CN118288297A - Robot motion control method, system, electronic equipment and storage medium - Google Patents
Robot motion control method, system, electronic equipment and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The application discloses a motion control method, a motion control system, electronic equipment and a storage medium for a robot, and belongs to the technical field of robot control technology. The motion control method of the robot comprises the following steps: acquiring dimension management information, and determining an indication vector corresponding to the robot according to the dimension management information; the robot is a robot which utilizes a motion executing mechanism to realize motion, the dimension management information is information for indicating that the motion dimension of the motion executing mechanism needs to be shielded or reserved, and the dimension of the indication vector corresponds to each motion dimension of the motion executing mechanism; reconstructing an optimization problem by using the indication vector; and controlling the motion executing mechanism according to the reconstructed optimization problem. The application can adaptively adjust the dimension quantity of the optimization problem and improve the calculation efficiency of the robot motion control.
Description
Technical Field
The present application relates to the field of robot control technologies, and in particular, to a method and a system for controlling motion of a robot, an electronic device, and a storage medium.
Background
Robots having a motion actuator can perform motions such as transportation and walking, and have been widely used in fields such as industry and logistics. In the related art, motion control of a robot is generally realized by solving an optimization problem, and in the mode, all possible contact states are required to be included in the optimization problem and decision variables of corresponding dimensions are set, so that the calculation complexity is high and the calculation efficiency is low.
Disclosure of Invention
The application aims to provide a motion control method, a motion control system, electronic equipment and a storage medium for a robot, which can adaptively adjust the number of dimensions of an optimization problem and improve the calculation efficiency of motion control of the robot.
In order to solve the above technical problems, the present application provides a motion control method of a robot, including:
Acquiring dimension management information, and determining an indication vector corresponding to the robot according to the dimension management information; the robot is a robot which utilizes a motion executing mechanism to realize motion, the dimension management information is information for indicating that the motion dimension of the motion executing mechanism needs to be shielded or reserved, the dimension of an indication vector corresponds to each motion dimension of the motion executing mechanism, the dimension of the indication vector comprises a first dimension and a second dimension, the first dimension corresponds to the motion dimension needing to be shielded, the second dimension corresponds to the motion dimension needing to be reserved, and the values of the first dimension and the second dimension are different;
Reconstructing an optimization problem by using the indication vector; the first dimension is not included in the reconstructed optimization problem, and the optimization problem is a track tracking optimization problem corresponding to the current task executed by the robot;
And controlling the motion executing mechanism according to the reconstructed optimization problem.
Optionally, the dimension management information includes user configuration information and/or a robot contact sequence;
The user configuration information comprises dimension shielding information and/or dimension reservation information input by a user; the robot contact sequence is a contact sequence when the robot executes the current task and is used for describing whether external environment reaction force exists in each motion dimension of the motion executing mechanism or not respectively; in the robot contact sequence, the motion dimension in which no external environment reaction force exists is the motion dimension which needs to be shielded, and the motion dimension in which the external environment reaction force exists is the motion dimension which needs to be reserved.
Optionally, if the dimension management information includes the user configuration information and the robot contact sequence, determining an indication vector corresponding to the robot according to the dimension management information includes:
Setting an alternative vector according to the robot contact sequence; wherein the dimension of the candidate vector corresponds to each motion dimension of the motion actuator;
generating dimension constraint conditions conforming to the user configuration information; wherein the dimension constraint condition is used for constraining the value of any one or any several dimensions in the indication vector;
and determining the indication vector corresponding to the robot according to the alternative vector and the dimension constraint condition.
Optionally, if the dimension management information includes the robot contact sequence, after the motion executing mechanism is controlled according to the reconstructed optimization problem, the method further includes:
recording interaction events of the motion executing mechanism and an external environment, and generating a new robot contact sequence according to the interaction events;
Judging whether the user configuration information is received or not;
If yes, updating the indication vector according to the user configuration information received last time and the new robot contact sequence;
If not, updating the indication vector according to the new robot contact sequence.
Optionally, reconstructing the optimization problem by using the indication vector includes:
generating a selection matrix according to the arrangement sequence of the first type dimension and the second type dimension in the indication vector; wherein the selection matrix is a matrix for shielding the first type of dimension and retaining the second type of dimension;
and reconstructing the optimization problem by using the selection matrix.
Optionally, the value of the first dimension is 0, and the value of the second dimension is 1;
correspondingly, generating a selection matrix according to the arrangement sequence of the first type dimension and the second type dimension in the indication vector comprises the following steps:
Constructing a square matrix according to the indication vector; wherein, the number of rows and columns of the square matrix are the number of dimensions of the indication vector;
Sequentially extracting the value of each dimension of the indication vector as a diagonal element of the square matrix, and setting the non-diagonal elements of the square matrix to 0;
and removing columns with all elements of 0 in the square matrix to obtain the selection matrix.
Optionally, reconstructing the optimization problem by using the selection matrix includes:
multiplying the selection matrix with a task matrix in the optimization problem to obtain a new task matrix;
multiplying the selection matrix with a constraint matrix in the optimization problem to obtain a new constraint matrix;
reconstructing an optimization problem based on the new task matrix and the new constraint matrix.
The application also provides a motion control system of the robot, which comprises:
The vector determining module is used for acquiring dimension management information and determining an indication vector corresponding to the robot according to the dimension management information; the robot is a robot which utilizes a motion executing mechanism to realize motion, the dimension management information is information for indicating that the motion dimension of the motion executing mechanism needs to be shielded or reserved, the dimension of an indication vector corresponds to each motion dimension of the motion executing mechanism, the dimension of the indication vector comprises a first dimension and a second dimension, the first dimension corresponds to the motion dimension needing to be shielded, the second dimension corresponds to the motion dimension needing to be reserved, and the values of the first dimension and the second dimension are different;
The problem reconstruction module is used for reconstructing the optimization problem by using the indication vector; the first dimension is not included in the reconstructed optimization problem, and the optimization problem is a track tracking optimization problem corresponding to the current task executed by the robot;
and the control module is used for controlling the motion executing mechanism according to the reconstructed optimization problem.
The present application also provides a storage medium having stored thereon a computer program which, when executed, implements the steps performed by the above-described robot motion control method.
The application also provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps executed by the motion control method of the robot when calling the computer program in the memory.
The application provides a motion control method of a robot, which comprises the steps of acquiring dimension management information and determining an indication vector corresponding to the robot according to the dimension management information, wherein the dimension of the indication vector corresponds to each motion dimension of a motion executing mechanism; the dimension management information is information for indicating that the motion dimension of the motion executing mechanism needs to be shielded or reserved, so that in an indication vector determined based on the dimension management information, the first dimension corresponds to the motion dimension needing to be shielded, and the second dimension corresponds to the motion dimension needing to be reserved; the application utilizes the indication vector to reconstruct the optimization problem so as to shield the first dimension and reserve the second dimension, thereby utilizing the reconstructed optimization problem to control the motion executing mechanism. According to the method and the device, the number of dimensions in the optimization problem can be adaptively adjusted according to the dimension management information, the calculated amount of the optimization problem is reduced, and the calculation efficiency of robot motion control is improved. The application also provides a motion control system of the robot, a storage medium and an electronic device, which have the beneficial effects and are not repeated here.
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For a clearer description of embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described, it being apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
Fig. 1 is a flowchart of a motion control method of a robot according to an embodiment of the present application;
FIG. 2 is a flowchart of a high real-time whole body control algorithm for handling multiple contact sequences of a foot robot according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a motion control system of a robot according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, fig. 1 is a flowchart of a motion control method of a robot according to an embodiment of the application.
S101: and acquiring dimension management information, and determining an indication vector corresponding to the robot according to the dimension management information.
The present embodiment may be applied to a processor of a robot that performs a motion using a motion actuator, and in particular, the robot may perform a motion using a motion actuator including a plurality of joints. The processor can realize the motion control of the robot by sending a control instruction to the motion executing mechanism.
The dimension management information is information for indicating that the movement dimension of the movement executing mechanism needs to be shielded or reserved; namely, the dimension management information prescribes the movement dimension to be shielded and the movement dimension to be reserved.
After the dimension management information is obtained, the step can determine an indication vector corresponding to the robot according to the dimension management information, wherein the dimension of the indication vector corresponds to each movement dimension of the movement executing mechanism. As a possible implementation, the total number of dimensions in the indication vector may be determined before this step according to the structural parameters of the robot, and the indication vector may include dimensions corresponding to each motion dimension of the motion actuator. As a possible embodiment, the motion actuators of the robot comprise a left lower limb, a right lower limb, a left upper limb and a right upper limb, each motion actuator having at least one motion dimension.
The dimension management information is information for indicating that the motion dimension of the motion executing mechanism needs to be shielded or reserved, so that the dimension of the indication vector determined according to the dimension management information can comprise a first dimension and a second dimension, wherein the first dimension corresponds to the motion dimension needing to be shielded, the second dimension corresponds to the motion dimension needing to be reserved, and the values of the first dimension and the second dimension are different. The indication vector may include at least 0 first class dimensions and at least 0 second class dimensions.
S102: and reconstructing the optimization problem by using the indication vector.
Before this step, there may be an operation of generating an optimization problem, and in this embodiment, the track tracking optimization problem corresponding to the current task is used as the optimization problem. Specifically, the embodiment may determine the control target and constraint condition of the current task, and may generate the track tracking optimization problem according to the control target and constraint condition. The embodiment can process the current task by using a whole body control algorithm based on quadratic programming to obtain an optimization problem. The decision variables of the optimization problem correspond to the motion dimensions of the motion actuators of the robot.
The values of the first type of dimension and the second type of dimension in the indication vector are different, so the indication vector contains information about the motion dimension to be masked and/or the motion dimension to be reserved. The optimization problem is a track tracking optimization problem corresponding to the current task executed by the robot, and the shielding or reservation of any motion dimension can be realized by reconstructing the optimization problem based on the indication vector. By the method, the reconstructed optimization problem does not comprise the first type of dimension and can comprise the second type of dimension.
S103: and controlling the motion executing mechanism according to the reconstructed optimization problem.
On the basis of obtaining the reconstructed optimization problem, the reconstructed optimization problem can be solved, and an optimal solution of the reconstructed optimization problem is determined, so that the target moment of each motion executing mechanism is determined according to the optimal solution, and the motion executing mechanism is controlled according to the target moment so as to complete the current task.
The embodiment obtains dimension management information and determines an indication vector corresponding to the robot according to the dimension management information, wherein the dimension of the indication vector corresponds to each movement dimension of the movement executing mechanism; the dimension management information is information for indicating that the motion dimension of the motion executing mechanism needs to be shielded or reserved, so that in an indication vector determined based on the dimension management information, the first dimension corresponds to the motion dimension needing to be shielded, and the second dimension corresponds to the motion dimension needing to be reserved; the embodiment utilizes the indication vector to reconstruct the optimization problem so as to shield the first dimension and reserve the second dimension, thereby utilizing the reconstructed optimization problem to control the motion executing mechanism. According to the method and the device for optimizing the motion control of the robot, the number of dimensions in the optimization problem can be adaptively adjusted according to the dimension management information, the calculated amount of the optimization problem is reduced, and the calculation efficiency of the motion control of the robot is improved.
As a further introduction to the corresponding embodiment of fig. 1, the dimension management information may include user configuration information, may include a robot contact sequence, and may include both user configuration information and a robot contact sequence.
Specifically, the user configuration information is information about dimension configuration input by a user, which may include dimension shielding information input by the user, dimension reserved information input by the user, and both dimension shielding information and dimension reserved information input by the user. The dimension shielding information is used for describing the movement dimension to be shielded, the dimension reservation information is used for describing the movement dimension reserved by the user, and the movement dimension to be shielded can be determined according to the dimension reservation information, namely: and taking other motion dimensions except the motion dimension corresponding to the dimension retention information as the motion dimension needing shielding.
Specifically, in one possible embodiment, assuming that the user selects a certain joint of the upper limb of the fixed robot not to participate in the movement of the robot, the movement dimension of the certain joint of the upper limb is the movement dimension that needs to be shielded. At this time, the user inputs information about the dimension arrangement as the user configuration information, which is the joint fixation selection information of the user.
The robot contact sequence is a contact sequence when the robot executes the current task and is used for describing whether external environment reaction force exists in each motion dimension of the motion executing mechanism or not. In the robot contact sequence, the motion dimension in which no external environment reaction force exists is the motion dimension to be shielded, and the motion dimension in which the external environment reaction force exists is the motion dimension to be reserved. The external environment refers to everything except the robot body (e.g., ground, cargo, other robots, etc.).
In particular, the present embodiment may deploy sensors (e.g., force sensors, tactile sensors, etc.) on a motion actuator of a robot, so as to sense a reaction force of an external environment to the motion actuator using the above-described sensors. The embodiment may also process (e.g., filter, threshold setting, and pattern recognition) the raw data collected by the sensor, so as to obtain a robot contact sequence. The robot contact sequence is a serialized data structure, and records whether the reaction force of the external environment exists in each motion dimension when the robot executes the current task. The robot contact sequence is presented in a time series for describing whether there is contact in the respective motion dimension at each point in time.
As a possible implementation, if the dimension management information only contains user configuration information, the indication vector may be determined by: generating dimension constraint conditions conforming to user configuration information; and determining an indication vector corresponding to the robot based on the dimension constraint condition.
Wherein the dimension constraint is used to constrain the values of any one or any number of dimensions of the indicative vector corresponding to the motion dimension in order to set the first type of dimension and/or the second type of dimension of the indicative vector. Specifically, the dimension value corresponding to the motion dimension to be masked in the indication vector is 0, and the other dimension values are 1.
As a possible implementation, if the dimension management information includes only the robot contact sequence, the indication vector corresponding to the robot may be determined according to the robot contact sequence.
By the method, all dimensions in the indication vector can be divided into the first dimension and the second dimension according to whether the reaction force of the external environment exists in a certain motion dimension, namely, the first dimension corresponds to the motion dimension in which the reaction force of the external environment does not exist, and the second dimension corresponds to the motion dimension in which the reaction force of the external environment exists. Specifically, the corresponding dimension value indicating the motion dimension in which the external environment reaction force does not exist in the vector is 0, and the corresponding dimension value indicating the motion dimension in which the external environment reaction force exists is 1.
As a possible implementation manner, if the dimension management information includes user configuration information and a robot contact sequence, the process of determining the indication vector corresponding to the robot according to the dimension management information includes the following steps:
step A1: the candidate vectors are set according to the robot contact sequence.
The dimension of the candidate vector corresponds to each motion dimension of the motion executing mechanism, the value of each dimension in the candidate vector is determined according to the robot contact sequence, specifically, the corresponding dimension value of the motion dimension of the candidate vector without external environment reaction force is 0, and the corresponding dimension value of the motion dimension with external environment reaction force is 1.
Step A2: and generating dimension constraint conditions conforming to the user configuration information.
Wherein the dimension constraint is used to constrain a value indicative of any one or any number of dimensions of the vector.
Step A3: and determining an indication vector corresponding to the robot according to the alternative vector and the dimension constraint condition.
In actual implementation, whether the candidate vector accords with the dimension constraint condition can be judged; if yes, setting the alternative vector as an indication vector corresponding to the robot; if not, correcting the candidate vector according to the dimension constraint condition, and setting the corrected candidate vector as an indication vector corresponding to the robot, wherein the corrected candidate vector accords with the dimension constraint condition.
Assuming that the candidate vectors set according to the robot contact sequence are [0,0,1,1,0,1,1,1,0,1,1,0,0,1,1,0,1,1], wherein the first dimension to the third dimension correspond to the motion dimension of the left upper limb of the robot, the fourth dimension to the sixth dimension correspond to the motion dimension of the right upper limb of the robot, the seventh dimension to the twelfth dimension correspond to the motion dimension of the left lower limb of the robot, and the thirteenth dimension to the eighteenth dimension correspond to the motion dimension of the right lower limb of the robot. If the user selects all joints of the left upper limb and the right upper limb of the fixed robot, all motion dimensions of the left upper limb and the right upper limb are motion dimensions needing shielding, and dimensional constraint conditions generated based on user configuration information are as follows: the values of the first dimension to the sixth dimension are 0. Based on the dimensional constraint, the candidate vector is modified, and the indication vector is determined to be [0,0,0,0,0,0,1,1,0,1,1,0,0,1,1,0,1,1].
As a possible implementation manner, if the dimension management information includes a robot contact sequence, after the motion execution mechanism is controlled according to the reconstructed optimization problem, an interaction event between the motion execution mechanism and an external environment can be recorded; the interaction events are ordered in order of events to obtain a new robot contact sequence, so that the indication vector is reset according to the new robot contact sequence. Specifically, the present embodiment may update the indication vector by: recording interaction events of the motion executing mechanism and an external environment, and generating a new robot contact sequence according to the interaction events; judging whether user configuration information is received or not; if yes, updating the indication vector according to the latest received user configuration information and a new robot contact sequence; if not, updating the indication vector according to the new robot contact sequence.
According to the mode, the indication vector can be dynamically updated according to the actual interaction condition of the robot and the external environment, and the calculation efficiency of the robot motion control is improved.
As a possible implementation, after the motion execution mechanism is controlled according to the reconstructed optimization problem, if the user configuration information is received, the indication vector may be updated according to the user configuration information received last time. By the mode, the corresponding motion dimension can be shielded according to the user requirement, and the flexibility of the motion control of the robot is improved.
As a further introduction to the corresponding embodiment of fig. 1, the optimization problem may be reconstructed by: generating a selection matrix according to the arrangement sequence of the first type dimension and the second type dimension in the indication vector; and reconstructing the optimization problem by using the selection matrix. The selection matrix is a matrix for shielding the first dimension and retaining the second dimension.
Specifically, in this embodiment, a selection matrix may be used to remove a portion corresponding to the first dimension and reserve a portion corresponding to the second dimension in the optimization problem, so as to obtain a new optimization problem. The process uses the selection matrix to reconstruct the optimization problem, and a new optimization problem is obtained. By the operation of the selection matrix, the adaptive adjustment of the dimension of the optimization problem can be realized. The new optimization problem is smaller in scale and more close to the actual control requirement.
The present embodiment may set the value of the first class dimension to 0 and the value of the second class dimension to 1. On this basis, the selection matrix may be generated by:
step B1: and constructing a square matrix according to the indication vector.
The number of rows and columns of the square matrix are the number of dimensions of the indication vector. The indication vector contains an element vector corresponding to each motion dimension of the motion actuator. Each element value is 1 or 0, respectively indicating that a reservation or a mask is required in the corresponding dimension. The square matrix is a matrix with equal number of rows and columns, and the number of dimensions is consistent with the number of dimensions of the indication vector.
Step B2: the values indicating each dimension of the vector are sequentially extracted as diagonal elements of the square matrix, and the non-diagonal elements of the square matrix are set to 0.
The square matrix can be made into a diagonal matrix in the mode, and elements on the diagonal line correspond to elements in the indication vector one by one.
Step B3: and removing columns with all elements of 0 in the square matrix to obtain a selection matrix.
Since the dimension indicating a value of 0 in the vector indicates that masking is required, the dimension of a value of 1 indicates that retention is required. By removing the columns with all 0, the obtained selection matrix is a screened matrix, and only the key dimension interacted with the external environment is reserved in the selection matrix, so that the self-adaptive adjustment of the dimension of the optimization problem is realized.
The process can simplify the original optimization problem into a new optimization problem only considering key dimensions, so that the calculation efficiency and the control precision are improved.
Based on the above manner of constructing the selection matrix, before the optimization problem is reconstructed by using the selection matrix, the method further comprises the step of processing the current task by using a whole body control (WBC, whole Body Control) algorithm optimized based on quadratic programming (QP, quadratic Programming) to obtain the optimization problem. The optimization problem obtained in the above manner includes a task matrix and a constraint matrix, so that the optimization problem can be reconstructed by the following method: multiplying the selection matrix with a task matrix in the optimization problem to obtain a new task matrix; multiplying the selection matrix with a constraint matrix in the optimization problem to obtain a new constraint matrix; the optimization problem is reconstructed based on the new task matrix and the new constraint matrix.
By the method, key motion dimensions can be effectively screened out, the computational complexity is reduced, and the optimization efficiency is improved. The novel optimization problem obtained by adopting the mode is closer to reality, and the accuracy and stability of robot control can be improved.
The flow described in the above embodiment is explained below by way of an embodiment in practical application.
There are generally two implementations of Whole Body Control (WBC) algorithms, one is a zero-space projection method and the other is an optimization-based method. The zero-space projection method has strict task priorities, but such strict priorities may be too "hard" for foot robots, especially humanoid robots, because such strict priorities are not required, although there are priorities between different tasks. Task priority is achieved by adjusting task weights based on optimized WBCs, which is relatively a way to achieve task priority "softer".
Current quadratic programming-based whole body control algorithms (i.e., quadratic programming optimization-based whole body control algorithms, QP-WBCs) typically include all possible contact states and set decision variables of corresponding dimensions when handling foot robot multi-contact sequences. The related art needs to take all possible contact situations into consideration, so as to construct a complete optimization problem with higher dimension, and under different contact sequences, decision variables of corresponding dimensions are usually set to zero to realize normal working logic. Taking a humanoid robot as an example, the force rotation when the foot is in contact with the ground is 6-dimensional, so that the corresponding decision variable is 6-dimensional when the foot is supported by a single foot, and the corresponding force rotation when the foot is supported by two feet is 12-dimensional. For working states such as a single-foot support, there are cases where a decision variable of a certain dimension is not effective, but the related technology usually sets the decision variable to zero, so that the dimension of the decision variable of the whole optimization problem is not reduced, which means that the calculation time of the optimization problem is not reduced. In addition, when the robot is in a certain working state, the robot is in additional physical contact with the environment, and accordingly, the force rotation at the contact position is required to be newly added into the optimization problem, and the current quadratic programming-based whole-body control algorithm cannot well cope with the requirement. As can be seen, the quadratic programming-based whole body control algorithm in the related art lacks a mechanism for on-line reduction and increase of the decision variable dimension in response to the multi-contact sequence of the foot robot.
Aiming at the technical problems in the related art, the application provides a high real-time whole body control scheme for coping with the multi-contact sequence of the foot robot, which can greatly reduce the solving time of the optimization problem and finally improve the real-time performance of a whole body control algorithm of the secondary planning.
In order to make the whole body control algorithm have better real-time performance, the embodiment adopts a whole body control method based on weighted quadratic programming. The following describes a quadratic programming-based whole body control algorithm by taking a foot robot as an example, which serves as a technical basis of the scheme.
Foot robots are typically floating-based systems, with configuration vectors for foot robotsBy generalized coordinatesA representation; wherein T represents a transpose;， Indicating the position and orientation coordinates of the floating base, The dimensions are represented as such,A representation field;， Is that A description of the coordinates of the joints that can be driven,Representing the degree of freedom of the floating base and the degree of freedom of the joint, the general dimension of the generalized coordinates. Definition of generalized space velocityGeneralized spatial acceleration，Representation ofIs used as a first derivative of (a),Representation ofIs used as a first derivative of (a),Representation ofIs used for the first derivative of (c),Representation ofIs a second derivative of (c). When the robot is in the in-situ standing state and the left and right upper limbs of the robot are in direct physical contact with the external environment, the whole body dynamics of the humanoid robot can be fully described by the following dynamics equation: Formula (1);
Wherein, Is a generalized mass matrix of the type,Is a nonlinear term comprising coriolis force, centripetal force and gravitational force,Representing the output torque of the drive joint; And The reaction force spin of the ground to the sole of the left and right lower limbs of the robot respectively (typically comprising three-dimensional forces and three-dimensional moments,Represents the dimension number of the left lower limb,Represents the dimension number of the right lower limb,），AndIs a contact jacobian matrix of a left lower limb and a right lower limb of the robot; also, the process of the present invention is,AndThe reaction force rotation of the external environment to the left upper limb and the right upper limb of the robot are respectively,Represents the dimension number of the left upper limb,Represents the dimension number of the right upper limb,AndThe contact jacobian matrices of the left upper limb and the right upper limb of the robot are respectively.
In order to accomplish the relevant task, the goal of the whole body control algorithm is to have the robot track the desired task trajectory. It is often more convenient to characterize the desired trajectory in the task (or operation) space than to characterize the desired trajectory in the joint space. Task space velocityAnd generalized space velocityThe following relationship is followed:
Formula (2);
representing a jacobian matrix defining a reference trajectory required to accomplish a particular task as The desired acceleration can be obtained using a feedback PD controller：
Formula (3);
Wherein the proportional gain Differential gainIs a diagonal feedback matrix. For different tasks i, corresponding to different task jacobiansA different desired task trajectory is required,The actual trajectory is represented by a representation of the actual trajectory,A reference trajectory is represented and a reference trajectory is represented,Representation ofIs used as a first derivative of (a),Representation ofIs used for the first derivative of (c),Representation ofThe controller should accomplish each task as much as possible, even if the following is minimized:
Formula (4);
the actual acceleration corresponding to task i is indicated, Indicating the desired acceleration corresponding to task i,Indicating the proportional gain corresponding to the ith task,Representing the differential gain corresponding to the ith task,Representing the reference track corresponding to the ith task,Representing the actual trajectory corresponding to the ith task,Representation ofIs used as a first derivative of (a),Representation ofIs used as a first derivative of (a),Representation ofIs used for the first derivative of (c),Representing the generalized spatial acceleration of the vehicle,Representing the jacobian matrix corresponding to task i,Representation ofIs a first derivative of (a).
From the formula (1), it is known that the ground reaction force applied to the lower limb and the external environment reaction force applied to the upper limb directly affect the generalized accelerationThus, in addition to generalized spatial accelerationT represents transposition, and ground reaction force and external environment reaction force applied by the robot are also included into the decision variableThe method comprises the following steps:
Formula (5);
wherein R represents a value range, and the dimension of a decision variable of a whole body control algorithm The following are provided:
Formula (6);
the task trajectory tracking can be converted into an optimization problem, namely:
Formula (7);
Wherein, Is the task matrix of the i-th task,Is the task vector of the i-th task,A task weight matrix for the ith task. Wherein the method comprises the steps ofIs the task dimension of the i-th task,Is the number of tasks.Is a constraint matrix that,AndIs the upper and lower boundary of the constraint, whereinIs the constraint dimension of the j-th constraint,Is the number of constraints.
The optimal solution under the constraint condition can be obtained by solving the above quadratic programming problem(I.e., a solution that minimizes the cost function if the constraint is satisfied); by combining the whole body dynamics equation, the joint moment required by the task can be finally calculated：
Formula (8);
And The generalized joint position and velocity feedback provided by the state estimation, respectively. M () represents a generalized quality matrixRepresenting the contact jacobian matrix for the left lower limb,Representing the contact jacobian matrix for the right lower limb,Representing the contact jacobian matrix for the upper left limb,Representing the contact jacobian matrix for the upper right limb, h () represents a nonlinear term comprising coriolis force, centripetal force and gravitational force.
The above is a complete framework and flow of the whole body control algorithm, and all tasks to be completed are as followsIn the form of (a) is expressed in an objective function by setting a proper task weight matrixTo achieve relative priorities of tasks; and the physical constraints imposed on the robot are considered as much as possible in the task completion process, and all the imposed constraints are expressed asForm (if)Then naturally expressed as an equality constraint).
In practical application, the user can set corresponding tasks and constraints according to specific requirements. The foot robot is used for realizing the walking function on the flat ground, and the tasks which can be selected from high to low according to the priority are as follows: a sole non-slip task, a body posture and height control task, a robot overall angular momentum task, a swing leg foot end track tracking task, a sole force variation minimizing task and the like; the constraints satisfied may be set as: floating base dynamics equality constraint, friction cone and plantar force upper and lower limit constraint, joint rotation speed saturation inequality constraint, joint output torque saturation inequality constraint and the like.
From equation (6), the decision variables of the previous whole body control algorithm are knownDimension of (2)Is typically fixed and does not change adaptively as the robot contact sequence changes. Taking a foot robot as an example, the feet are alternately contacted with the ground, the states of left foot supporting and right foot swinging, right foot supporting and left foot swinging, left foot and right foot supporting and the like exist, and when the left foot or the right foot is in a swinging period, the ground does not have reaction force. Similarly, when the left and right upper limbs are not in direct physical contact with the environment, the corresponding environmental reaction forces will also be absent. Existing whole body control algorithms, to facilitate processing, typically set the decision variable for which no reaction force exists to zero and do not remove it from the decision variable (where the decision variable still remains the same dimension, the optimization problem will also remain the same scale). Because the dimension of the decision variable is changed after the dimension is removed from the decision variable, the corresponding task matrixAnd task vectorAlso, adaptive changes should be made, while corresponding constraint matricesUpper boundary of constraintAnd lower boundaryAdaptation should also be made. However, at present, no mechanism for effectively processing the above needs exists, so that only the decision variable which does not exist in the corresponding physical sense in the decision variables can be set to zero, and the mode can work normally, but the scale of the optimization problem cannot be changed adaptively along with the different contact sequences of the robots, so that the waste of calculation resources is caused.
Referring to fig. 2, fig. 2 is a flowchart of a high real-time whole body control algorithm for handling a multi-contact sequence of a foot robot according to an embodiment of the application, which includes the following steps:
Step 1: an indication vector is constructed.
The present embodiment may set a value of each dimension in the indication vector based on the dimension mask information and the robot contact sequence determination result.
The above-mentioned indication vector IndexVector is constructed as follows:
Formula (9);
Wherein the method comprises the steps of As a decision variableIs the m-th dimension of (2)Is used as an indicator variable of (a),，Indicating decision variablesIs the m-th dimension of (2)Will continue to remain in the decision variable,Indicating decision variablesIs the m-th dimension of (2)Will be removed from the decision variables.
The indication variable can be determined according to the judgment of the robot contact sequence (such as whether the four limbs are subjected to the reaction force of the external environment) or can be shielded according to the selection of the user (for example, if a certain joint of the upper limb needs to be fixed, the indication vector value of the corresponding dimension can be set to zero). Taking walking of the robot as an example, the left foot and the right foot alternately collide (or contact) with the ground when the robot is in the robot, and the alternate collision is a robot contact sequence from the time dimension.
Step 2: a selection matrix is generated.
After the indicator vector IndexVector is constructed, a selection matrix is generated by the following method: first, a square matrix is constructedThe diagonal lines of which are generated in sequence by the indication vector IndexVector and all other off-diagonal elements are zero, i.eDiagonal represents a diagonal line, and will againThe selection matrix S is obtained by removing columns with zero for all elements in the matrix. As can be seen from the above construction, the number of columns of the matrix S is selected to be the number of all non-zero elements in the indication vector, i.eThe number of rows of the selection matrix S isThat is to say；
Step 3: reconstruction optimization problem.
After the selection matrix is generated, the optimization problem shown in the formula (7) can be reconstructed according to the selection matrix S, and the reconstructed task matrix isThe reconstructed constraint matrix isThe other task vectors, task weights, and upper and lower bound vectors of the constraints remain unchanged.
Step 4: and solving the optimization problem, controlling the movement of the robot according to the optimal solution, and updating the robot contact sequence.
The problem of optimization after reconstruction in this embodiment is as follows:
Formula (10);
Dimension of optimization problem For most scenarios, this can greatly reduce the dimensionality of the decision variables, i.e., effectively reduce the computational effort of solving the optimization problem.Representing the reconstructed decision variables.
In practical applications, the present embodiment may flexibly add or remove relevant decision variables in the optimization problem adaptively according to the robot contact sequence. The embodiment can add and remove decision variables on line and reconstruct optimization problems, so that the scale of the optimization problems can be adaptively changed according to different contact sequences, thereby removing the decision variables which do not play a role from the optimization problems, finally reducing the calculated amount, improving the real-time performance of whole body control and avoiding the waste of calculation resources.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a motion control system of a robot according to an embodiment of the present application, where the system may include:
The vector determining module 301 is configured to obtain dimension management information, and determine an indication vector corresponding to the robot according to the dimension management information; the robot is a robot which utilizes a motion executing mechanism to realize motion, the dimension management information is information for indicating that the motion dimension of the motion executing mechanism needs to be shielded or reserved, the dimension of an indication vector corresponds to each motion dimension of the motion executing mechanism, the dimension of the indication vector comprises a first dimension and a second dimension, the first dimension corresponds to the motion dimension needing to be shielded, the second dimension corresponds to the motion dimension needing to be reserved, and the values of the first dimension and the second dimension are different;
A problem reconstruction module 302, configured to reconstruct an optimization problem using the indication vector; the method comprises the steps that the reconstructed optimization problem does not comprise a first dimension, and the optimization problem is a track tracking optimization problem corresponding to a current task executed by a robot;
And the control module 303 is used for controlling the motion executing mechanism according to the reconstructed optimization problem.
Further, the dimension management information comprises user configuration information and/or a robot contact sequence;
The user configuration information comprises dimension shielding information and/or dimension reservation information input by a user; the robot contact sequence is a contact sequence when the robot executes the current task and is used for describing whether external environment reaction force exists in each motion dimension of the motion executing mechanism or not respectively; in the robot contact sequence, the motion dimension in which no external environment reaction force exists is the motion dimension to be shielded, and the motion dimension in which the external environment reaction force exists is the motion dimension to be reserved.
Further, if the dimension management information includes user configuration information and a robot contact sequence, the process of determining, by the vector determining module 301, an indication vector corresponding to the robot according to the dimension management information includes: setting an alternative vector according to the robot contact sequence; wherein the dimension of the candidate vector corresponds to each motion dimension of the motion actuator; generating dimension constraint conditions conforming to user configuration information; wherein the dimension constraint is used to constrain values indicative of any one or more dimensions of the vector; and determining an indication vector corresponding to the robot according to the alternative vector and the dimension constraint condition.
Further, if the dimension management information includes a robot contact sequence, the system may further include:
The vector updating module is used for recording interaction events between the motion executing mechanism and the external environment after the motion executing mechanism is controlled according to the reconstructed optimization problem, and generating a new robot contact sequence according to the interaction events; and is also used for judging whether the user configuration information is received; if yes, updating the indication vector according to the latest received user configuration information and a new robot contact sequence; if not, updating the indication vector according to the new robot contact sequence.
Further, the process of the problem reconstruction module 302 reconstructing the optimization problem by using the indication vector includes: generating a selection matrix according to the arrangement sequence of the first type dimension and the second type dimension in the indication vector; the method comprises the steps of selecting a matrix to be used for shielding a first dimension and reserving a second dimension; and reconstructing the optimization problem by using the selection matrix.
Further, the first dimension has a value of 0 and the second dimension has a value of 1;
Accordingly, the process of generating the selection matrix by the problem reconstruction module 302 according to the arrangement order of the first class dimension and the second class dimension in the indication vector includes: constructing a square matrix according to the indication vector; wherein, the number of rows and columns of the square matrix are the number of the dimension of the indication vector; sequentially extracting the value of each dimension of the indicating vector as a diagonal element of the square matrix, and setting the non-diagonal elements of the square matrix to 0; and removing columns with all elements of 0 in the square matrix to obtain a selection matrix.
Further, the process of the problem reconstruction module 302 reconstructing the optimization problem by using the selection matrix includes: multiplying the selection matrix with a task matrix in the optimization problem to obtain a new task matrix; multiplying the selection matrix with a constraint matrix in the optimization problem to obtain a new constraint matrix; the optimization problem is reconstructed based on the new task matrix and the new constraint matrix.
Since the embodiments of the system portion and the embodiments of the method portion correspond to each other, the embodiments of the system portion refer to the description of the embodiments of the method portion, which is not repeated herein.
The present application also provides a storage medium having stored thereon a computer program which, when executed, performs the steps provided by the above embodiments. The storage medium may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The application also provides an electronic device, which can comprise a memory and a processor, wherein the memory stores a computer program, and the processor can realize the steps provided by the embodiment when calling the computer program in the memory. Of course, the electronic device may also include various network interfaces, power supplies, and the like.
In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section. It should be noted that it will be apparent to those skilled in the art that the present application may be modified and practiced without departing from the spirit of the present application.
It should also be noted that in this specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method of controlling movement of a robot, comprising:
Acquiring dimension management information, and determining an indication vector corresponding to the robot according to the dimension management information; the robot is a robot which utilizes a motion executing mechanism to realize motion, the dimension management information is information for indicating that the motion dimension of the motion executing mechanism needs to be shielded or reserved, the dimension of an indication vector corresponds to each motion dimension of the motion executing mechanism, the dimension of the indication vector comprises a first dimension and a second dimension, the first dimension corresponds to the motion dimension needing to be shielded, the second dimension corresponds to the motion dimension needing to be reserved, and the values of the first dimension and the second dimension are different;
Reconstructing an optimization problem by using the indication vector; the first dimension is not included in the reconstructed optimization problem, and the optimization problem is a track tracking optimization problem corresponding to the current task executed by the robot;
And controlling the motion executing mechanism according to the reconstructed optimization problem.
2. The method of claim 1, wherein the dimension management information comprises user configuration information and/or a robot contact sequence;
The user configuration information comprises dimension shielding information and/or dimension reservation information input by a user; the robot contact sequence is a contact sequence when the robot executes the current task and is used for describing whether external environment reaction force exists in each motion dimension of the motion executing mechanism or not respectively; in the robot contact sequence, the motion dimension in which no external environment reaction force exists is the motion dimension which needs to be shielded, and the motion dimension in which the external environment reaction force exists is the motion dimension which needs to be reserved.
3. The method according to claim 2, wherein if the dimension management information includes the user configuration information and the robot contact sequence, determining an indication vector corresponding to the robot according to the dimension management information includes:
Setting an alternative vector according to the robot contact sequence; wherein the dimension of the candidate vector corresponds to each motion dimension of the motion actuator;
generating dimension constraint conditions conforming to the user configuration information; wherein the dimension constraint condition is used for constraining the value of any one or any several dimensions in the indication vector;
and determining the indication vector corresponding to the robot according to the alternative vector and the dimension constraint condition.
4. The method according to claim 2, further comprising, after controlling the motion actuator according to the reconstructed optimization problem, if the dimension management information includes the robot contact sequence:
recording interaction events of the motion executing mechanism and an external environment, and generating a new robot contact sequence according to the interaction events;
Judging whether the user configuration information is received or not;
If yes, updating the indication vector according to the user configuration information received last time and the new robot contact sequence;
If not, updating the indication vector according to the new robot contact sequence.
5. The method of motion control of a robot according to any one of claims 1 to 4, wherein reconstructing an optimization problem using the indication vector comprises:
generating a selection matrix according to the arrangement sequence of the first type dimension and the second type dimension in the indication vector; wherein the selection matrix is a matrix for shielding the first type of dimension and retaining the second type of dimension;
and reconstructing the optimization problem by using the selection matrix.
6. The method of claim 5, wherein the first dimension has a value of 0 and the second dimension has a value of 1;
correspondingly, generating a selection matrix according to the arrangement sequence of the first type dimension and the second type dimension in the indication vector comprises the following steps:
Constructing a square matrix according to the indication vector; wherein, the number of rows and columns of the square matrix are the number of dimensions of the indication vector;
Sequentially extracting the value of each dimension of the indication vector as a diagonal element of the square matrix, and setting the non-diagonal elements of the square matrix to 0;
and removing columns with all elements of 0 in the square matrix to obtain the selection matrix.
7. The method of claim 6, wherein reconstructing the optimization problem using the selection matrix comprises:
multiplying the selection matrix with a task matrix in the optimization problem to obtain a new task matrix;
multiplying the selection matrix with a constraint matrix in the optimization problem to obtain a new constraint matrix;
reconstructing an optimization problem based on the new task matrix and the new constraint matrix.
8. A motion control system for a robot, comprising:
The vector determining module is used for acquiring dimension management information and determining an indication vector corresponding to the robot according to the dimension management information; the robot is a robot which utilizes a motion executing mechanism to realize motion, the dimension management information is information for indicating that the motion dimension of the motion executing mechanism needs to be shielded or reserved, the dimension of an indication vector corresponds to each motion dimension of the motion executing mechanism, the dimension of the indication vector comprises a first dimension and a second dimension, the first dimension corresponds to the motion dimension needing to be shielded, the second dimension corresponds to the motion dimension needing to be reserved, and the values of the first dimension and the second dimension are different;
The problem reconstruction module is used for reconstructing the optimization problem by using the indication vector; the first dimension is not included in the reconstructed optimization problem, and the optimization problem is a track tracking optimization problem corresponding to the current task executed by the robot;
and the control module is used for controlling the motion executing mechanism according to the reconstructed optimization problem.
9. An electronic device comprising a memory and a processor, the memory having stored therein a computer program, the processor, when calling the computer program in the memory, implementing the steps of the method for controlling movement of a robot according to any one of claims 1 to 7.
10. A storage medium having stored therein computer executable instructions which, when loaded and executed by a processor, implement the steps of the method of controlling movement of a robot as claimed in any one of claims 1 to 7.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6317651B1 (en) * | 1999-03-26 | 2001-11-13 | Kuka Development Laboratories, Inc. | Trajectory generation system |
| US20140052151A1 (en) * | 1999-09-17 | 2014-02-20 | Intuitive Surgical Operations, Inc. | Phantom degrees of freedom for manipulating the movement of surgical systems |
| CN108289719A (en) * | 2016-01-20 | 2018-07-17 | 直观外科手术操作公司 | Quickly pause and recovery Medical Devices can reposition the system and method that the movement in arm deviates |
| CN109086706A (en) * | 2018-07-24 | 2018-12-25 | 西北工业大学 | Applied to the action identification method based on segmentation manikin in man-machine collaboration |
| CN113070880A (en) * | 2021-03-31 | 2021-07-06 | 深圳市优必选科技股份有限公司 | Motion control method and device, robot control equipment and readable storage medium |
| CN113386128A (en) * | 2021-05-11 | 2021-09-14 | 华南理工大学 | Body potential interaction method for multi-degree-of-freedom robot |
| CN116460841A (en) * | 2023-02-28 | 2023-07-21 | 之江实验室 | A humanoid robot non-grasping mobile push task control method and system |
| CN116999178A (en) * | 2023-10-07 | 2023-11-07 | 北京科鹏医疗器械有限公司 | Dual-frequency filtering visual master-slave mapping method operated by natural channel endoscope |
| CN117733863A (en) * | 2023-12-29 | 2024-03-22 | 科大讯飞股份有限公司 | Robot motion control method, device, equipment, robot and storage medium |
-
2024
- 2024-06-06 CN CN202410726980.7A patent/CN118288297B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6317651B1 (en) * | 1999-03-26 | 2001-11-13 | Kuka Development Laboratories, Inc. | Trajectory generation system |
| US20140052151A1 (en) * | 1999-09-17 | 2014-02-20 | Intuitive Surgical Operations, Inc. | Phantom degrees of freedom for manipulating the movement of surgical systems |
| CN108289719A (en) * | 2016-01-20 | 2018-07-17 | 直观外科手术操作公司 | Quickly pause and recovery Medical Devices can reposition the system and method that the movement in arm deviates |
| CN109086706A (en) * | 2018-07-24 | 2018-12-25 | 西北工业大学 | Applied to the action identification method based on segmentation manikin in man-machine collaboration |
| CN113070880A (en) * | 2021-03-31 | 2021-07-06 | 深圳市优必选科技股份有限公司 | Motion control method and device, robot control equipment and readable storage medium |
| CN113386128A (en) * | 2021-05-11 | 2021-09-14 | 华南理工大学 | Body potential interaction method for multi-degree-of-freedom robot |
| CN116460841A (en) * | 2023-02-28 | 2023-07-21 | 之江实验室 | A humanoid robot non-grasping mobile push task control method and system |
| CN116999178A (en) * | 2023-10-07 | 2023-11-07 | 北京科鹏医疗器械有限公司 | Dual-frequency filtering visual master-slave mapping method operated by natural channel endoscope |
| CN117733863A (en) * | 2023-12-29 | 2024-03-22 | 科大讯飞股份有限公司 | Robot motion control method, device, equipment, robot and storage medium |
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