpha is alpha2The ankle joint turnover angle of the left leg of the robot is shown; alpha is alpha3A knee joint pitch angle of a left leg of the robot; alpha is alpha4Is the hip joint pitch angle of the left leg of the robot; alpha is alpha5Is the hip joint flip angle of the left leg of the robot.
4. A gait planning method for walking on a slope by a robot according to claim 1, characterized in that said constructing a control model according to said system of nonlinear equations comprises:
obtaining an accurate solution of the robot joint angle value through a Newton-Raphson iterative algorithm;
and deriving to obtain a control model through the nonlinear equation set according to the accurate solution of the joint angle value.
5. A gait planning method for walking on a slope by a robot according to claim 1, characterized in that the method further comprises:
acquiring motion data of the robot on the target slope;
optimizing and updating the control model according to the motion data;
and determining the action track of the robot by optimizing the updated control model.
6. A gait planning device for walking on a slope by a robot is characterized by comprising:
the determining module is used for determining the inclined body material and the inclination angle of the target slope;
the acquisition module is used for acquiring a bone point data set of a leg part of a human body in the walking process on the target slope and constructing a slope walking track;
the first building module is used for building a leg model of the robot and determining the incidence relation among joint angles of the robot;
the second building module is used for building a nonlinear equation set according to the incidence relation and the slope walking track;
the third construction module is used for constructing a control model according to the nonlinear equation set; the control model is used for calculating a joint angle value when the robot walks on a target slope;
the motion control module is used for controlling the robot to walk on a slope according to the joint angle value;
wherein the first building block is specifically configured to:
constructing a robot leg model;
determining a left leg ankle joint pitch angle, a right leg ankle joint pitch angle, a left leg ankle joint flip angle, a right leg ankle joint flip angle, a left leg hip joint pitch angle and a right leg hip joint pitch angle of the robot according to the robot leg model;
determining a left leg knee joint pitch angle of the robot according to the left leg ankle joint pitch angle and the left leg hip joint pitch angle;
determining a right leg knee joint pitch angle of the robot according to the right leg ankle joint pitch angle and the right hip joint pitch angle;
determining the left leg hip joint flip angle of the robot according to the left leg ankle joint flip angle;
and determining the turnover angle of the hip joint of the right leg of the robot according to the turnover angle of the ankle joint of the right leg.
7. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program realizes the method according to any one of claims 1-5.
8. A computer-readable storage medium, characterized in that the storage medium stores a program, which is executed by a processor to implement the method according to any one of claims 1-5.
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