Mobile robot path planning method and equipment based on time sequence

Abstract

The invention discloses a mobile robot path planning method and equipment based on a time series, and relates to the technical field of robot navigating.The mobile robot path planning method based on the time series mainly comprises the steps that according to a starting point and an ending point, a preliminary planning path is obtained through a fast expansion random tree method, and a preliminary planning track is obtained by utilizing a straight line segment function with parabola transition, and a final planning track is obtained by utilizing a five-order polynomial according to the motion state of the mobile robot. By implementing the time sequence-based mobile robot path planning method and equipment provided by the invention, the continuity and stability of the path planning of the mobile robot can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of robot navigation, and more specifically, to a path planning method and device for a mobile robot based on time series. Background Art

[0002] With the rise of various autonomous navigation mobile robots, planning a safe and efficient path is an important foundation for mobile robots. Commonly used path planning can be divided into three categories according to algorithm strategies: heuristic algorithms, probability-based algorithms, and bionic algorithms. These algorithms can obtain the shortest path, but the accuracy of modeling complex spaces and obstacles directly affects the search efficiency, and the computational complexity and time cost are relatively large. Probability-based algorithms include RRT and other algorithms. These algorithms have the ability to avoid accurate modeling of complex spaces, but they also have disadvantages such as low search efficiency and being easily trapped in local minima. Intelligent bionic algorithms include genetic algorithms, particle swarm algorithms, ant colony algorithms, etc. Although these algorithms have very strong learning capabilities, they have poor real-time performance, large computational complexity, and require a large amount of storage space.

[0003] The Rapidly-exploring Random Tree (RRT) algorithm has the advantages of simple structure, probabilistic completeness, small computational complexity in high-dimensional spaces, and can easily handle obstacles and differential constraints, and is widely used in the path planning of robots. However, it also has disadvantages such as low search efficiency of sampling nodes, being easily trapped in local minima, non-optimal path length, and non-smooth path. Many improved RRT algorithms, such as the RRT algorithm that introduces probabilistic target bias in the sampling strategy, where sampling nodes shift towards the target point with a certain probability to improve the search efficiency, but the adaptability is poor. And the Bi-RRT and RRT Connect algorithms of bidirectional growing random trees reduce the time of path planning. However, the motion planning of mobile robots not only includes front-end path planning, but also includes back-end trajectory planning that adapts to the constraints of the kinematics and dynamics of mobile robots. This kind of motion planning based on time and energy forms will be the focus of research on ground mobile robots. Summary of the Invention

[0004] The purpose of the present invention is to provide a path planning method and device for a mobile robot based on time series, which can improve the continuity and stability of the path planning of the mobile robot.

[0005] The present invention provides a path planning method for a mobile robot based on time series, including the following steps: S1: According to the starting point and the ending point, use the rapidly-exploring random tree method to obtain a preliminary planned path; S2: According to the preliminary planned path, use a straight-line segment function with parabolic transition to obtain a preliminary planned trajectory; S3: According to the preliminary planned trajectory and the motion state of the mobile robot, use a fifth-order polynomial to obtain the final planned trajectory.

[0006] Further, step S1 specifically includes: S11: According to the starting point and the ending point, with the starting point as the initial root node, use the rapidly-exploring random tree method to obtain the first path; S12: According to the starting point and the ending point, with the ending point as the initial root node, use the rapidly-exploring random tree method to obtain the second path; S13: According to the first path and the second path, in accordance with the principle that the line segment between two points is the shortest, find the node with the shortest distance between two obstacles, determine that the number of nodes with the shortest distance between the two obstacles does not exceed the preset number of nodes, and connect all the nodes with the shortest distance between the two obstacles in the order of proximity to obtain a preliminary planned path.

[0007] Further, step S2 specifically includes: S21: According to the starting point and the second node in the preliminary planned path, use the straight-line segment function with parabolic transition to obtain the first segment of the trajectory; S22: According to the preliminary planned path, use the straight-line segment function with parabolic transition to obtain the middle segment of the trajectory; S23: According to the ending point and the second-to-last node in the preliminary planned path, use the straight-line segment function with parabolic transition to obtain the last segment of the trajectory; S24: According to the first segment of the trajectory, the middle segment of the trajectory, and the last segment of the trajectory, obtain a preliminary planned trajectory.

[0008] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned mobile robot path planning method based on time series are implemented.

[0009] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned mobile robot path planning method based on time series are implemented.

[0010] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned mobile robot path planning method based on time series are implemented.

[0011] Implementing the mobile robot path planning method and device based on time series provided by the present invention has the following beneficial effects: The present invention constrains the motion states of each segment of the mobile robot in the optimized path, and uses a linear segment with parabolic blend (LSPB) function model to smooth the optimized planned path and perform trajectory planning; the displacement, speed, and acceleration of the motion planning all conform to the motion performance of the mobile robot, and can maintain continuity and stability. The turning radius is also constrained, and the smoothness of the turning angle of the optimized path is greatly improved; the present invention divides the planned path after RRT optimization into three parts: the first segment, the middle segment, and the last segment. The transition segment uses a fifth-order polynomial variable-speed motion, and the solid line in the middle of the path nodes uses a uniform acceleration linear motion. The trajectory planning of each segment of the mobile robot is combined through a time series to realize the stable and continuous operation of the motion state of the mobile robot; on the basis of improving the RRT algorithm, the present invention combines the LSPB function model and the motion constraints of the mobile robot to smooth the subsequent trajectory planning, and conducts a simulation experiment on this algorithm in a complex map environment. Through the simulation analysis of the motion state of the mobile robot, the reliability and effectiveness of this algorithm are verified. Description of the Drawings

[0012] The present invention will be further described below in conjunction with the drawings and embodiments. In the drawings: Figure 1 is a flowchart of the mobile robot path planning method based on time series provided by the present invention; Figure 2 is a schematic diagram of the LSPB function model provided by the present invention; Figure 3 is a schematic diagram comparing the planned paths of three algorithms provided by the present invention; Figure 4 is a schematic diagram of the planned path trajectory and the displacements in the X and Y directions provided by the present invention; Figure 5 is a schematic diagram of the speeds in the X and Y directions of the mobile robot provided by the present invention; Figure 6 is a schematic diagram of the relationship between the speed and time when the mobile robot moves along the planned path provided by the present invention; Figure 7 is a schematic diagram of the accelerations in the X and Y directions of the mobile robot provided by the present invention; Figure 8 is a schematic diagram of the acceleration of the mobile robot provided by the present invention; Figure 9 is a schematic diagram of the curvature of the mobile robot provided by the present invention; Figure 10 is a structural block diagram of the computer device provided by the present invention. Detailed Embodiments

[0013] For a clearer understanding of the technical features, objectives, and effects of the present invention, the specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0014] Figure 1 The schematic diagram of the mobile robot path planning method based on time series in this embodiment is shown. In this embodiment, the mobile robot path planning method based on time series includes the following steps: S1: According to the starting point and the ending point, use the rapidly-exploring random tree method to obtain a preliminary planned path; In an exemplary embodiment, step S1 specifically includes: S11: According to the starting point and the ending point, with the starting point as the initial root node, use the rapidly-exploring random tree method to obtain the first path; As an exemplary embodiment, in step S11, assume that the starting point of the path to be planned is A and the ending point is B; first, with point A as the initial root node, use the rapidly-exploring random tree method to find the first path, and this first path is connected by obstacle avoidance nodes A, C, D, etc.; S12: According to the starting point and the ending point, with the ending point as the initial root node, use the rapidly-exploring random tree method to obtain the second path; As an exemplary embodiment, in step S12, then with point B as the initial root node, use the rapidly-exploring random tree method to find the second path, and this second path is connected by obstacle avoidance nodes B, F, H, etc.; S13: According to the first path and the second path, in accordance with the principle that the line segment between two points is the shortest, find the node with the shortest distance between two obstacles, determine that the number of nodes with the shortest distance between two obstacles does not exceed the preset number of nodes, and connect all the nodes with the shortest distance between two obstacles in the nearest order to obtain a preliminary planned path; As an exemplary embodiment, in step S13, then from path 1 and path 2, in accordance with the principle that the line segment between two points is the shortest, find the node with the shortest distance between two obstacles. If the number of nodes is too large, repeat steps 1 and 2 until the final number of nodes does not exceed the preset number of nodes. In this embodiment, the preset number of nodes is 12, such as nodes A, C, H,..., B; connect nodes A, C, H,..., B in the nearest order to complete the RRT path planning, that is, obtain a preliminary planned path; S2: According to the preliminary planned path, use the straight-line segment function with parabolic transition to obtain a preliminary planned trajectory; In an exemplary embodiment, step S2 specifically includes: S21: According to the starting point and the second node in the preliminary planned path, use the straight-line segment function with parabolic transition to obtain the first segment of the trajectory; In an exemplary embodiment, step S21 specifically includes: according to the starting point and the second node in the preliminary planned path, using a straight-line segment function with a parabolic transition to obtain the first segment of the trajectory, as shown in the formula: , , , , , wherein, represents the corresponding moment, represents the position at the moment, represents the starting point of the planned path, and represent the speed at the moment, represents the time required for the mobile robot's speed to change from 0 to , represents the movement time from the end point of the first uniform straight-line motion to the node, and are the speeds during the time period, represents the second node of the planned path and the corresponding moment is is the position during the time period, is from the node to the node, i.e., the total time required for the first segment of the movement path trajectory, S22: According to the preliminary planned path, using a straight-line segment function with a parabolic transition to obtain the middle segment of the trajectory; In an exemplary embodiment, step S22 specifically includes: according to the preliminary planned path, using a straight-line segment function with a parabolic transition to obtain the middle segment of the trajectory, as shown in the formula: , , , , , , , , Among them, represents the i th node of the planned path, represents the corresponding moment, represents the speed during the time period, which represents the time required to complete the curve transition and reach the specified speed at this node time period, represents the starting speed of the curve, represents the end position of the curve at the node, represents the end speed of the curve, represents the position during the time period, which represents the time taken from the node to the node. Among them, i= 2 j, k, l, m n- 1, represents the movement time of the middle section, represents the time of the linear uniform motion of this section; S23: According to the end point and the penultimate node in the preliminary planned path, use the linear segment function with parabolic transition to obtain the tail trajectory; In an exemplary embodiment, step S23 specifically includes: According to the end point and the penultimate node in the preliminary planned path, use the linear segment function with parabolic transition to obtain the tail trajectory, such as the formula: , , , , , where n represents the number of nodes, represents the end point, is the corresponding moment of the end point, represents the position at the and represent the speed at the and are the speeds during the time period, and represents the (n - 1)th node of the planned path, i.e., the penultimate node, represents the time required from the (n - 1)th node to the nth node, Indicates the position of the time period, indicating the moment corresponding to the (n - 1)-th node, indicating the time required for linear motion at a specified speed, indicating the time to decelerate to the end point ; S24: Obtain a preliminary planned trajectory according to the head segment trajectory, the middle segment trajectory, and the tail segment trajectory; S3: Obtain a final planned trajectory according to the preliminary planned trajectory and the motion state of the mobile robot by using a fifth-order polynomial; In an exemplary embodiment, step S3 specifically includes: obtaining a final planned trajectory according to the preliminary planned trajectory and the motion state of the mobile robot by using a fifth-order polynomial, as shown in the formula: , , , , , ,

[0015] , , , ,

[0016] , , , , , , , , , , , , Among them, is the position in the horizontal direction, is the position in the vertical direction, is the magnitude of the resultant velocity in the direction and is the velocity in the direction, is the velocity in the direction and the magnitude of the resultant acceleration in the is the acceleration in the direction, is , , , , , are respectively the fifth-order polynomial coefficients in the , , , , , are respectively the fifth-order polynomial coefficients in the is the motion state matrix, is the coefficient matrix, is the matrix form of the motion equation in the is the starting point position in the is the starting time, is the position in the direction at the starting time, is the velocity in the direction at the starting time, is the acceleration in the direction at the starting time, is the ending point position in the is the ending time, is the position in the direction at the ending time, is the velocity in the direction at the ending time, is the acceleration in the direction at the ending time, is the coefficient matrix, is the initial and final states of the transition section the matrix form of the motion equation in the is Direction starting point position is the starting moment Direction position is the starting moment Direction speed is the starting moment Direction acceleration is Direction end point position is the end moment Direction position is the end moment Direction speed is the end moment Direction acceleration is the mobile robot trajectory is the mobile robot speed is the magnitude of the resultant velocity is the velocity direction is the mobile robot acceleration is the magnitude of the resultant acceleration is the acceleration direction

[0017] In an exemplary embodiment, the mobile robot path planning method based on time series includes the following steps: The first part: First, complete the optimized path planning of the improved RRT algorithm; Motion planning consists of path planning and trajectory planning. The sequence of points or curves connecting the starting point position and the end point position is called a path, and the strategy for constructing the path is called path planning; Step 1: Assume that the starting point of the path to be planned is A and the end point is B; First, take point A as the starting root node and use the method of rapidly expanding the random tree of RRT to find a path 1 connecting obstacle avoidance nodes such as A, C, D, etc.; Step 2: Then take point B as the starting root node and use the method of rapidly expanding the random tree of RRT to find a path 2 connecting obstacle avoidance nodes such as B, F, H, etc.; Step 3: Then, based on the principle that the line segment between two points is the shortest, find the node with the shortest distance between two obstacles from path 1 and path 2. If the number of nodes is too large, repeat steps 1 and 2 until the final number of nodes does not exceed 12, such as A, C, H..., B; Connect A, C, H..., B in the order of proximity to complete the RRT path planning path 3; The second part: The improved RRT algorithm combines the LSPB function model and the motion constraints of the mobile robot; Since the path planned by the above RRT algorithm is a polyline path, the smoothness of the path, especially at the corners, still needs to be corrected and optimized; Divide path 3 into three parts: the first segment, the middle segment, and the last segment. The trajectory planning model based on the LSPB function (parabola - straight line - parabola) is asFigure 2 As shown in the figure; among them, the blue dashed part (the connecting node part, the transition section is optimized to a parabola) adopts a variable-speed motion of a fifth-order polynomial, and the solid line in the middle of the path nodes adopts a uniformly accelerated linear motion. The trajectory planning of each section of the mobile robot is combined through a time series to achieve a stable and continuous operation of the mobile robot's motion state; the LSPB function has the advantages of ensuring the speed, acceleration, and even the continuous and stable operation of the acceleration of the robotic arm's rotation angle, and is widely used in the trajectory planning of industrial robot manipulators, but is less used in the trajectory planning of mobile robots; in order to make the mobile robot meet the boundary conditions of the position, speed, and acceleration at the starting and ending points during trajectory planning, the planned path after RRT optimization is divided into three parts: the first section, the middle section, and the last section. The trajectory planning model based on the LSPB function is as Figure 2 shown; among them, the blue dashed part (the transition section) adopts a variable-speed motion of a fifth-order polynomial, and the solid line in the middle of the path nodes adopts a uniformly accelerated linear motion. The trajectory planning of each section of the mobile robot is combined through a time series to achieve a stable and continuous operation of the mobile robot's motion state; Step 4: Trajectory planning of the first section; For the first section, represents the starting point of the planned path, represents the corresponding moment, represents the time required for the mobile robot's speed to increase from 0 to , represents the time of uniform linear motion, represents the second node of the planned path and its corresponding moment is , from node to node, the total time required is ; The position at the moment of (1) The speed at the moment of (2) The speed equation for the time period of (3) The position equation for the time period of (4) The total motion time of the first section is: (5) At the start time and the end time of the first-section straight line, the corresponding start and end states are shown in Table 1: Table 1: Initial and final states of the first straight segment

[0018] Step 5: Intermediate segment path trajectory planning; For any intermediate segment, represents the i th node of the planned path, represents the corresponding time, represents the time required to complete the curve transition and reach the specified speed at this node represents the time of uniform motion of this straight segment, represents from node to node, where i= 2 j, k l、m n- 1; Starting position of the curve at the node: (6) Starting speed of the curve (7) Ending position of the curve at the node: (8) Ending speed of the curve: (9) The position equation for the time period is: (10) The speed equation for the time period is: (11) To ensure the effectiveness of the planned path and that the transition curves do not overlap, the following equation is satisfied: (12) The motion time of the intermediate segment is: (13) At any node of the intermediate curve, the start time and end time of the start of the curve correspond to the initial and final states shown in Table 2, where i represents the subscript of the planned path node, s represents the starting position of the transition curve, and e represents the ending position of the transition curve: Table 2: Initial and final states of the intermediate curve ​

[0019] Step 6: Trajectory planning for the tail section: For the tail section, represents the (n - 1)-th node of the planned path, represents the corresponding time, represents the time required for linear motion at the specified speed represents the time to decelerate to the end point of the end point The corresponding time of the end point is , represents the time required from the (n - 1)-th node to the n-th node; The position at time is: (14) The speed at time is: (15) The speed equation for the time period is: (16) The position equation for the time period is: (17) The total motion time of the tail section is: (18) At the start time and the end time of the straight line in the tail section, the initial and final states are shown in Table 3: Table 3: Initial and final states of the straight line in the tail section

[0020] Part 3: To ensure that the mobile robot meets the motion states at the start and end points of each motion plan and has the advantages of continuous and smooth operation of the speed, acceleration, and even jerk of the robotic arm rotation angle, a fifth-order polynomial is selected; the motion state of the mobile robot satisfies the following functional relationship (mobile robot state equation): .(19) The motion equation of the transition section in the X direction: (20) The motion equation of the transition section in the Y direction: (21) The initial and final states of the transition section, written in matrix form in the X direction: ​ (22) Among them, i is the subscript of the planned path node, , , , ,

[0021] The start and end states of the transition section in the Y direction are written in matrix form: (23) Among them, i is the subscript of the planned path node, , , ,

[0022] Time series of motion planning: (24) The trajectory equation of the mobile robot is as follows: (25) The speed equation of the mobile robot is as follows: (26) Among them, the magnitude of the resultant speed is: , Speed direction:

[0023] The acceleration equation of the mobile robot is as follows: (27) Among them, the magnitude of the resultant acceleration is: , Acceleration direction:

[0024] Curvature of the turning radius: (28) Among them: ; As an exemplary embodiment, the size of the simulation map is 500mm×500mm. Assume that the starting coordinates of the mobile robot are (50, 50), the ending coordinates are (450, 450), the initial speed is 0, the ending speed is 0, the starting and ending accelerations of the transition section are also 0, the target point threshold of the improved RRT algorithm is 40mm, and the maximum allowable expansion step size for search is 30mm. Set the time of each section of the planned path according to Equation (24). t i =20 s (i = 1, 2…n), t i（i+1） =20 s (i = 1, 2…n - 1); The comparison of the path planning effects of the three algorithms is as Figure 3 shown. The red dot in the upper left corner of the figure represents the starting point, the green dot in the lower right corner represents the target point, the red tree-like branches represent the expanded path of the RRT algorithm, the blue solid line represents the planned path found by the RRT algorithm, and the green solid line represents the optimized path obtained through LSPB smoothing processing; Figure 4 represents the position trajectories of each section of the mobile robot. The horizontal and vertical coordinates respectively represent the position of the mobile robot in the Figure X and Y directions. The dotted line in the figure represents the variable-speed motion using a fifth-degree polynomial, and the solid line represents the uniform linear motion. The first section of the motion trajectory starts from the starting point, and the last section of the motion trajectory ends at the ending point. The motion trajectory composed of the dotted line and the solid line is continuous and is Figure 3 consistent with the planned path of the algorithm proposed in; By planning the running time of each section of the trajectory, the displacement change of the mobile robot in the X and Y axis directions over time can be obtained. It can be seen from the figure that the displacements of the mobile robot in the X and Y axis directions both pass through the starting point (50, 50) and the ending point (450, 450), and the displacements are continuous; Therefore, it can be shown that the proposed LSPB function model can meet the requirements of the mobile robot for the continuity of the motion path in trajectory planning; The speeds of the mobile robot in the X and Y directions in each section of the planned path are as Figure 5 shown. At 0s, the initial speeds in the X and Y directions are 0, and at 160s, the speeds in the X and Y directions return to 0; The dotted line and the solid line in the figure respectively represent the speed change of the mobile robot in the X and Y directions along the corresponding path. It can be seen from the figure that the motion speeds in the X and Y directions can both remain continuous; Figure 6 represents the relationship between the speed of the mobile robot moving along the planned path and time. The speeds at the starting and ending states are also both 0, and the maximum speed during the entire motion process is less than 7mm / s, and the average speed remains at about 3mm / s; Figure 7 and Figure 8They are the acceleration in the X and Y directions and the combined acceleration of the mobile robot changing with time in each section of the planned path. It can be seen from the figure that the starting and ending accelerations of the acceleration in the X and Y directions and the combined acceleration are both 0, and the starting and ending accelerations of the intermediate transition section and the uniform linear section are also 0. Moreover, the acceleration in the X and Y directions and the combined acceleration can all be kept continuous, and the magnitude of the entire combined acceleration does not exceed 2 mm / s 2 ; This shows that the mobile robot meets the motion constraints of the mobile robot in each section of the trajectory planning, and the acceleration runs continuously and smoothly; The curvature of the planned path is as Figure 9 shown. At the nodes P 2 ~P 9 , the curvature of the transition curve reaches the maximum value at each corner. Among them, the curvature of 0.167 at the node P 3 is the largest in the entire planned path. Therefore, the minimum turning radius of the mobile robot in the entire motion planning is 5.99 mm. This minimum turning radius will also impose constraints on the motion state of the mobile robot, and in turn optimize the time series in the motion planning.

[0025] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned mobile robot path planning method based on time series are implemented. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; The storage medium can also include a combination of the above-mentioned types of memories.

[0026] This embodiment provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the above-mentioned mobile robot path planning method based on time series are implemented.

[0027] As Figure 10As shown, the computer device 120 may include: at least one processor 121, such as a Central Processing Unit (CPU), at least one communication interface 123, a memory 124, and at least one communication bus 122. Among them, the communication bus 122 is used to realize the connection and communication between these components. Among them, the communication interface 123 may include a display screen and a keyboard. Optionally, the communication interface 123 may further include a standard wired interface and a wireless interface. The memory 124 may be a high-speed random access memory (RAM), or a non-volatile memory, such as at least one disk memory. Optionally, the memory 124 may further be at least one storage device located far from the aforementioned processor 121. Among them, application programs are stored in the memory 124, and the processor 121 calls the program code stored in the memory 124 to execute any of the above method steps. Among them, the communication bus 122 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 122 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10It is represented by only one line in the figure, but it does not mean that there is only one bus or one type of bus. Among them, the memory 124 may include volatile memory, such as random-access memory (RAM); the memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD) or solid-state drive (SSD); the memory 124 may further include a combination of the above types of memories. Among them, the processor 121 may be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and an NP. Among them, the processor 121 may further include a hardware chip. The above hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof. Optionally, the memory 124 is further configured to store program instructions. The processor 121 may call the program instructions to implement the time-series-based mobile robot path planning method as in this embodiment.

[0028] This embodiment provides a computer program product, including a computer program, which implements the steps of the above time-series-based mobile robot path planning method when executed by a processor.

[0029] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the purpose of the present invention and the scope protected by the claims. All of these are within the protection scope of the present invention.

Claims

1. A mobile robot path planning method based on time series, characterized in that: The following steps are involved: S1: Based on the starting point and the end point, the initial planning path is obtained using the fast expansion random tree method; S2: according to the preliminary planned path, using a straight line segment function with a parabola transition, obtain a preliminary planned trajectory; S3: According to the preliminary planned trajectory and the motion state of the mobile robot, a fifth-order polynomial is used to obtain a final planned trajectory.

2. The mobile robot path planning method based on time series according to claim 1 is characterized in that: Step S1 specifically includes: S11: according to the starting point and the end point, taking the starting point as the starting root node, using the fast expansion random tree method to obtain the first path; S12: according to the starting point and the end point, taking the end point as the starting root node, using a fast expansion random tree method to obtain a second path; S13: According to the first path and the second path, in accordance with the principle of the shortest line segment between two points, find the nodes with the shortest distance between two obstacles, determine that the number of nodes with the shortest distance between two obstacles does not exceed the preset number of nodes, connect all the nodes with the shortest distance between two obstacles in the nearest order, and obtain a preliminary planned path.

3. The mobile robot path planning method based on time series according to claim 1 is characterized in that: Step S2 specifically includes: S21: according to the starting point and the second node in the preliminary planned path, the first segment trajectory is obtained by using the straight line segment function with parabola transition; S22: according to the preliminary planned path, using a straight line segment function with a parabola transition to obtain an intermediate segment trajectory; S23: according to the end point and the second to last node in the preliminary planned path, the tail segment trajectory is obtained by using a straight line segment function with a parabola transition; S24: Obtain a preliminary planned trajectory according to the first trajectory, the middle trajectory and the last trajectory.

4. The mobile robot path planning method based on time series according to claim 3 is characterized in that: Step S21 specifically includes: according to the starting point and the second node in the preliminary planned path, using the straight line segment function with parabola transition, obtaining the first segment trajectory, such as formula: ， ， ， ， ， in, Indicates the time it corresponds to, express The location at the moment, represents the starting point of the planned path, and express The speed of time, Indicates the speed of the mobile robot from 0 to Time required, It indicates that the first segment of uniform linear motion ends at The movement time of the node, and for The speed of the time period, It represents the second node of the planned path and its corresponding time is , for The location of the time period, For Node to The node is the total time required for the first segment of the motion path trajectory. Represents the time of uniform linear motion.

5. The mobile robot path planning method based on time series according to claim 3 is characterized in that: Step S22 specifically includes: according to the preliminary planned path, using the straight line segment function with parabola transition, obtaining the intermediate segment trajectory, such as formula: ， ， ， ， ， ， ， ， in, The planned path i nodes, express The corresponding moment, express The speed of the time period, Indicates that the curve transition is completed at this node and reaches the specified speed The time required, Indicates the starting speed of the curve, represents the end position of the curve at the node, Indicates the end speed of the curve, express The location of the time period, Indicates from Node to The time used by the node, where i= 2 j, k, l, m n- 1, Indicates the movement time of the middle segment, It represents the time of this section of uniform linear motion.

6. The mobile robot path planning method based on time series according to claim 3 is characterized in that: Step S23 specifically includes: according to the end point and the second to last node in the preliminary planned path, using the straight line segment function with parabola transition, to obtain the tail segment trajectory, such as formula: ， ， ， ， ， Where n represents the number of nodes. Indicates the end point, The time corresponding to the end point, express The location at the moment, and express The speed of time, and for The speed of the time period, Indicates the n-1th node of the planned path, which is the second to last node. It represents the time required from the n-1th node to the nth node. express The location of the time period, represents the time corresponding to the n-1th node, Indicates the specified speed The time required for linear motion, Indicates deceleration to the end point time.

7. The mobile robot path planning method based on time series according to claim 1 is characterized in that: Step S3 specifically includes: according to the preliminary planned trajectory and the motion state of the mobile robot, using a fifth-order polynomial, obtaining the final planned trajectory, such as formula: ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， ， in, is the horizontal position, is the vertical position, for Direction and Direction and velocity magnitude, for Direction speed, for Direction speed, for Direction and Direction and magnitude of acceleration, for Directional acceleration, for Directional acceleration, , , , , , They are Directional fifth-order polynomial coefficients, , , , , , They are Directional fifth-order polynomial coefficients, is the motion state matrix, is the coefficient matrix, for The matrix form of the equation of motion for the direction, for Direction starting point position, is the starting time, Starting time Direction position, Starting time Direction speed, Starting time Directional acceleration, for End position of direction, For the end moment, For the end time Direction position, For the end time Direction speed, For the end time Directional acceleration, is the coefficient matrix, The beginning and end states of the transition section The matrix form of the equation of motion for the direction is, for Direction starting point position, Starting time Direction position, Starting time Direction speed, Starting time Directional acceleration, for End position of direction, For the end time Direction position, For the end time Direction speed, For the end time Directional acceleration, is the trajectory of the mobile robot, is the mobile robot speed, is the combined velocity, is the velocity direction, is the mobile robot acceleration, is the magnitude of the combined acceleration, is the acceleration direction.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the time series-based mobile robot path planning method as described in any one of claims 1 to 7 are implemented.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the time series-based mobile robot path planning method as described in any one of claims 1-7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the time series-based mobile robot path planning method described in any one of claims 1 to 7 are implemented.

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