Autonomous motion planning method for humanoid robot legs

Abstract

The present invention provides a method for planning autonomous motion of humanoid robot legs, comprising: step S1: describing the tasks of the legs when the robot is doing an action in a world coordinate system; step S2: defining a problem to be solved, and deriving a dynamic equation of a two-link model of a humanoid leg; step S3: using a b-spline polynomial with a time scale factor to represent the robot joint torque trajectory, and incorporating its physical limitations into the formula; step S4: on the basis of the torque curve formula, by constructing a variety of objective functions, the torque curve planning problem is converted into a multi-objective function optimization problem with constraints; step S5: solving the multi-objective optimization problem on the basis of a standard dynamics model, optimizing multiple objectives at the same time, and obtaining an optimal torque curve; step S6: on the basis of the existing torque curve, designing a closed-loop control system to track the torque curve in the joint space. The present invention can expand the motor capacity of the robot and reduce energy consumption at the same time.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a method for autonomous leg motion planning of a humanoid robot. Background Art

[0002] Humanoid robots are now the focus of robotics research because they can perform more precise operations and their appearance is more acceptable to users. Many tasks have been proposed for humanoid robots, such as using humanoid robots to play football, but in order to successfully complete the task and achieve the flexibility and dynamics comparable to humans, this poses new challenges to the design of humanoid robot control algorithms.

[0003] At present, various trajectory planning methods for humanoid robot legs are usually implemented in Cartesian space. Among them, Cartesian space trajectory planning first finds a curve in Cartesian space that can achieve the task requirements, and then performs inverse kinematics and optimization technology on each point on the trajectory to obtain the trajectory of the joint space, and then designs a controller in the joint space to track the trajectory. However, when the robot is manufactured from the factory, there are dynamic performance limitations, such as physical limitations set by the manufacturer. This method usually only considers all tasks and constraints at the speed or position level during motion planning, but ignores the dynamic performance of the robot, making it impossible for the robot to perform excellent dynamic operations beyond the limit. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a method for autonomous motion planning of the legs of a humanoid robot.

[0005] According to a method for autonomous motion planning of a humanoid robot leg provided by the present invention, the method is as follows:

[0006] Step S1: describe the tasks of the robot's legs when performing actions in the world coordinate system;

[0007] Step S2: define the problem to be solved and derive the dynamic equations of the two-link model of the humanoid leg;

[0008] Step S3: using b-spline polynomials with time scale factors to represent the robot joint torque trajectory, and incorporating its physical limitations into the formula;

[0009] Step S4: Based on the torque curve formula, the torque curve planning problem is transformed into a constrained multi-objective function optimization problem by constructing multiple objective functions;

[0010] Step S5: solving the multi-objective optimization problem based on the standard dynamics model, optimizing multiple objectives at the same time, and obtaining the optimal torque curve;

[0011] Step S6: Based on the existing torque curve, a closed-loop control system is designed to track the torque curve in the joint space.

[0012] Preferably, the step S1 comprises:

[0013] When the robot is performing an action, the leg task requires the two joints of the leg to swing and cooperate to achieve the maximum linear velocity of the end of the foot. The high dynamic tasks of the legged robot are expressed in the world coordinate system:

[0014] (1)

[0015] (2)

[0016] in,

[0017] (3)

[0018] in, is the active torque of the first joint, the hip joint, and the second joint, the knee joint; is the coupling torque is the passive torque produced by the joint; is the moment of inertia; is the mass of the connecting rod; is the length of the connecting rod; is the length from the center of mass to the joint; are generalized coordinates; and r indicates the absolute angular position of the first joint and the second joint; represents the angular position of the second joint relative to the first joint; is the free rotation range of the second joint; and is the range of motion of the joint in the clockwise and counterclockwise directions. represents the acceleration due to gravity in the plane of the swing.

[0019] Preferably, step S2 comprises:

[0020] Define the problem: Formulate the motion as a three-point boundary condition problem;

[0021] Initial Configuration: ;

[0022] Impact time configuration: ;

[0023] Abort configuration: ;

[0024] in, It’s the moment of impact; It’s the end moment; for The translational velocity in the direction for Translational velocity in direction; The target impact velocity The direction of the component, The target impact velocity The direction of the component, is the target impact position; then define the motion error variable,

[0025] (4)

[0026] Represents the joint angle error variable and the y-direction translation velocity error variable at the moment of impact; represents the joint angle error variable and joint velocity error variable when the termination condition is reached; considering the motion error and constraints, the objective function with constraints is:

[0027] (5)

[0028] in, represents the lower bound, represents the upper bound; Represents joint variables; upper and lower bounds of joint speed and the torque upper and lower bounds Given manually, the robot dynamics model is defined as:

[0029] (6)

[0030] in, is the mass matrix; is the Coriolis force matrix; is the gravity vector; is the friction term.

[0031] Preferably, step S3 comprises:

[0032] The active torque trajectory is directly approximated by using B-spline curve, and then the hard constraints of the active torque are transformed into a series of linear inequality constraints of B-spline coefficients;

[0033] The fourth-order B-spline basis function is used to parameterize the initial time To the final time The active torque is defined as:

[0034] (7)

[0035] in, is the parameter vector, is the b-spline basis function, which has the following form:

[0036] (8)

[0037] Among them, the operator and The definition is as follows

[0038] (9)

[0039] (10)

[0040] Assume the maximum active torque is Then the hard constraint of the active moment is expressed as follows:

[0041] (11)

[0042] Now the hard constraints on the active torque have become a series of linear inequality constraints on the b-spline coefficients.

[0043] Preferably, the step S4 comprises:

[0044] according to The parameter constraints, total power consumption TCW, minimization of the torque SSDT and the sum of the square derivatives of the kinetic energy KE, define the trajectory planning problem as:

[0045] (12)

[0046] in, is the total consumed work TCW, is the minimum torque SSDT, is the kinetic energy KE; and is the corresponding weight coefficient;

[0047] While satisfying the kinematic specifications and active torque hard constraints, TCW, SSDT, and KE are defined as:

[0048] (13).

[0049] Preferably, the step S5 comprises: solving the multi-objective optimization problem based on the Knee point evolutionary algorithm KGEA;

[0050] The overall framework of the evolutionary algorithm KGEA includes:

[0051] 1) Randomly generate an initial parent population of size N;

[0052] 2) A binary competition strategy is used to select individuals from the parent population and generate N offspring individuals by mutation. In the binary competition selection, three competition indicators are used, namely, dominance relationship, knee point criterion and weighted distance measurement;

[0053] 3) Perform non-dominated sorting on the combination of parent population and offspring population, and then use an adaptive strategy to identify the solution of the knee region of each non-dominated front in the combined population;

[0054] 4) Finally, environmental selection is carried out to select N individuals as the parent group of the next generation;

[0055] This process is repeated until the preset termination condition is met. For example, the minimum Euclidean distance or Manhattan distance composed of multiple objective functions can be used as the termination condition, which can be expressed as argmin or argmin , where argmin Indicates Minimum, represents the Euclidean distance of the three objective functions, represents the Manhattan distance.

[0056] Preferably, step S6 comprises: obtaining the real-time angle and angular velocity of each joint in the leg joints of the humanoid robot through a designed closed-loop control system, and tracking the torque curve in the joint space.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. Compared with the traditional position or speed level planning method, the present invention can obtain the appropriate minimum torque solution that meets the high-burst conditions such as jumping and sudden braking that occur during the actual movement of the robot by introducing the limitation of the motor's physical parameter performance in the process of solving the joint torque, thereby effectively extending the service life of the motor.

[0059] 2. The present invention expresses the various constraints for solving the robot's optimal trajectory as a multi-objective function problem, effectively avoiding the problem of falling into the local optimal solution that occurs when the traditional method integrates all objective functions into one cost function, and avoids the problem of redesigning weights required for different motion conditions.

[0060] 3. The present invention proposes a KGEA optimization method for solving the optimal trajectory of the robot under the multi-objective function, which can obtain a convergent optimal solution set under a limited number of iterations, thereby effectively improving the solution speed.

[0061] 4. The present invention reduces the joint torque while expanding the motor performance of the robot, reducing energy consumption and having high application value.

[0062] Other beneficial effects of the present invention will be explained in the specific implementation manner through the introduction of specific technical features and technical solutions. Through the introduction of these technical features and technical solutions, those skilled in the art should be able to understand the beneficial technical effects brought about by the technical features and technical solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0064] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0065] Figure 2 It is a schematic diagram of a high-dynamic task of a legged robot in an embodiment of the present invention in a world coordinate system;

[0066] Figure 3 Schematic diagram of a multi-objective optimization framework in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0068] The embodiment of the present invention provides a method for autonomous motion planning of the legs of a humanoid robot, which solves the difficulty of autonomous motion planning of the legs of a humanoid robot and overcomes the shortcoming that the traditional planning method ignores the dynamic performance of the robot, so as to improve the calculation efficiency of trajectory planning and achieve excellent dynamic operation beyond the limit. Figure 1 As shown, the method specifically includes:

[0069] Step S1: for the leg task of the legged robot kicking a ball, the task is re-expressed in the world coordinate system;

[0070] The step S1 specifically includes:

[0071] The task of a footed robot kicking a ball is considered. For this task, only the two joints of the swinging leg need to cooperate simultaneously to achieve the maximum linear velocity of the end of the foot (in the kicking task, the hip joint and the knee joint need to move at the same time, and the thigh drives the calf, and the calf hits the football, so the leg is regarded as a two-link model). Figure 2 As shown in the figure, the representation of high dynamic tasks of legged robots in the world coordinate system is:

[0072] (1)

[0073] (2)

[0074] in,

[0075] (3)

[0076] in, is the active torque of the first joint (hip joint) and the second joint (knee joint); is the coupling torque is the passive torque produced by the joint; is the moment of inertia; is the mass of the connecting rod; is the length of the connecting rod; is the length from the center of mass to the joint; are generalized coordinates; and r indicates the absolute angular position of the first joint and the second joint; represents the angular position of the second joint relative to the first joint; is the free rotation range of the second joint; and is the range of motion of the joint in the clockwise and counterclockwise directions. represents the gravitational acceleration on the swinging plane. Based on this model, we will discuss the realization of ultra-dynamic operation using anthropomorphic mechanisms.

[0077] Step S2: define the problem to be solved and derive the dynamic equations of the two-link model of the humanoid leg.

[0078] Specifically include:

[0079] Define the problem: Formulate the motion as a three-point boundary condition problem;

[0080] Initial Configuration: ;

[0081] Impact time configuration: ;

[0082] Abort configuration: ;

[0083] in, It’s the moment of impact; It’s the end moment; is the translational velocity in the x direction (impact velocity), is the translational velocity in the y direction; The target impact velocity The direction component, The target impact velocity The direction component, is the target impact position; then define the motion error variable,

[0084] (4)

[0085] Represents the joint angle error variable and the y-direction translation velocity error variable at the moment of impact; represents the joint angle error variable and joint velocity error variable when the termination condition is reached; considering the motion error and constraints, the objective function with constraints is:

[0086] (5)

[0087] in, represents the lower bound, represents the upper bound; Represents joint variables; upper and lower bounds of joint speed and the torque upper and lower bounds Given manually, the robot dynamics model is defined as:

[0088] (6)

[0089]

[0090] in, is the mass matrix; is the Coriolis force matrix; is the gravity vector; is the friction term;

[0091]

[0092] Step S3: The robot joint torque trajectory is represented by a b-spline polynomial with a time scale factor, and its physical limitations are incorporated into the formula.

[0093] The step S3 specifically includes:

[0094] The active torque trajectory is directly approximated by using B-spline curve, and then the hard constraints of the active torque are transformed into a series of linear inequality constraints of B-spline coefficients;

[0095] The fourth-order B-spline basis function is used to parameterize the initial time To the final time The active torque is defined as:

[0096] (7)

[0097] in, is the parameter vector, is the b-spline basis function, which has the following form:

[0098] (8)

[0099] Among them, the operator and The definition is as follows

[0100] (9)

[0101] (10)

[0102] B-splines have many useful properties. The most useful properties for the constrained moment trajectory generation problem are as follows:

[0103] Since any b-spline basis function and equation The convex hull property of the active torque can be transformed into a series of linear inequalities in the parameter space when the active torque is approximated as a b-spline. During optimization, only the linear search space of the parameters needs to be restricted.

[0104] Assume the maximum active torque is Then the hard constraint of the active torque can be expressed as follows:

[0105] (11)

[0106] Now the hard constraints on the active torque have become a series of linear inequality constraints on the b-spline coefficients.

[0107] Step S4: Based on the torque curve formula, the torque curve planning problem is transformed into a constrained multi-objective function optimization problem by constructing multiple objective functions.

[0108] according to The parameter constraints, total consumed work (TCW), minimizing the sum of the square derivatives of torque (SSDT) ​​and kinetic energy (KE), define the trajectory planning problem as:

[0109] (12)

[0110] in, is the total consumed work TCW, is the minimum torque SSDT, is the kinetic energy KE; and is the corresponding weight coefficient;

[0111] While satisfying the kinematic specifications and active torque hard constraints, TCW, SSDT, and KE are defined as:

[0112] (13)

[0113] Step S5: using a Knee point based evolutionary algorithm (KGEA) based on the standard dynamics model to solve the above multi-objective optimization problem, optimizing multiple objectives at the same time, and obtaining the optimal torque curve;

[0114] Knee point based evolutionary algorithm (KGEA) is used to solve multi-objective optimization problems;

[0115] like Figure 3 As shown, the overall framework of KGEA consists of the following main components:

[0116] 1) Randomly generate an initial parent population of size N;

[0117] 2) A binary competition strategy is used to select individuals from the parent population and generate N offspring individuals by mutation. In the binary competition selection, three competition indicators are used, namely, dominance relationship, knee point criterion and weighted distance measurement;

[0118] 3) Perform non-dominated sorting on the combination of parent population and offspring population, and then use an adaptive strategy to identify the solution of the knee region of each non-dominated front in the combined population;

[0119] 4) Finally, environmental selection is carried out to select N individuals as the parent group of the next generation;

[0120] This process is repeated until the termination condition is met. For example, the minimum Euclidean distance or Manhattan distance composed of multiple objective functions can be used as the termination condition, which can be expressed as argmin or argmin , where argmin Indicates Minimum, represents the Euclidean distance of the three objective functions, represents the Manhattan distance.

[0121] Step S6: Based on the existing torque curve, a closed-loop control system is designed to track the torque curve in the joint space.

[0122] The designed closed-loop control system obtains the real-time angle and angular velocity of each joint in the leg joint of the humanoid robot, and tracks the torque curve in the joint space.

[0123] An embodiment of the present invention provides a method for autonomous motion planning of humanoid robot legs. For the leg tasks of the humanoid robot during motion (such as a kicking task), the tasks are re-expressed in the world coordinate system, and then the robot joint torque trajectory is fitted using a b-spline polynomial with a time scale factor, and its physical limitations are incorporated into the formula. The torque limit is expressed as a soft constraint added to the objective function, while the position and speed limits are considered to be hard linear inequality constraints on the parameters. Subsequently, we transform the motion planning problem into a constrained multi-objective function optimization problem, and develop a Knee point-based evolutionary algorithm (KGEA), which can automatically search for a Pareto set with a quality trade-off between parameter scale and performance, where two conflicting objectives can be optimized simultaneously. The solution obtained by our algorithm extends the robot's motor performance while reducing the joint torque, and also reduces energy consumption, and has high application value.

[0124] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0125] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for autonomous motion planning of humanoid robot legs, characterized in that: include: Step S1: describe the tasks of the robot's legs when performing actions in the world coordinate system; Step S2: define the problem to be solved and derive the dynamic equations of the two-link model of the humanoid leg; Step S3: using b-spline polynomials with time scale factors to represent the robot joint torque trajectory, and incorporating its physical limitations into the formula; Step S4: Based on the torque curve formula, the torque curve planning problem is transformed into a constrained multi-objective function optimization problem by constructing multiple objective functions; Step S5: solving the multi-objective function optimization problem based on the standard dynamics model, optimizing multiple objectives at the same time, and obtaining the optimal torque curve; Step S6: Based on the existing torque curve, a closed-loop control system is designed to track the torque curve in the joint space.

2. The method for autonomous motion planning of humanoid robot legs according to claim 1, characterized in that: The step S1 comprises: When the robot is performing an action, the leg task requires the two joints of the leg to swing and cooperate to achieve the maximum linear velocity of the end of the foot. The high dynamic tasks of the legged robot are expressed in the world coordinate system: in, Where, τ1, τ2 are the active torques of the first joint hip joint and the second joint knee joint; τ 1d , τ 2d is the coupling torque τ p is the passive torque generated by the joint; J1, J2 are the moments of inertia; m1, m2 are the masses of the connecting rod; l1, l2 are the lengths of the connecting rod; l g1 , l g2 is the length from the center of mass to the joint; q1, q2 are generalized coordinates; and represents the absolute angular position of the first joint and the second joint; β 21 =q2-π represents the angular position of the second joint relative to the first joint; is the free rotation range of the second joint; and is the range of motion of the joint in the clockwise and counterclockwise directions, g y represents the acceleration due to gravity in the plane of the swing.

3. The method for autonomous motion planning of humanoid robot legs according to claim 1, characterized in that: The step S2 comprises: Define the problem: Formulate the motion as a three-point boundary condition problem; Initial configuration: q(0) = q0, Impact time configuration: q(t m )=q m , v y (t m )=v ym ; Abort configuration: q(t f )=q f , Among them, t m is the impact moment; t f is the end moment; x is the translational velocity in the x direction, v y is the translational velocity in the y direction; v xm is the x-component of the target impact velocity, v ym is the y-direction component of the target impact velocity, q m is the target impact position; then define the motion error variable, Θ m Represents the joint angle error variable and the y-direction translation velocity error variable at the moment of impact; Θ f represents the joint angle error variable and joint velocity error variable when the termination condition is reached; considering the motion error and constraints, the objective function with constraints is: Where V represents the lower bound, ^ represents the upper bound; l represents the joint variable; the upper and lower bounds of the joint velocity and the torque upper and lower bounds Given manually; the robot dynamics model is defined as: in, is the mass matrix; N(q) is the Coriolis force matrix; G(q) is the gravity vector; is the friction term.

4. The method for autonomous leg motion planning of a humanoid robot according to claim 3, characterized in that: The step S3 comprises: The active torque trajectory is directly approximated by using B-spline curve, and then the hard constraints of the active torque are transformed into a series of linear inequality constraints of B-spline coefficients; The fourth-order B-spline basis function is used to parameterize the time from the initial time t0 = 0 to the final time t f The active torque is defined as: Where p = {p -3 ,…,p i ,…,p N-1 }, p l ∈R n is the parameter vector, B j，4 (t) is the b-spline basis function, which is in the following form: Among them, the operators + and w i,4 (t) is defined as follows Assume the maximum active torque is Then the hard constraint of the active moment is expressed as follows: Now the hard constraints on the active torque have become a series of linear inequality constraints on the b-spline coefficients.

5. The method for autonomous leg motion planning of a humanoid robot according to claim 4, characterized in that: The step S4 comprises: according to The parameter constraints, total power consumption TCW, minimization of the torque SSDT and the sum of the square derivatives of the kinetic energy KE, define the trajectory planning problem as: Where f1 is the total consumed work TCW, f2 is the minimum torque SSDT, and f3 is the kinetic energy KE; ω c1 ,ω c2 and ω c3 is the corresponding weight coefficient; While satisfying the kinematic specifications and active torque hard constraints, TCW, SSDT, and KE are defined as:

6. The method for autonomous leg motion planning of a humanoid robot according to claim 1, characterized in that: The step S5 comprises: solving the multi-objective optimization problem based on the Knee point evolutionary algorithm KGEA; The overall framework of the evolutionary algorithm KGEA includes: 1) Randomly generate an initial parent population of size N; 2) A binary competition strategy is used to select individuals from the parent population and generate N offspring individuals by mutation. In the binary competition selection, three competition indicators are used, namely, dominance relationship, knee point criterion and weighted distance measurement; 3) Perform non-dominated sorting on the combination of parent population and offspring population, and then use an adaptive strategy to identify the solution of the knee region of each non-dominated front in the combined population; 4) Finally, environmental selection is carried out to select N individuals as the parent population of the next generation; This process is repeated until the preset termination condition is met.

7. The method for autonomous leg motion planning of a humanoid robot according to claim 1, characterized in that: The step S6 includes: obtaining the real-time angle and angular velocity of each joint in the leg joints of the humanoid robot through a designed closed-loop control system, and tracking the torque curve in the joint space.

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