Macro-micro mechanical arm task planning method

Abstract

The application provides a macro-micro mechanical arm time-sharing task planning method, comprising the following steps: constructing a macro-micro mechanical arm task representation according to the constraints between operation steps in an on-orbit task and the macro-micro mechanical arm action; constructing a constraint equation of the macro-micro mechanical arm according to the spatial connection relationship and motion characteristics of the macro-micro mechanical arm; constructing a macro-mechanical action similarity index according to the time-sharing motion mode of the macro-micro mechanical arm; constructing a macro-mechanical action and work point matching index according to the connection relationship and working mode of the macro-micro mechanical arm; and designing a macro-micro mechanical arm action generation method according to the macro-micro mechanical arm task representation model, the constraint equation of the macro-micro mechanical arm, the macro-mechanical action and work point matching index and the macro-micro mechanical action similarity index. According to the technical scheme provided by the embodiment of the application, a feasible, few-step and short-time macro-micro mechanical arm action sequence is generated, so that the macro-micro mechanical arm can meet the future on-orbit tasks with the characteristics of large span and complex process.

Description

[Technical Field]

[0001] This invention belongs to the field of robotic arm task planning, and relates to a macro-micro robotic arm task planning method. [Background Technology]

[0002] As a crucial on-orbit servicing platform, the space station can effectively extend the on-orbit lifespan of satellites by refueling and replacing satellite components, thereby reducing the cost of space exploration. These on-orbit missions targeting satellites are characterized by long operating distances and high precision. Most existing space stations deploy two types of robotic arms to handle different on-orbit tasks—large robotic arms represented by Canadarm2 and dexterous robotic arms represented by Dexter. However, existing space robotic arms cannot independently handle long-distance, precision on-orbit tasks. The emergence of macro- and micro-robotic arms provides a feasible technical solution for achieving these tasks, expanding the on-orbit servicing capabilities of the space station.

[0003] The macro-micro robotic arm consists of two different robotic arms connected in series. The macro robotic arm has a high load-bearing capacity and a large working range, primarily responsible for the large-scale transfer of the micro robotic arm. The micro robotic arm features fast response speed and high operational precision, making it the main executor of on-orbit tasks. The macro-micro robotic arm employs two motion modes: time-sharing motion and coordinated motion. The time-sharing motion control method is relatively simple, involves fewer joint movements, requires less energy, and has higher stability; while the coordinated motion mode offers greater flexibility, but its motion control algorithm is more complex. Currently, the macro-micro robotic arm mainly completes on-orbit tasks using time-sharing motion.

[0004] However, each transition between the macro and micro robotic arms requires additional time for starting, stopping, and vibration suppression, significantly extending the task execution time of both macro and micro robotic arms. Furthermore, various temporal constraints in on-orbit missions increase the difficulty of autonomous planning for space robots, making it difficult to directly apply existing task planning methods to macro-micro robotic arm systems to handle diverse on-orbit tasks. [Summary of the Invention]

[0005] In view of this, the present invention provides a task planning method for robotic arms, which is used to plan tasks for macro and micro robotic arms.

[0006] This invention provides a robotic arm task planning method, comprising:

[0007] Based on the constraint information between operation steps in the on-orbit mission and the motion data of the macro and micro manipulators, a macro and micro manipulator mission representation model is constructed; the macro and micro manipulators include a macro manipulator and a micro manipulator.

[0008] Based on the connection relationships and motion characteristics of the macro and micro robotic arms, constraint equations for the macro and micro robotic arms are constructed.

[0009] Based on the motion data of the macro-robotic arm, a similarity index for macro-robotic motion is constructed;

[0010] Based on the connection relationship and working mode of macro and micro robotic arms, a matching index between macro robotic motion and work point is constructed.

[0011] Based on the macro-micro robotic arm task representation model, the constraint equations of the macro-micro robotic arm, the macro-mechanical motion and work point matching index, and the similarity index of the macro-micro robotic motion, the macro-micro robotic arm is task-planned.

[0012] The above method constructs a macro-micro robotic arm task representation model based on the constraint information between operation steps in the on-orbit mission and the macro-micro robotic arm motion data, including:

[0013] Based on the constraint information between operation steps in the on-orbit mission and the motion data of the macro- and micro-manipulators, a macro- and micro-manipulator mission representation model of the following form is constructed for an on-orbit mission with n operation points and macro- and micro-manipulator motions:

[0014] T = {t1,…,t} n}

[0015] t i ={PE i C i Visited i}

[0016] A={A b A s}

[0017]

[0018]

[0019] Where T represents the task to be performed, t i Let PE represent the i-th job point in task T. i The pose information representing the task consists of a six-dimensional vector, C i This represents the priority constraint of operations, describing the order of operations between tasks in terms of numerical value. (Visited) i Indicates whether the status has been visited, Visited i =1 indicates that the work point has been planned. Each movement of the robotic arm is considered an action, and it is assumed that the micro-robotic arm will automatically begin executing the operation after reaching the work point. A b and A s These represent the macro-manipulator motion sequence and the micro-manipulator motion sequence, respectively. This represents the i-th macro robotic arm movement. This indicates the j-th macro-robotic arm action, which is executed after the i-th macro-robotic arm action.

[0020] In the above method, based on the connection relationships and motion characteristics of the macro-micro robotic arms, constraint equations for the macro-micro robotic arms are constructed, including:

[0021] Based on the connection relationship and motion characteristics of the spatial macro-micro manipulator, and considering factors such as the dynamic performance of the manipulator, the connection relationship between the two arms, and whether the micro manipulator can perform tasks, a series of constraint equations for the macro-micro manipulator were constructed, as follows:

[0022] -π≤q ma_i ≤π, i=1,2,...,7

[0023] -π≤q mi_j ≤π,j=1,2,...,7

[0024] a b ∈W ma

[0025] a s ∈W mi

[0026] PE i -a b ∈W mi

[0027] Where, q ma_i Let q be the joint angle of the macro robotic arm. ma_i For the joint angle of the micro-manipulator, to prevent the joints of the macro-micromanipulator system from exceeding the limits, a b ∈W ma and a s ∈W mi The two constraint equations represent the motion of the macromanipulator and a, respectively. b and micro robotic arm movements a s In their respective workspaces W ma and W mi Inside. PE i -a b ∈W mi The task constraint for the work point is that the distance between the work point and the macro robotic arm's movement should be within the workspace of the micro robotic arm.

[0028] In the above method, a similarity index for macro-mechanical movements is constructed based on the motion data of macro and micro robotic arms, including:

[0029] Based on the motion data of the macro-robotic arm, in order to accurately and quantitatively describe the similarity between macro-robotic arm movements, and taking into account the distance between macro-robotic arm movements and the work arrangement under each macro-robotic arm movement, a macro-robotic arm movement is defined. and Similarity index between The specific definitions are as follows:

[0030]

[0031] Among them, w i ,w j These are the target poses of two macro robotic arms, ROCP. i ROCP j The density of the work points under each macro-robotic arm movement is defined as follows:

[0032]

[0033] c represents the constraint function of the macro-micro robotic arm, and its specific form is as follows:

[0034]

[0035] In the above method, based on the connection relationship and working mode of the macro and micro robotic arms, a matching index for macro-mechanical actions and work points is constructed, including:

[0036] Based on the connection relationships and working modes of the macro and micro robotic arms, and to accurately describe the matching degree between the macro robotic arm's movements and the work points, and to guide the planning algorithm to adjust the macro robotic arm's movements, we design a matching index between the macro robotic arm's movements and the work points, based on the macro robotic arm's movement similarity evaluation function. The specific definition is as follows:

[0037]

[0038] Where B, w, and ROCP are respectively the fused motion constraint satisfaction function, the macro-manipulator motion target point, and the density of the work point under the macro-manipulator motion, and PE is the position information of the work point.

[0039] In the above method, task planning for the macro-micro robotic arm is performed based on the macro-micro robotic arm task representation model, the constraint equations of the macro-micro robotic arm, the macro-mechanical motion and work point matching index, and the macro-mechanical motion similarity index, including:

[0040] Initialize all work points in macro-micro robotic arm task T, as well as the state of the macro-micro robotic arm;

[0041] Determine the set t_set of all currently executable job points, i.e., all job points with the highest priority constraints;

[0042] Based on the work point set t_set, a hierarchical clustering algorithm from bottom to top is used to generate the macro-robotic arm motion set {a B} and the corresponding clusters {cluster};

[0043] Based on the matching index between macro-mechanical motion and work point, the macro-mechanical arm motion a to be optimized is determined. B and the corresponding job cluster;

[0044] Select the work point with the highest matching index between the current macro-mechanical arm's macro-mechanical motion and work point.

[0045] If the matching index s = 0, skip the current step; otherwise, for the current macro-manipulator's action, add the new job point to the job cluster and return to the step of selecting the job point with the highest matching index between the macro-manipulator's action and the job point.

[0046] If there are still unexecuted task points in the macro-micro robotic arm task T, return to the step of determining the set of all currently executable task points t_set. Otherwise, based on the joint travel of the macro-micro robotic arm, use the Cluster Travelling Salesman Problem (CTSP) algorithm and configuration optimization algorithm to generate the motion sequence A of each arm in the macro-micro robotic arm. b and A s .

[0047] The proposed macro-micro robotic arm task planning method reduces the difficulty of planning by first determining the macro robotic arm's actions and then generating the micro robotic arm's actions. It addresses various constraints in on-orbit operations through clustering algorithms and iterative mechanisms, enabling the generation of macro-micro robotic arm actions and reducing the number of motion switching operations, thus avoiding additional time spent on macro-micro robotic arm start-up, shutdown, and vibration suppression. Finally, it employs a clustered traveling salesman algorithm and a configuration optimization algorithm for optimization, obtaining the macro-micro robotic arm action sequence with the shortest task execution time. [Attached Image Description]

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort or labor.

[0049] Figure 1 This is a flowchart illustrating the macro / micro robotic arm task planning method provided in an embodiment of the present invention;

[0050] Figure 2 This is the macro-micro robotic arm model used in the simulation experiment of this invention embodiment.

[0051] Figure 3 This is the simulated task scenario in this experimental embodiment.

[0052] Figure 4 This is a visual representation of the macro-micro robotic arm planning results in this experimental embodiment.

[0053] Figure 5 This is a visual representation of the macro-micro robotic arm planning results obtained from conventional robotic arm solutions. [Specific Implementation Examples]

[0054] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] It should be understood that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0056] This invention provides a motion planning method for macro- and micro-robotic arms based on motion iteration. Please refer to [the relevant documentation]. Figure 1 This is a flowchart illustrating the motion planning method for macro-micro robotic arms based on motion iteration provided in this invention example, as shown below. Figure 1 As shown, the method includes the following steps:

[0057] Step 101: Based on the constraint information between operation steps in the on-orbit mission and the motion data of the macro and micro manipulators, construct a macro and micro manipulator mission representation model.

[0058] Specifically, based on the on-orbit task constraints, the tasks to be executed are defined as T = {t1, ...,t}. n}, where n is the number of job points in the task, and t i This represents the i-th job point in the task. It is defined as follows:

[0059] t i ={PE i C i Visited i}

[0060] Among them, PE i The pose information representing the task consists of a six-dimensional vector, C. i This represents the priority constraint of operations, describing the order of operations between tasks in terms of numerical value. (Visited) i Indicates whether the status has been visited, Visited i =1 indicates that the work site has been planned and completed.

[0061] Based on the motion data of the macro-micro robotic arm, the motion sequence of the macro-micro robotic arm is constructed as follows:

[0062] A={Ab A s}

[0063] Among them, A b and A s These represent the macro-manipulator motion sequence and the micro-manipulator motion sequence, respectively. This represents the i-th macro robotic arm movement. This indicates the j-th macro-robotic arm action, which is executed after the i-th macro-robotic arm action.

[0064] Step 102: Construct constraint equations for the macro-micro robotic arms based on their connection relationships and motion characteristics.

[0065] Specifically, based on the connection relationship and motion characteristics of the spatial macro-micro manipulators, and considering factors such as the dynamic performance of the manipulators, the connection relationship between the two arms, and whether the micro manipulators can perform tasks, a series of constraint equations for the macro-micro manipulators were constructed, as follows:

[0066]

[0067] Where, q ma_i Let q be the joint angle of the macro robotic arm. ma_i For the joint angle of the micro-manipulator, to prevent the joints of the macro-micromanipulator from exceeding the limits, a b ∈W ma and a s ∈W mi The two constraint equations represent the motion of the macromanipulator and a, respectively. b and micro robotic arm movements a s In their respective workspaces W ma and W mi Inside. PE i -a b ∈W mi The task constraint for the work point is that the distance between the work point and the macro robotic arm's movement should be within the workspace of the micro robotic arm.

[0068] Step 103: Based on the macro-manipulator motion data, construct a macro-mechanical motion similarity index;

[0069] Specifically, based on the motion data of the macro robotic arm, the macro robotic arm motion is constructed. and Similarity index between

[0070]

[0071] Among them, w i ,w j These are the target poses of two macro robotic arms, ROCP. i ROCP jThe density of the work points under each macro-robotic arm movement is defined as follows:

[0072]

[0073] c represents the constraint function of the macro-micro robotic arm, and its specific form is as follows:

[0074]

[0075] Step 104: Based on the connection relationship and working mode of the macro and micro robotic arms, construct the matching index between macro robotic motion and work point;

[0076] Specifically, based on the connection relationship and working mode of the macro and micro robotic arms, the macro robotic arm motion a is constructed. b Matching index s(a) between the work point t and the work point t b ,t):

[0077]

[0078] Where B, w, and ROCP+ represent the fused motion constraint satisfaction function, the macro-manipulator's motion target point, and the density of the work point under the macro-manipulator's motion, respectively, and PE represents the position information of the work point.

[0079] Step 105: Based on the macro-micro robotic arm task representation model, macro-micro robotic arm constraint equations, macro-mechanical motion and work point matching index, and macro-mechanical motion similarity index, design a macro-micro robotic arm motion generation method:

[0080] Specifically, the motion sequence of the macro-micro robotic arm is solved according to the following process:

[0081] Step 1: Initialize all work points in macro-micro robotic arm task T, as well as the state of the macro-micro robotic arm;

[0082] Step 2: Determine the set of all currently executable job points t_set, that is, all job points with the highest priority;

[0083] Step 3: Based on the work point set t_set, a hierarchical clustering algorithm from bottom to top is used, with macro-motion similarity index as the criterion, to generate the macro-robotic arm motion set {a B} and the corresponding clusters {cluster};

[0084] Step 4: Based on the matching index between the macro-mechanical motion and the work point, determine the macro-mechanical arm motion a to be optimized. B and the corresponding job cluster;

[0085] Step 5: Based on the macro robotic arm's movement a BThe distribution cluster of the work points and the information t of the work points to be iterated are used to select the matching index s(a) between the current macro-manipulator motion and the work point. b The highest job point to be iterated upon (t);

[0086] Step 6: If the matching index s = 0, skip the current step; otherwise, for the current macro-manipulator's action, add the new job point to the job cluster and return to the step of selecting the job point with the highest matching index between the macro-manipulator's action and the job point.

[0087] Step 7: If there are still unexecuted task points in the macro-micro robotic arm task T, return to the step of determining the set of all currently executable task points t_set. Otherwise, based on the joint travel of the macro-micro robotic arm, use the Clustering Travelling Salesman Problem (CTSP) algorithm and configuration optimization algorithm to generate the motion sequence A of each arm in the macro-micro robotic arm. b and A s .

[0088] Based on the method provided in the embodiments of the present invention, a simulation experiment was conducted on a macro-microarm composed of two redundant robotic arms. Please refer to... Figure 2 The dashed boxes represent macro-robotic arms, and the solid boxes represent micro-robotic arms. The macro and micro-robotic arms are connected by a connecting component. The DH parameters of the macro and micro-robotic arms are shown in Table 1. The parameters of the macro-robotic arm are d1=d7=0.6m, d2=d3=d4=d5=0.35m, a3=a4=5m, and the parameters of the micro-robotic arm are d1=d7=0.6m, d2=d3=d4=d5=0.25m, a3=a4=2.5m.

[0089] Table 1 DH Parameters of Macro / Micro Robotic Arm

[0090] i ]]> ​ a i ]]> d i ]]> i ]]> ​ 1 0 0 d1 -180 2 0 0 [d2] -90 3 -90 [a3] [d3] 0 4 0 <![CDATA[a4]]> <![CDATA[d4]]> 0 5 0 0 <![CDATA[d5]]> 0 6 90 0 0 90 7 90 0 <![CDATA[d7]]> 180

[0091] Please refer to Figure 3 Each point represents a work point. The specific parameters and constraints of the work points are shown in Table 2. The first column is the work point number, the second column is the six-dimensional pose information of the work point, and the third column is the constraint relationship of the work point, indicating the execution order of the work points. The macro-micro robotic arm needs to execute in order of the constraint values ​​from smallest to largest. There is no order for the same values.

[0092] Table 2 Task Information

[0093]

[0094]

[0095] Simulation experiments were conducted on a computer with a CPU of 7-7700k and 16GB of RAM. Please refer to the planning results. Figure 4 and Figure 5 In this diagram, the motion trajectory of the macro-manipulator is represented by a continuous solid line segment, while the motion trajectory of the micro-manipulator is represented by a dashed line segment. Because the macro- and micro-manipulators employ time-division multiplexing, the state of the micro-manipulator changes during the macro-manipulator's movement, resulting in a discontinuous motion trajectory for the micro-manipulator. The sphere in the diagram represents the approximate workspace of the micro-manipulator under the macro-manipulator's actions. This demonstrates that the algorithm presented in this paper can effectively generate corresponding manipulator movements covering all task points, indicating the effectiveness of the proposed method.

[0096] Table 3 shows a comparison of the two methods in terms of motion sequence length, joint range, and running time.

[0097] Table 3 Comparison of Planning Results of the Two Methods

[0098]

[0099] The motion sequence generated by the motion generation method based on motion iteration is significantly shorter than that generated by conventional motion sequence generation methods for macro-micro robotic arms, with a sequence length reduction of 18.75%. The reduction in the number of macro-robotic arm movements improves the stability of the macro-micro robotic arm during task execution. The joint travel and task execution time are reduced by an average of 16.9% and 20%, respectively. If the vibration suppression process during the switching of macro-micro robotic arm movements is also considered, the algorithm proposed in this paper will further reduce the task execution time of the macro-micro robotic arm, proving the rationality of the method of this invention.

[0100] The technical solutions of the embodiments of the present invention have the following beneficial effects:

[0101] Based on the constraints between operational steps and the movements of macro- and micro-manipulators in on-orbit missions, a macro- and micro-manipulator task representation and constraint equations are constructed. Combining the unique time-sharing motion patterns of macro- and micro-manipulators, similarity indices for macro-manipulator movements and matching indices between macro- and micro-manipulator movements and work points are developed. By integrating clustering methods and iterative mechanisms, a macro- and micro-manipulator task planning method is designed to decouple the coupling relationships between the macro- and micro-manipulators, generating feasible macro- and micro-manipulator movement sequences with fewer execution steps and shorter running times, thereby improving the efficiency of macro- and micro-manipulators in executing large-span, precise on-orbit tasks.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0103] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A task planning method for a robotic arm, characterized in that, The method includes: Based on the constraint information between operation steps in the on-orbit mission and the motion data of the macro and micro manipulators, a macro and micro manipulator mission representation model is constructed; the macro and micro manipulators include a macro manipulator and a micro manipulator. Based on the connection relationships and motion characteristics of the macro and micro robotic arms, constraint equations for the macro and micro robotic arms are constructed. Based on the motion data of the macro-robotic arm, a similarity index for macro-robotic movements is constructed. include: Among them, w i ,w j These are the target poses of the two macro robotic arms. ROCP for the i-th and j-th actions of the macro robotic arm respectively. i ROCP j The tightness of the work points under each macro-robotic arm movement includes: cluster i For the cluster of work points corresponding to the i-th macro-robotic arm action, c represents the constraint function of the macro-micro robotic arm, including: Based on the connection relationship and working mode of macro and micro robotic arms, a matching index between macro robotic motion and work point is constructed. Based on the macro-micro robotic arm task representation model, the constraint equations of the macro-micro robotic arm, the macro-mechanical motion and work point matching index, and the similarity index of the macro-mechanical motion, the macro-micro robotic arm is task-planned.

2. The method according to claim 1, characterized in that, The process of constructing a macro-micro robotic arm task representation model based on constraint information between operation steps in the on-orbit mission and macro-micro robotic arm motion data includes: Based on the constraint information between operation steps in the on-orbit mission and the motion data of the macro- and micro-manipulators, a macro- and micro-manipulator mission representation model of the following form is constructed for an on-orbit mission with n operation points and macro- and micro-manipulator motions: T={t1,L,t n } t i ={PE i C i Visited i } A={A b A s } Where T represents the on-orbit task to be performed, t i PE represents the i-th operation point in on-orbit mission T. i This represents the pose information of an on-orbit mission, which consists of a six-dimensional vector, C. i This represents a priority constraint for operations, used to describe the order of operations between tasks in terms of numerical magnitude; Visited i Indicates whether the status has been visited, Visited i =1 indicates that the on-orbit mission has been planned and completed; A b and A s These represent the macro-manipulator motion sequence and the micro-manipulator motion sequence, respectively. This represents the i-th macro robotic arm movement. This indicates the j-th micro-robotic arm action, which is executed after the ith macro-robotic arm action.

3. The method according to claim 1, characterized in that, Based on the connection relationships and motion characteristics of the macro-micro robotic arms, the constraint equations for the macro-micro robotic arms are constructed, including: Based on the connection relationships and motion characteristics of the macro-micro robotic arms, and considering factors such as the dynamic performance of the macro-micro robotic arms, the connection relationships between the two arms, and whether the micro robotic arm can perform tasks, constraint equations for the macro-micro robotic arms are constructed, including: -π≤q ma_i ≤π,i=1,2,...,7 -π≤q mi_j ≤π,j=1,2,...,7 a b ∈W ma a s ∈W mi ON i -a b ∈W mi Where, q ma_i Let q be the joint angle of the macro robotic arm. ma_i Let a be the joint angle of the micro-robotic arm. b ∈W ma and a s ∈W mi The two constraint equations represent the motion of the macromanipulator, a. b and micro robotic arm movements a s In their respective workspaces W ma and W mi Inside; PE i For the pose information of the i-th work point, PE i -a b ∈W mi The task constraint for the work point indicates that the distance between the work point and the macro robotic arm's movement should be within the workspace of the micro robotic arm.

4. The method according to claim 1, characterized in that, Based on the connection relationship and working mode of the macro and micro robotic arms, a matching index for macro-mechanical actions and work points is constructed, including: Based on the connection relationship and working mode of the macro and micro robotic arms, a matching index for macro robotic motion and work point is constructed, including: Where B, w, and ROCP are the fused motion constraint satisfaction function, the macro-manipulator motion target point, and the density of the work point under the macro-manipulator motion, respectively, and PE is the position information of the work point.

5. The method according to claim 1, characterized in that, Based on the macro-micro robotic arm task representation model, the constraint equations of the macro-micro robotic arm, the matching index between the macro-mechanical motion and the work point, and the similarity index of the macro-mechanical motion, task planning for the macro-micro robotic arm is performed, including: Initialize all work points in macro-micro robotic arm task T, as well as the state of the macro-micro robotic arm; Determine the set of all currently executable job points, t_set; Based on the work point set t_set, a hierarchical clustering algorithm is used to generate the macro-robotic arm motion set {a B } and the corresponding clusters {cluster}; Based on the matching index between macro-mechanical motion and work point, the macro-mechanical arm motion a to be optimized is determined. B and the corresponding job cluster; Select the work point with the highest matching index between the current macro-mechanical arm's macro-mechanical motion and work point. If the matching index s = 0, skip the current step; otherwise, for the current macro-manipulator's action, add the new work point to the work point cluster and return to the step of selecting the work point with the highest matching index between the macro-manipulator's action and the work point. If there are still unexecuted task points in the macro-micro robotic arm task T, return to the step of determining the set of all currently executable task points t_set. Otherwise, based on the joint travel of the macro-micro robotic arm, use the clustered traveling salesman algorithm and configuration optimization algorithm to generate the motion sequence A of each arm in the macro-micro robotic arm. b and A s .

Images