Mobile robot multi-task point location path planning method based on A star algorithm and related equipment

Abstract

The invention discloses a mobile robot multi-task point location path planning method based on an A star algorithm and related equipment, and the method comprises the steps: (1) building an environment map, planning a path from a starting point to a next target cargo point in sequence based on the A star algorithm, and forming a preliminary path containing all target cargo points; and (2) aiming at the position of each target cargo point in the planned path in the step (1), calculating an optimal stop point aiming at each target cargo point on the planned path. And (3) according to the optimal stop point information, the starting point position and the obstacle information, constructing an environment map, and planning a path from the starting point to the next optimal stop point in sequence by using an A star algorithm, thereby forming the whole path planning. According to the scheme, a complete path can be planned for the mobile robot during multi-point task operation, and meanwhile, the path can enable the position error and the power consumption of an end effector of a mechanical arm of the mobile robot to be minimum.

Description

Technical Field

[0001] The present invention relates to a mobile robot control technology, and in particular to a mobile robot path planning technology. Background Art

[0002] With the rapid development of robot technology, mobile robots are currently widely used in automated production lines, assembly, welding, spraying and other links.

[0003] Tasks in different scenarios are usually complex and diverse, and require high positioning accuracy and power consumption of robots. Currently, in order to improve work efficiency, multiple robots are usually deployed to perform multi-point tasks. This method does not take into account the situation when only one robot is performing multi-point tasks when conditions are limited or in emergencies; moreover, in actual application, mobile robots do not complete only one task at a time, but need to complete tasks at multiple target cargo locations.

[0004] Therefore, it is crucial to plan the path for the mobile robot to move to each task point and to ensure the position accuracy and energy consumption control of the joint angle movements of the robotic arm at different task points.

[0005] Many mobile robot path planning schemes based on the A-star algorithm are provided in the prior art, such as the Chinese invention patent with announcement number CN114152263B, the Chinese invention patent application with publication number CN108253984A and other public schemes. The existing mobile robot path planning schemes based on the A-star algorithm do not fully consider the structure of the mobile robot itself (such as a mobile robot with an operating robotic arm) when performing path planning and determining the docking nodes, resulting in the determined docking nodes being unsuitable for the mobile robot, so that the mobile robot cannot carry out effective operations when moving to the corresponding docking nodes as planned.

[0006] Therefore, when the same robot performs multi-point task operations, how to perform path planning and improve the accuracy and power consumption of the mobile robot when operating at multiple task points is an urgent problem to be solved in this field. Summary of the invention

[0007] In view of the problems existing in the existing mobile robot path planning scheme based on the A-star algorithm, the purpose of the present invention is to provide a mobile robot multi-task point path planning method based on the A-star algorithm, which can not only perform multi-point task path planning for the mobile robot, but also simultaneously improve the accuracy and power consumption of the mobile robot when operating at multiple task points; on this basis, the present invention further provides a smooth curve construction system and related equipment that can implement this method.

[0008] In order to achieve the above object, the present invention provides a mobile robot multi-task point path planning method based on the A-star algorithm, comprising:

[0009] (1) Construct an environment map and plan the path from the starting point to the next target cargo point in sequence based on the A-star algorithm until the final target cargo point is reached, thereby forming a preliminary path that includes all target cargo points;

[0010] (2) For each target cargo point location in the path planned in step (1), the optimal stop point for each target cargo point on the planned path is calculated in combination with the working space feature data of the mobile robot.

[0011] (3) Based on the optimal stop point information, starting point location and obstacle information determined in step (2), an environmental map is constructed, and then the A-star algorithm is used to plan a path from the starting point to the next optimal stop point in sequence until the final optimal stop point is reached, thereby forming the entire path planning.

[0012] In some embodiments of the present invention, in step (1), an environment map is constructed based on the location of the starting point, the locations of n target cargo points and obstacle information.

[0013] In some embodiments of the present invention, in step (2), the structural characteristics of the working parts of the mobile robot are used as constraints on the path preliminarily planned in step (1), and a multi-objective optimization algorithm is introduced to calculate the point with the minimum energy consumption and error of the working parts of the mobile robot on the path points near each target point as the optimal stopping point.

[0014] In some embodiments of the present invention, in step (3), the environment map is reconstructed based on the information of the n optimal stopping points U, the starting point positions and the obstacle information determined in step (2).

[0015] In some embodiments of the present invention, when the path planning is performed based on the A-star algorithm in step (3), the path from the starting point to the optimal stop point U1 is planned based on the secondary constructed environment map using the A-star algorithm. After the planning is completed, the path from the optimal stop point U1 to the optimal stop point U2 is continued to be planned, and the path from the optimal stop point Ui to the optimal stop point Ui+1 is planned in turn, and finally until the optimal stop point Un is reached, thereby forming the entire path planning.

[0016] In order to achieve the above-mentioned object, the present invention also provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the steps of the above-mentioned mobile robot multi-task point path planning method are implemented.

[0017] In order to achieve the above-mentioned object, the present invention also provides a processor, which is used to run a program, and when the program is running, the steps of the above-mentioned mobile robot multi-task point path planning method are executed.

[0018] In order to achieve the above-mentioned purpose, the present invention also provides a terminal device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program code is loaded and executed by the processor to implement the steps of the above-mentioned mobile robot multi-task point path planning method.

[0019] In order to achieve the above-mentioned object, the present invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of the above-mentioned mobile robot multi-task point path planning method.

[0020] The mobile robot multi-task point path planning method based on the A-star algorithm provided by the present invention can plan a complete path for the mobile robot when performing multi-point task operations, and at the same time, the path can minimize the position error and power consumption of the end effector of the mobile robot's mechanical arm. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0022] Figure 1 The overall flow chart of multi-task point path planning of a mobile robot based on the A-star algorithm in the example of the present invention;

[0023] Figure 2 A flowchart of solving the calculation for determining the optimal stop point for a target cargo point in an example of the present invention;

[0024] Figure 3 is an execution flow chart of the A-star algorithm in the example of the present invention;

[0025] Figure 4 An example of environmental map information containing a target cargo point constructed in an example of the present invention;

[0026] Figure 5 This is an example of the preliminary path planning performed in the example of the present invention;

[0027] Figure 6 An example of environmental map information containing an optimal stop point in an example of the present invention;

[0028] Figure 7 This is an example of the final path planning performed in the example of the present invention. DETAILED DESCRIPTION

[0029] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below with reference to specific diagrams.

[0030] By studying the existing solutions for realizing mobile robot path planning based on the A-star algorithm, the present invention provides a mobile robot multi-task point path planning solution based on the A-star algorithm. On the basis of multi-task point path planning for the same mobile robot, the present invention can further calculate and determine the optimal stop point for each task point in combination with the working space feature data of the mobile robot, and further optimize the overall path of the multi-task points based on the determined optimal stop point, thereby ensuring the position accuracy of the action of the working parts on the mobile robot at different task points and reducing the operating power consumption of the entire mobile robot.

[0031] The solution of the present invention takes a mobile robot with an operating robotic arm mounted on its upper part as an example to specifically illustrate the process of performing path planning for the mobile robot based on the A-star algorithm provided by the present invention, and simultaneously providing efficient joint angle motion optimization control for the robotic arm mounted on the upper part of the mobile robot, thereby ensuring the optimal position accuracy and power consumption of the robotic arm during operation.

[0032] Specifically, this example solution first constructs an environment map containing the starting point, target cargo point, and obstacles through the A-star algorithm, and then performs preliminary path planning.

[0033] On this basis, in order to ensure that the position error and energy consumption of the robotic arm are minimized during operation, and to prevent the robotic arm from colliding, the robotic arm structural parameters are innovatively used as constraints, and a multi-objective optimization method is used to calculate the minimum robotic arm energy consumption and position error at each point for the path points near each target cargo point; then the minimum value among the multiple minimum values ​​is further selected as the optimal value, the point corresponding to the optimal value is used as the optimal stopping point, and the solution corresponding to the optimal value is used as the working parameter of the robotic arm joint angle when the robot performs the task at the stopping point.

[0034] Finally, after all the optimal stop points are obtained, the A-star algorithm is used to construct an environmental map containing the starting point, optimal stop points, and obstacles, and then the final path is planned.

[0035] See also Figure 1 , which is an overall flow chart of multi-task point path planning of a mobile robot based on the A-star algorithm in an example of the present invention.

[0036] Based on the diagram, the implementation process of the overall path planning for the mobile robot with an operating robot arm on top is as follows:

[0037] (1) Construct an environmental map and plan the path from the starting point to the next target cargo point based on the A-star algorithm until the final target cargo point is reached. This completes the path planning for the entire target cargo point and forms a preliminary path that includes all target cargo points.

[0038] (2) For each target cargo point location in the path planned in step (1), the optimal stop point for each target cargo point on the planned path is calculated in combination with the working space feature data of the mobile robot.

[0039] (3) Based on the optimal stop point information, starting point location and obstacle information determined in step (2), an environmental map is constructed, and then the A-star algorithm is used to plan a path from the starting point to the next optimal stop point in sequence until the final optimal stop point is reached. In this way, the entire path planning is completed, forming a complete path containing all the optimal stop points corresponding to different target cargo points.

[0040] As a further specific explanation, when constructing the environment map in step (1) of this example solution, based on the A-star algorithm, first based on the known starting point start_point position, n target points T1, T2, T3, ...Ti, ...T n The initial environment map is constructed based on the location and obstacle information of the vehicle, and the initial path planning is further performed on this basis.

[0041] As a further example, this example solution is preferably implemented by the following steps:

[0042] S1: First, based on the known starting point start_point position, n target cargo points T1, T2, T3,…Ti,…T n The location and obstacle information of the environment are used to build an environment map, such as Figure 4 As shown;

[0043] Next, the path from the starting point start_point to the target cargo point T1 is planned based on the A-star algorithm. After the planning is completed, the path from the target cargo point T1 to the target cargo point T2 is planned in turn. i To the target cargo point T i+1 The path, until the destination cargo point T is reached n , thereby forming a preliminary path containing all target cargo points, such as Figure 5 shown.

[0044] It should be noted that during the entire path planning process, if there is no solution for the path planning of a certain section, it means that the multi-task point path planning has failed; if the multi-task point path planning has failed, the path planning can be performed again by adjusting the heuristic function h. For example, if the heuristic function h is adjusted to 0, the A-star algorithm becomes the Dijkstra algorithm.

[0045] Finally, after the entire path planning is successful, you will get a path from the starting point through T1, T2, T3, ... T i ,…T n A complete path of a series of points.

[0046] As a further specific explanation, the preliminary path including all target cargo points planned in step (1) is specifically based on the starting point start_point position, n target cargo points T1, T2, T3, ...Ti, ...T n The planning process is based on the location and obstacle information of the mobile robot. The planning process does not incorporate the operating space feature data of the mobile robot equipped with an operating robot arm, such as the operating radius, height, angle, etc. of the operating robot arm. Since each connecting rod of the robot arm has a certain length, when the mobile robot moves to the corresponding target cargo point according to the path planned in step (1), the robot arm on the mobile robot overlaps with the target cargo point, which will cause the end actuator to be unable to perform the corresponding operation, such as clamping, cutting, etc., and the mobile robot needs to stop before reaching the target cargo point to perform the target cargo point operation task.

[0047] Specifically, in step (1) of this example solution, when planning and forming a multi-task point path, the cargo point is specifically used as the stop point or a stop point is roughly determined near the cargo point, and the optimal stop point is not determined. If this multi-task point path is used to guide the mobile robot to operate, it will cause the robot arm to collide with the cargo once the mobile robot arrives at the cargo point, or a stop point is roughly determined near the cargo point, but the error and energy consumption of the robot arm when picking up the cargo at this point are not minimized, thereby affecting the operation and endurance of the mobile robot robot arm.

[0048] Accordingly, this example solution determines the working space characteristic data of the mobile robot based on the structural parameters of the mechanical arm configured on the mobile robot, and optimizes the position of each target cargo point on the path planned in step (1) accordingly, thereby calculating and determining the optimal stop point for each target cargo point on the planned path.

[0049] Furthermore, when optimizing the position of each target cargo point on the path planned in step (1) to obtain the optimal stop point, this example solution uses the structural characteristics of the robot arm (joint angle range) as constraints on the path preliminarily planned in step (1), and introduces a multi-objective optimization algorithm to calculate the point on the path near each target cargo point where the energy consumption and error of the robot arm are minimized as the optimal stop point.

[0050] As a further example, based on the above-mentioned example solution of step S1, this example solution is preferably implemented by the following steps:

[0051] S2: The optimal stop point U for each target cargo point is solved by combining the position of the target cargo point with the position of each point on the path in S1. The solution process is as follows: Figure 2 As shown, the specific implementation process is as follows:

[0052] S21: According to the structural parameters of the robot arm, the DH parameter method combined with the Monte Carlo method is used to calculate the working space of the robot arm in a three-dimensional environment.

[0053] S22: According to the target cargo point T i (X i ,Y i ,Z i ), calculate the height Z of the robot arm in the workspace i Working radius R i ;

[0054] S23: In the XY plane, with the target cargo point T i is the center of the circle, R i Search for path points P1, P2, P3, ... P in the area with radius j ,…P m ;

[0055] S24: Calculate path point P j (j=1,2,3,…,m) and the target cargo point T i The relative position (x i ,y i ,z i );

[0056] S25: For each joint angle of the robot arm θ={θ1,θ2,…,θ q} is optimized (q is the total number of robot arm joints) so that the robot arm reaches the target cargo point T i The position error and energy consumption are minimized.

[0057] Specifically, define E error (θ) represents the position error objective function, that is, the Euclidean distance between the target cargo point and the end effector of the robot arm, and the formula is expressed as:

[0058]

[0059] Where (x(θ), y(θ), z(θ)) is the actual position of the end effector, which can be obtained by the transformation matrix from the end effector coordinate system to the base coordinate system To obtain.

[0060] In this example, the position error objective function based on the above can effectively and accurately measure the difference between the position of the end effector in space and the position of the cargo point after the robot arm actually moves to the position. At the same time, using it as an objective function can effectively perform multi-objective optimization of the joint angle.

[0061] Furthermore, define E energy (θ) represents the energy consumption objective function of each joint rotating to the target cargo point, and the formula is expressed as:

[0062]

[0063] Among them I i is the moment of inertia of the ith joint, ω i is the average angular velocity of the ith joint, m is the total mass of the robot and the end load, g is the acceleration due to gravity, and h is the height difference of the robot from the zero position to the final actual position.

[0064] In actual application, the movement of the robot from one point to another consumes energy, and the amount of energy consumption affects its endurance. Therefore, in the actual control process, it is necessary to consider how to minimize the energy consumption when the robot moves. In this regard, this example solution establishes the above energy consumption objective function, which is composed of the gravitational potential energy and the kinetic energy of the joint angle when the robot moves from one point to another. By defining the energy consumption objective function and using it as an objective function, it can effectively perform multi-objective optimization of the joint angle.

[0065] On this basis, combining the above two objective functions, a multi-objective optimization function is established:

[0066] min f(θ)=α·E error (θ)+β·E energy (θ) (3);

[0067] Constraints:

[0068] Among them, α and β are weight factors used to balance the two objectives. These two factors are adjusted according to the actual test; 100mm is the allowable range of the position error of the end, and this value can be modified according to the actual situation.

[0069] By using genetic algorithms to optimize and solve the established multi-objective function, m values ​​of f(θ) can be obtained.

[0070] S26: Extract the smallest value among the m f(θ) determined in step S25, and determine that the smallest value corresponds to a set of joint angles θ and a stop point. This joint angle is the minimum value at the target cargo point T i The minimum position error and energy consumption when the stop is at the target cargo point T i The optimal stop point U at time .

[0071] Based on this, this example solution optimizes the joint angles of the robot arm. Combined with step (S1), it can be seen that this example solution innovatively effectively screens the path points in the robot arm workspace in the preliminary path planning before optimizing the solution, and then constructs the corresponding multi-objective optimization function of energy consumption and position error, and further calculates the optimal solution of each path point through a genetic algorithm with the robot arm structure parameters and error range as constraints, and then the minimum value in the optimal solution corresponds to the optimal stop point.

[0072] S27: Repeat steps S22-S27 until the target cargo point T1, T2, T3, ... T i ,…T n The optimal stop point U is solved.

[0073] As a further specific explanation, when constructing the environment map in step (3) of this example solution, the optimized environment map is constructed based on the A-star algorithm according to the above-mentioned n optimal stop point U information, the starting point start_point position and the obstacle information.

[0074] Furthermore, in step (3) of this example solution, when path planning is performed based on the A-star algorithm, after the path is completed based on the A-star planning, the structural characteristics of the robot arm are used as constraints, and a multi-objective optimization algorithm is introduced to calculate each point within the path range, and the point within the working range of the robot arm that minimizes the energy consumption and error of the robot arm is selected as the optimal stop point; finally, the A-star algorithm is used again to plan a path from the starting point to the next optimal stop point in sequence, until the path to the final optimal stop point is reached.

[0075] As a further example, this example solution is preferably implemented by the following steps:

[0076] S3: First, based on the above n optimal stop point U information, the starting point start_point position and obstacle information, a secondary environment map is constructed, such as Figure 6 As shown;

[0077] Next, based on the secondary constructed environment map, the A-star algorithm is used to plan the path from the starting point start_point to the stop point U1. After the planning is completed, the path from the stop point U1 to the stop point U2 is planned in turn, and the path from the stop point Ui to the stop point Ui+1 is planned in turn, and finally until the stop point Un is reached, thus forming the entire path planning, as shown in Figure 7 shown.

[0078] During the entire path planning process, if there is no solution for the path planning of a certain section, it means that the multi-task point path planning has failed. If the multi-task point path planning has failed, the path planning can be performed again by adjusting the heuristic function h. For example, if the heuristic function h is adjusted to 0, the A-star algorithm becomes the Dijkstra algorithm.

[0079] Finally, after the entire path planning is successful, a complete path will be obtained, which starts from the starting point and passes through a series of points including U1, U2, U3,…Ui,…Un.

[0080] As a further specific explanation, in this example solution, the A-star algorithm is introduced to perform path planning through a heuristic search mode, which can not only ensure that the optimal path is found, but also improve the search efficiency.

[0081] The A-star algorithm introduced in this example searches by calculating an evaluation function F = g + h for each node. g is the actual cost function, which represents the actual path length from the starting point to the current point; h is the heuristic cost function, which represents the estimated cost from the current point to the target cargo point.

[0082] See also Figure 3 The basic operation process of the A-star algorithm introduced in this example is as follows:

[0083] (a) First, put the starting point into the open list and initialize the g value of the starting point to 0. At this time, F = h (start_point);

[0084] (b) Determine whether the open list is empty. If so, end the process; otherwise, proceed to the next step.

[0085] (c) Take the node N with the smallest F value from the open list;

[0086] (d) Determine whether it is the target cargo point, if so, construct the path and end;

[0087] (e) Otherwise, move N to the closed list, indicating that the node N has been processed;

[0088] (f) For each adjacent node node of N, determine whether the node node is accessible or not in the closed list. If it is not accessible or in the list, return to step b, otherwise proceed to the next step;

[0089] (g) Determine whether node node is in the open list. If not, add node node to the open list, update the F value of node node, and return to step b, otherwise proceed to the next step;

[0090] (h) Calculate the g value and F value of node node, and determine whether the sum of the g value of node node is less than its old g value. If not, return to step b, otherwise continue to the next step;

[0091] (i) Update the g value and value of node node, set the parent node of node node to point N, and return to step b.

[0092] The resulting multi-task point path planning scheme for mobile robots based on the A-star algorithm can plan a complete path for the mobile robot when performing multi-point task operations compared to the current methods on the market. At the same time, this path can minimize the position error and power consumption of the end effector of the mobile robot's robotic arm.

[0093] The mobile robot multi-task point path planning scheme based on the A-star algorithm given in this example can be used as a corresponding software program to form a corresponding mobile robot multi-task point path planning system based on the A-star algorithm. When the software program is running, it will execute the above-mentioned mobile robot multi-task point path planning method and store it in the corresponding storage medium for the processor to call and execute.

[0094] With respect to the above-mentioned mobile robot multi-task point path planning scheme based on the A-star algorithm, an embodiment of the present invention further provides a computer-readable storage medium on which a program is stored, which, when executed by a processor, implements the steps of the above-mentioned mobile robot multi-task point path planning method based on the A-star algorithm.

[0095] An embodiment of the present invention further provides a processor, which is used to run a program, wherein when the program is running, the steps of the above-mentioned mobile robot multi-task point path planning method based on the A-star algorithm are executed.

[0096] An embodiment of the present invention also provides a terminal device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program code is loaded and executed by the processor to implement the steps of the above-mentioned mobile robot multi-task point path planning method based on the A-star algorithm.

[0097] The present invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of the above-mentioned mobile robot multi-task point path planning method based on the A-star algorithm.

[0098] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0104] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0105] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0106] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0107] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0108] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0109] The above-mentioned method of the present invention, or a specific system unit, or a part of the same, is a pure software architecture, which can be arranged on a physical medium, such as a hard disk, an optical disk, or any electronic device (such as a smart phone, a computer-readable storage medium) through program code. When the machine loads the program code and executes it (such as a smart phone loading and executing it), the machine becomes a device for implementing the present invention. The above-mentioned method and device of the present invention can also be transmitted in the form of program code through some transmission media, such as cables, optical fibers, or any transmission mode. When the program code is received, loaded and executed by a machine (such as a smart phone), the machine becomes a device for implementing the present invention.

[0110] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A mobile robot multi-task point path planning method based on the A-star algorithm, characterized in that: include: (1) Construct an environment map and plan the path from the starting point to the next target cargo point in sequence based on the A-star algorithm until the final target cargo point is reached, thereby forming a preliminary path that includes all target cargo points; (2) For each target cargo point location in the path planned in step (1), the optimal stop point for each target cargo point on the planned path is calculated in combination with the working space feature data of the mobile robot. (3) Based on the optimal stop point information, starting point location and obstacle information determined in step (2), an environmental map is constructed, and then the A-star algorithm is used to plan a path from the starting point to the next optimal stop point in sequence until the final optimal stop point is reached, thereby forming the entire path planning.

2. The mobile robot multi-task point path planning method according to claim 1, characterized in that: In the step (1), an environment map is constructed based on the starting point position, the positions of n target cargo points and obstacle information.

3. The mobile robot multi-task point path planning method according to claim 1, characterized in that: The step (2) takes the structural characteristics of the working parts of the mobile robot as constraints on the path preliminarily planned in step (1), and introduces a multi-objective optimization algorithm to calculate the point with the minimum energy consumption and error of the working parts of the mobile robot on the path points near each target point as the optimal stopping point.

4. The mobile robot multi-task point path planning method according to claim 1, characterized in that: In the step (3), the environment map is reconstructed based on the information of the n optimal stopping points U, the starting point position and the obstacle information determined in the step (2).

5. The mobile robot multi-task point path planning method according to claim 1, characterized in that: When the path planning is performed based on the A-star algorithm in step (3), the A-star algorithm is used to plan the path from the starting point to the optimal stop point U1 based on the secondary constructed environment map. After the planning is completed, the path from the optimal stop point U1 to the optimal stop point U2 is continued to be planned, and the path from the optimal stop point Ui to the optimal stop point Ui+1 is planned in sequence, and finally until the optimal stop point Un is reached, thereby forming the entire path planning.

6. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the mobile robot multi-task point path planning method described in any one of claims 1-5 are implemented.

7. A processor for running a program, characterized in that: When the program is running, the steps of the mobile robot multi-task point path planning method described in any one of claims 1-5 are executed.

8. A terminal device, comprising a processor, a memory, and a program stored in the memory and executable on the processor, characterized in that: The program code is loaded and executed by the processor to implement the steps of the mobile robot multi-task point path planning method described in any one of claims 1-5.

9. A computer program product, characterized in that When executed on a data processing device, it is suitable for executing the steps of the mobile robot multi-task point path planning method described in any one of claims 1-5.

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