Mobile robot path planning method and system based on dual-region path combination

Abstract

The invention provides a mobile robot path planning method and system based on dual-region path combination, and belongs to the technical field of mobile robot path planning. Comprising the following steps: performing global path planning and generating a global optimal path by adopting an A * algorithm according to a selected target point; eliminating redundant turning points from the global optimal path by adopting a two-stage method, and smoothing the path; intercepting a local path initial trajectory from the global optimal path, sensing the surrounding environment in real time by using a laser radar, and performing local path planning based on a TEB algorithm; and circularly executing the planning process from the global path to the local path until the global target point is reached, and taking the path formed at the moment as the final planning path of the robot. According to the invention, while the search efficiency and the search precision are considered, the obstacle avoidance capability of the mobile robot in a dynamic environment is enhanced, and the collision risk is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of mobile robot path planning, and in particular relates to a mobile robot path planning method and system based on dual-region path combination. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the continuous advancement of industrial intelligence, mobile robots have been widely used in fields such as manufacturing, logistics, and healthcare, playing a significant role in improving production efficiency and quality of life. Therefore, the development of mobile robot technologies has become a key focus of research and industry. Path planning is a key technology for autonomous robot navigation. Path planning can be categorized into global and local path planning, depending on the degree of environmental information the robot acquires during path planning.

[0004] However, existing robot path planning methods, whether global or local, have some difficult-to-solve technical problems, such as: (1) Global path planning is to find a global path from a starting point to a destination point when the surrounding environment map is known. Currently, common algorithms include the standard A* algorithm, Dijkstra algorithm, ant colony algorithm, and RRT algorithm. However, with these methods, planning often fails when unknown obstacles appear in the environment. In addition, there are problems such as easy collision with obstacles, many traversed nodes, many turns in the path, and insufficient smoothness of the path.

[0005] (2) Local path planning is based on real-time sensor acquisition of information about unknown obstacles in the surrounding environment and dynamic obstacle avoidance. Currently, common algorithms include the artificial potential field method, the standard TEB algorithm, and the dynamic window method. However, under these methods, during the local path planning process, due to the lack of global prior information, only the local optimal trajectory can be planned; and there is a problem of sudden speed changes when passing through turning points, which leads to a high collision risk and poor obstacle avoidance effect in dynamic environments. Summary of the Invention

[0006] In order to overcome the above-mentioned shortcomings of the existing technology, the present invention provides a mobile robot path planning method and system based on the combination of dual-area paths, which can enhance the obstacle avoidance ability of the mobile robot in a dynamic environment and reduce the risk of collision while taking into account both search efficiency and search accuracy.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: A first aspect of the present invention provides a mobile robot path planning method based on dual-region path combination.

[0008] A mobile robot path planning method based on dual-region path combination includes: Constructing a grid map and selecting a target point from it; using the A* algorithm to perform global path planning based on the selected target point and generate a globally optimal path; wherein the A* algorithm introduces a dynamic domain expansion and a heuristic function that considers obstacle distribution and the current position of the node; A two-stage method is used to remove redundant turning points from the obtained global optimal path, and a path smoothing operation is performed on the global optimal path after removing the redundant turning points. The global optimal path after path smoothing is intercepted to obtain the initial trajectory of the local path; the surrounding environment is perceived in real time using the lidar, and local path planning is performed based on the TEB algorithm; the above planning process from global path to local path is executed cyclically until the global target point is reached, and the path formed at this time is used as the final planned path of the robot.

[0009] Furthermore, when the A* algorithm performs dynamic domain expansion, it first uses the angle between the current node and the target point as the screening basis to screen the expansion nodes; then, it determines whether there are obstacles in the horizontal and vertical directions of the current node to be expanded. If so, the expansion of the nodes adjacent to the obstacles above, below, and to the left and right is abandoned.

[0010] Furthermore, the A* algorithm performs weight adjustment based on the introduced heuristic function. Specifically: in the early stage of global path planning, path search is performed with search speed as the main condition; in the later stage of global path planning, path search is performed with search accuracy as the main condition; at the same time, the weight value is adjusted in combination with the current position of the node and the distribution of obstacles.

[0011] Furthermore, a two-stage method is adopted to remove redundant turning points from the global optimal path, including: first, executing the first stage of redundant turning point removal, that is, taking the global optimal path before the redundant nodes are removed as the original global path; starting from the second node in the original global path, traversing, and calculating the vertical distance from the current node to the previous retained point and the next node; determining whether to retain the current node based on the relationship between the obtained vertical distance and a preset distance threshold; and repeating the first stage of redundant turning point removal operation until the target point appears in the retained point set.

[0012] Furthermore, after the redundant turning point elimination operation of the first stage is completed, all the retained points are relabeled and represented as the original retained point set; then, the redundant turning point elimination operation of the second stage is executed, that is, reverse traversal is performed starting from the previous node of the target point in the original retained point set, and by comparing the distance to the nearest obstacle in the rectangular area corresponding to the current node under reverse traversal with the preset safety distance, it is determined whether to retain the current node; the redundant turning point elimination operation of the second stage is repeated until the target point appears in the new retained point set.

[0013] Furthermore, local path planning is performed based on the TEB algorithm, including: discretizing the initial trajectory of the local path to form an initial pose-time series that incorporates time information, and using lidar to perceive the surrounding environment in real time to construct a multi-objective optimization function; based on the constructed multi-objective optimization function, solving it through the G2O graph optimization framework to obtain the optimal pose-time series that meets the constraints.

[0014] Furthermore, the time interval of the initial trajectory of the local path is determined by the time resolution. At the same time, the Sigmoid function is introduced to adaptively adjust the time resolution parameter, that is, when a dynamic obstacle is detected, the time resolution is reduced.

[0015] A second aspect of the present invention provides a mobile robot path planning system based on dual-region path combination.

[0016] A mobile robot path planning system based on dual-region path combination includes: The global path planning module is configured to: construct a grid map and select a target point from it; perform global path planning based on the selected target point using the A* algorithm to generate a globally optimal path; wherein the A* algorithm introduces a dynamic domain expansion and a heuristic function that considers obstacle distribution and the current position of the node; The node processing module is configured to: remove redundant turning points from the obtained global optimal path using a two-stage method, and perform a path smoothing operation on the global optimal path after removing the redundant turning points; The local path planning module is configured to: intercept the global optimal path after path smoothing to obtain the initial trajectory of the local path; use the lidar to perceive the surrounding environment in real time and perform local path planning based on the TEB algorithm; and cyclically execute the above planning process from global path to local path until the global target point is reached, and the path formed at this time is used as the final planned path of the robot. A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the mobile robot path planning method based on dual-region path combination as described in the first aspect of the present invention.

[0017] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and runnable on the processor. When the processor executes the program, it implements the steps in the mobile robot path planning method based on dual-area path combination as described in the first aspect of the present invention.

[0018] One or more of the above technical solutions have the following beneficial effects: (1) The present invention adopts an improved A* algorithm for global path planning, namely: the A* algorithm introduces a heuristic function with dynamic domain expansion and consideration of obstacle distribution and the current position of the node. By discarding useless nodes through dynamic neighborhood expansion and abandoning high-risk expansion directions based on obstacle distribution, the risk of collision with obstacles can be reduced. By dynamically adjusting the weights through the heuristic function, the search speed is accelerated in the early stage and the path is optimized in the later stage, which can reduce the expansion of useless nodes and the number of traversed nodes. By adopting a two-stage method to eliminate redundant turning points, namely, in the first stage, redundant nodes are eliminated based on the vertical distance threshold and the obstacle safety distance, and in the second stage, segmentation points are inserted in reverse order and the safety distance is verified for further optimization, the number of turns and the number of path bends can be reduced. After eliminating redundant turning points, the segmented cubic Hermite interpolation curve is used for smoothing, which can improve the continuity and smoothness of the path, make the path more in line with the robot kinematic requirements, and thus solve the problem of insufficient path smoothness.

[0019] (2) The present invention intercepts the generated global optimal path as the initial trajectory of the local path, which can provide global prior information for the optimized TEB algorithm, thereby ensuring that the local planning is dynamically adjusted based on the global optimal path, avoiding falling into the local optimum, and solving the planning limitation problem caused by the lack of global information. The present invention adds an additional acceleration constraint function to the TEB algorithm. By limiting the acceleration change rate, it can improve the turning smoothness, reduce speed mutations, and thus reduce the risk of collision. In addition, by introducing an adaptive time resolution mechanism, the time resolution can be reduced when a dynamic obstacle is detected to improve the prediction accuracy, and the original resolution can be restored when there is no dynamic obstacle to balance the computational efficiency, thereby improving the obstacle avoidance effect in a dynamic environment.

[0020] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1This is a flow chart of a mobile robot path planning method based on dual-region path combination in Example 1 of the present invention.

[0023] Figure 2 Schematic diagram of the principle of the dynamic neighborhood expansion strategy in Example 1 of the present invention.

[0024] Figure 3 This is a schematic diagram of the principle of the security search direction in the first embodiment of the present invention.

[0025] Figure 4 This is a comparison diagram of the effect of eliminating path nodes in the first stage in the first embodiment of the present invention; wherein, Figure 4 (a) in the figure represents the trajectory diagram of the original path node. Figure 4 (b) in the figure shows the trajectory graph after the first stage of path node elimination.

[0026] Figure 5 This is a diagram showing the effect of eliminating path nodes in the second stage in the first embodiment of the present invention.

[0027] Figure 6 This is a schematic diagram of the principle of time resolution adjustment in Example 1 of the present invention.

[0028] Figure 7 This is a simulation diagram of the standard A* algorithm in the prior art in a multi-obstacle map environment.

[0029] Figure 8 Schematic diagram of simulation of the improved A* algorithm in a multi-obstacle map environment in Example 1 of the present invention.

[0030] Figure 9 : This is a schematic diagram comparing the actual trajectory simulations of the standard TEB algorithm and the improved TEB algorithm of the present invention in the first embodiment of the present invention under the condition of an inflection point; wherein, Figure 9 (a) shows the actual trajectory simulation diagram of the standard TEB algorithm. Figure 9 (b) in FIG. 1 is a schematic diagram of the actual trajectory simulation of the improved TEB algorithm according to the present invention.

[0031] Figure 10 1 is a schematic diagram showing a comparison of linear velocity simulations corresponding to the standard TEB algorithm in Example 1 of the present invention and the improved TEB algorithm of the present invention under the condition of an inflection point.

[0032] Figure 11 3 is a schematic diagram showing a comparison of angular velocity simulations corresponding to the standard TEB algorithm in Example 1 of the present invention and the improved TEB algorithm of the present invention under the condition of an inflection point.

[0033] Figure 12 This is a schematic diagram of the simulation of the dynamic obstacle avoidance effect of the fusion algorithm in the existing technology.

[0034] Figure 13 Schematic diagram of the simulation of the dynamic obstacle avoidance effect of the fusion algorithm in Example 1 of the present invention. DETAILED DESCRIPTION

[0035] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0036] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0037] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0038] The overall idea proposed by the present invention is as follows: The present invention provides a mobile robot path planning method based on the combination of dual-region paths. After selecting the target point, the method performs global path planning based on the improved A* algorithm, namely: quickly finding the global target point and generating the global optimal path through dynamic neighborhood expansion and heuristic function weight adjustment; according to the global optimal path, a two-stage method is used to eliminate redundant turning points; according to the eliminated turning points, a piecewise cubic Hermite curve is used to smooth the broken line path; the global path is intercepted as the initial trajectory of the local path, and the surrounding environment is perceived in real time by using a laser radar, and local path planning is performed based on the optimized TEB; the above steps are iterated repeatedly until the global target point is reached. The present invention can solve the problems of poor global path quality, easy collision of local paths with turning points, and poor obstacle avoidance in dynamic environments when planning for mobile robots in multi-obstacle scenarios, thereby improving the path planning performance.

[0039] Example 1 This embodiment discloses a mobile robot path planning method based on dual-region path combination.

[0040] like Figure 1 As shown, the mobile robot path planning method based on the combination of dual-region paths includes: Step S1: construct a grid map and select a target point from it; use the A* algorithm to perform global path planning based on the selected target point and generate a global optimal path; wherein the A* algorithm introduces a dynamic domain expansion and a heuristic function that considers obstacle distribution and the current position of the node; Step S2: using a two-stage method to remove redundant turning points from the obtained global optimal path, and performing a path smoothing operation on the global optimal path from which the redundant turning points have been removed; Step S3: intercept the global optimal path after path smoothing to obtain the initial trajectory of the local path; use the lidar to perceive the surrounding environment in real time, and perform local path planning based on the TEB algorithm; loop the above planning process from global path to local path until the global target point is reached, and use the path formed at this time as the final planned path of the robot.

[0041] Based on the above process, the present invention can enhance the obstacle avoidance capability of a mobile robot in a dynamic environment and reduce the risk of collision while balancing search efficiency and accuracy. To facilitate understanding of the technical solution of the present invention, the following further explains and illustrates the specific implementation methods of the technical solution of the present invention.

[0042] In step S1, a grid map is constructed and a target point is selected from it. The A* algorithm is used to perform global path planning based on the selected target point and generate a globally optimal path, thereby reducing the risk of collision with obstacles while improving search efficiency. The A* algorithm incorporates dynamic domain expansion and a heuristic function that considers obstacle distribution and the current location of the node.

[0043] The present invention improves the A* algorithm. That is, when the A* algorithm performs dynamic domain expansion, first, according to the angle between the line connecting the current node and the target point and the positive direction of the horizontal x-axis, some useless expansion nodes are discarded. The specific correspondence between the angle and the search direction node is shown in Table 1 and Figure 2 Determine whether there are obstacles in the horizontal and vertical directions of the current node to be expanded. If so, abandon the expansion of the nodes above, below, left and right of the obstacle to reduce the risk of collision between the mobile robot and the obstacle while ensuring the search efficiency. Figure 3 shown.

[0044] Table 1 Correspondence between different angles and extension nodes

[0045] When the A* algorithm adjusts the weights based on the introduced heuristic function, it takes the current position of the node and the distribution of obstacles into consideration. That is, it speeds up the search speed in the early stage of global path planning to improve planning efficiency; it slows down the search speed in the later stage to find the optimal path. At the same time, it further adjusts the weights based on the obstacle distribution between the current node and the target point to speed up the search speed.

[0046] The heuristic function weight adjustment and obstacle distribution calculation formulas can be expressed as: ; ; ; ; ; in, Indicates that the node is expanded from the starting point through the current node to be expanded The total cost of reaching the target point; Indicates the actual cost from the starting point to the current node to be expanded; 、 represents the proportional adjustment coefficient; Indicates the Euclidean distance ratio from the starting point to the current node to be expanded and the target point; represents a constant with a value of approximately 2.71, represents the obstacle distribution rate; Indicates the estimated cost from the current node to be expanded to the target point; 、 and Respectively represent the coordinates of the starting point, the current node to be expanded, and the target node; Representing coordinates The value function of Representing coordinates The corresponding cost value.

[0047] Determine whether the global target point has been searched. If it has been searched, go to step S2; if not, restart the global path planning.

[0048] In step S2, a two-stage method is used to remove redundant turning points from the obtained global optimal path to reduce the number of turning points; and a path smoothing operation is performed on the global optimal path without redundant turning points to further improve the path smoothness. This can be achieved by the following methods: Step S2-1: Use a two-stage method to eliminate redundant turning points from the global optimal path.

[0049] First, perform the first stage of eliminating redundant turning points, that is, the global optimal path before eliminating redundant nodes is used as the original global path; start traversing from the second node in the original global path, calculate the vertical distance from the current node to the previous retained point and the next node; determine whether to retain the current node based on the relationship between the obtained vertical distance and the preset distance threshold; repeat the first stage of eliminating redundant turning points until the target point appears in the set of retained points. Specifically: 1) Define a collection Represents the original global path node; where and Represent the starting point and target point respectively, and define a new set of reserved points Indicates the path nodes after the first stage of elimination, initially only including the starting point of the original global path .

[0050] 2) From the collection The second node Start traversal and calculate the current node To the last save point With the next node The straight line segment formed Vertical distance , the calculation formula can be expressed as: ; in, 、 and Represents the last retention point , current node and the next node coordinate.

[0051] 3) Determine vertical distance Is it greater than or equal to the preset distance threshold? If the vertical distance Greater than or equal to the preset distance threshold , indicating that the segment is too curved and cannot be skipped directly. Join the reserved point set ; If the vertical distance Less than the preset distance threshold , and the corresponding straight line segment To and The distance to the nearest obstacle in the rectangular area formed by the two points is greater than or equal to the preset safety distance , then the current node Not as a retention point, otherwise the node is used as a retention point and added to the retention point set .

[0052] 4) Repeat the above traversal steps until the target point Appear in the collection In the first stage, the elimination of path nodes is completed, and all retained points are re-marked according to the number of elements, that is, the set , the optimization results are as follows Figure 4 As shown. Figure 4 In the figure, a circle represents the starting point, a five-pointed star represents the target point, a black square represents an inaccessible area, a white square represents a accessible area, and a light gray square represents the nodes that have been searched during the search process; Figure 4 (a) in the figure represents the trajectory diagram of the original path node. Figure 4 (b) in the figure shows the trajectory diagram after the first stage of path node elimination. Figure 4As can be seen from (a) in the example, the original path has 13 nodes; Figure 4 As can be seen from (b), after the first stage of elimination, the path has 8 retained points (the dotted circle represents the preset safety distance , the dotted line segments represent the parts that are smaller than the preset safety distance and need to be discarded, and the remaining black segments are the results of the first stage optimization). It is not difficult to find that compared with the original path, after completing the elimination of the path nodes in the first stage, the nodes and corners have been significantly improved.

[0053] After the first phase of redundant turning point elimination is completed, all retained points are relabeled and represented as the original retained point set; then, the second phase of redundant turning point elimination is performed, which is to start from the previous node of the target point in the original retained point set and perform reverse traversal. By comparing the distance to the nearest obstacle in the rectangular area corresponding to the current node in the reverse traversal with the preset safety distance, it is determined whether to retain the current node; the second phase of redundant turning point elimination is repeated until the target point appears in the new retained point set. Specifically: 1) Define a new set of retention points Represents the path nodes after the second stage of elimination. Initially, it only contains the target points of the path after the first stage of elimination. .

[0054] 2) From the collection Target point The previous node Start reverse order traversal, at and The insertion step size is The calculation formula of the segmentation point can be expressed as: ; ; in, Represents a line segment On the segmentation points; and Represents nodes respectively and coordinate; Indicates the number of segmentation points inserted; Indicates the segmentation point step size.

[0055] 3) Follow Sequentially connect to the previous reservation point and segmentation points , determine the line segment To and Is the distance to the nearest obstacle within the rectangular area formed by the two points greater than or equal to the preset safety distance? If the distance is greater than or equal to the preset safety distance , then the segmentation point Not used as a retention point, otherwise it is a segmentation point The previous segment point As a retention point.

[0056] Repeat the above reverse traversal steps until the starting point Appear in the collection In the process, the second stage of path node elimination is completed, and all retained points are re-marked according to the number of elements, that is, the set , the optimization results are as follows Figure 5 Specifically, Figure 5 The dotted line segment in the figure represents the optimization result of the first stage. After the second stage optimization, a black line segment is formed. There are 5 retained points in this stage. It can be seen that compared with the optimization results of the first stage, the nodes and corners in the second stage have achieved further improvement.

[0057] Step S2-2: Based on the turning points after elimination, use the piecewise cubic Hermite curve to smooth the broken line path, that is, different from the original global path nodes, for the set of path nodes eliminated in the second stage As these nodes are discrete and unevenly distributed, piecewise cubic Hermite interpolation curve is used for smoothing to reduce the non-smoothness of the path.

[0058] In step S3, the smoothed global optimal path is intercepted to obtain the initial trajectory of the local path. The surrounding environment is perceived in real time using the LiDAR, and local path planning is performed based on the TEB algorithm. The above planning process from global path to local path is repeated until the global target point is reached. The path formed at this time is used as the final planned path of the robot to improve turning smoothness and dynamic obstacle avoidance. This can be achieved through the following methods: Step S3-1: Use the global path intercepted after interpolation processing as the initial trajectory of the local path, discretize the initial trajectory and form an initial pose-time sequence that incorporates time information. At the same time, use the lidar to perceive the surrounding environment in real time and construct a multi-objective optimization function.

[0059] The time interval between discrete trajectories is determined by the temporal resolution Decision, such as Figure 6As shown in the figure, the Sigmoid function is introduced to adaptively adjust the time resolution parameter. That is, when a dynamic obstacle is detected, the time resolution is reduced to improve the prediction accuracy, so as to avoid the problem of poor dynamic obstacle avoidance effect of the existing standard TEB algorithm. When there is no dynamic obstacle, the original resolution is restored to maintain computational efficiency. This can achieve a better balance between computing resources and obstacle avoidance effect. The calculation formula can be expressed as: ; in, Indicates the time resolution after adaptive adjustment; represents the Euclidean distance between the mobile robot and the dynamic obstacle, Indicates the distance setting threshold, represents the scaling factor, represents the Sigmoid function, Indicates the minimum value of dynamic adjustment, Indicates the maximum value of dynamic adjustment.

[0060] Step S3-2: In the multi-objective optimization function construction part, in order to improve the stability of movement and the smoothness of obstacle avoidance in local path planning, and at the same time reduce the risk of collision with corners in curved roads, an additional acceleration constraint function is added. The calculation formula can be expressed as: ; ; in, represents the linear acceleration, represents angular acceleration; Indicates linear velocity; Represents the time interval between two adjacent trajectory points; represents angular velocity; represents the linear acceleration constraint; Represents the angular acceleration constraint.

[0061] In step 3-3, the multi-objective optimization function with the jerk constraint function is solved using the G2O graph optimization framework to obtain the optimal pose-time sequence that satisfies the constraints. The robot is then judged to determine whether it has reached the global target point. If so, path planning ends; if not, the global path is re-intercepted and local path planning begins.

[0062] In order to highlight the significant progress of the present invention, this embodiment makes the following comparison, specifically: 1) Compare the improved A* algorithm of the present invention with the standard A* algorithm in the prior art.

[0063] The paths generated by the improved A* algorithm of the present invention are compared with those generated by the standard A* algorithm in the prior art. The simulation results are as follows: Figure 7 、 Figure 8 As shown in the figure, it can be seen that the path planned by the standard A* algorithm is prone to touching obstacles, and has many traversal nodes and turning points, and the path is not smooth enough; while the path planned by the improved A* algorithm of the present invention maintains a certain safe distance from obstacles, and has fewer traversal nodes and turning points, and a smooth path, which is more in line with the movement requirements of the mobile robot.

[0064] In this embodiment, each algorithm is executed 50 times, and the path length, search time, expanded nodes, total turning angle, and number of obstacle collisions of the algorithm are recorded. The experimental results are shown in Table 2.

[0065] Table 2 Comparison results between the present invention and the standard A* algorithm

[0066] As can be seen from Table 2, compared with the standard A* algorithm, the improved A* algorithm of the present invention reduces path length, search time, expanded nodes, total steering angle, and number of obstacle collisions by 1.76%, 53.51%, 75.72%, 63.28%, and 100%, respectively. This data shows that the present invention can significantly improve planning efficiency and global path planning quality.

[0067] 2) Compare the improved TEB algorithm of the present invention with the standard TEB algorithm in the prior art.

[0068] The paths generated by the improved TEB algorithm of the present invention are compared with the paths generated by the standard TEB algorithm in the prior art and the corresponding speed curves during the motion process. The simulation results are as follows: Figures 9-11 As shown, Figure 9 (a) shows the actual trajectory simulation diagram of the standard TEB algorithm. Figure 9 (b) shows a schematic diagram of the actual trajectory simulation of the improved TEB algorithm of the present invention; Figure 10 and Figure 11 The following are simulation comparison diagrams of the linear velocity and angular velocity corresponding to the standard TEB algorithm and the improved TEB algorithm of the present invention under the condition of inflection point. Figures 9-11 As can be seen from the figure, the standard TEB algorithm cannot adjust the posture in time due to speed fluctuations when passing the inflection point, which easily leads to the risk of collision with the inflection point. The improved TEB algorithm of the present invention can reduce such fluctuations by taking into account the acceleration constraint, and safely pass the inflection point in a smoother way. That is, the present invention has more advantages in terms of trajectory smoothness, linear velocity, angular velocity, etc.

[0069] 3) Compare the fusion algorithm of the present invention with the fusion algorithm in the prior art.

[0070] The fusion algorithm of the present invention is compared with the fusion algorithm in the prior art. The simulation results are as follows: Figure 12 、 Figure 13 As shown, it can be seen that when facing an oncoming pedestrian, the fusion algorithm in the existing technology (standard A* algorithm fused with standard TEB algorithm) is limited by the time resolution. Although it can complete dynamic obstacle avoidance, it can be seen from the actual trajectory that the effect is poor; the fusion algorithm of the present invention (improved A* algorithm fused with improved TEB algorithm) can improve the dynamic obstacle avoidance effect due to the introduction of adaptive time resolution adjustment, further ensuring the smooth completion of the mobile robot's autonomous navigation task.

[0071] Example 2 This embodiment discloses a mobile robot path planning system based on dual-region path combination.

[0072] A mobile robot path planning system based on dual-region path combination includes: The global path planning module is configured to: construct a grid map and select a target point from it; perform global path planning based on the selected target point using the A* algorithm to generate a globally optimal path; wherein the A* algorithm introduces a dynamic domain expansion and a heuristic function that considers obstacle distribution and the current position of the node; The node processing module is configured to: remove redundant turning points from the obtained global optimal path using a two-stage method, and perform a path smoothing operation on the global optimal path after removing the redundant turning points; The local path planning module is configured to: intercept the global optimal path after path smoothing to obtain the initial trajectory of the local path; use the lidar to perceive the surrounding environment in real time and perform local path planning based on the TEB algorithm; and cyclically execute the above planning process from global path to local path until the global target point is reached, and the path formed at this time is used as the final planned path of the robot. Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0073] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the mobile robot path planning method based on dual-region path combination as described in the first embodiment of the present disclosure.

[0074] Example 4 The purpose of this embodiment is to provide an electronic device.

[0075] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the mobile robot path planning method based on dual-region path combination as described in the first embodiment of the present disclosure are implemented.

[0076] The steps involved in the apparatuses of Examples 2, 3, and 4 above correspond to those of Method Example 1. For detailed implementations, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0077] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0078] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A mobile robot path planning method based on dual-region path combination, characterized in that: include: Construct a grid map and select target points from it; The A* algorithm is used to perform global path planning based on the selected target point and generate a globally optimal path; wherein the A* algorithm introduces a heuristic function with dynamic domain expansion and consideration of obstacle distribution and the current position of the node; A two-stage method is used to remove redundant turning points from the obtained global optimal path, and a path smoothing operation is performed on the global optimal path after removing the redundant turning points. The global optimal path after path smoothing is intercepted to obtain the initial trajectory of the local path; the surrounding environment is perceived in real time using the lidar, and local path planning is performed based on the TEB algorithm; the above planning process from global path to local path is executed cyclically until the global target point is reached, and the path formed at this time is used as the final planned path of the robot.

2. The mobile robot path planning method based on dual-region path combination according to claim 1, characterized in that: When performing dynamic domain expansion, the A* algorithm first uses the angle between the current node and the target point as the screening basis to screen the expansion nodes; then, it determines whether there are obstacles in the horizontal and vertical directions of the current node to be expanded. If so, the expansion of the nodes adjacent to the obstacles above, below, and to the left and right is abandoned.

3. The mobile robot path planning method based on dual-region path combination according to claim 1, characterized in that: The A* algorithm performs weight adjustment based on the introduced heuristic function. Specifically, in the early stage of global path planning, path search is performed with search speed as the main condition; in the later stage of global path planning, path search is performed with search accuracy as the main condition; at the same time, the weight value is adjusted in combination with the current position of the node and the distribution of obstacles.

4. The mobile robot path planning method based on dual-region path combination according to claim 1, characterized in that: A two-stage method is adopted to remove redundant turning points from the global optimal path, including: first, performing the first stage of redundant turning point removal, that is, taking the global optimal path before redundant nodes are removed as the original global path; starting from the second node in the original global path, traversing, and calculating the vertical distance from the current node to the previous retained point and the next node; determining whether to retain the current node based on the relationship between the obtained vertical distance and a preset distance threshold; and repeating the first stage of redundant turning point removal operation until the target point appears in the set of retained points.

5. The mobile robot path planning method based on dual-region path combination according to claim 4, characterized in that: After the first stage of redundant turning point elimination is completed, all retained points are relabeled and represented as the original retained point set; Subsequently, the second stage of redundant turning point elimination is performed, that is, reverse traversal is performed starting from the node before the target point in the original retained point set, and by comparing the distance to the nearest obstacle in the rectangular area corresponding to the current node in the reverse traversal with the preset safety distance, it is determined whether to retain the current node; Repeat the redundant turning point elimination operation in the second stage until the target point appears in the new set of retained points.

6. The mobile robot path planning method based on dual-region path combination according to claim 1, characterized in that: Local path planning is performed based on the TEB algorithm, including: discretizing the initial trajectory of the local path to form an initial pose-time series that incorporates time information, while using lidar to perceive the surrounding environment in real time to construct a multi-objective optimization function; based on the constructed multi-objective optimization function, solving it through the G2O graph optimization framework to obtain the optimal pose-time series that meets the constraints.

7. The mobile robot path planning method based on dual-region path combination according to claim 6, characterized in that: The time interval of the initial trajectory of the local path is determined by the time resolution. At the same time, the Sigmoid function is introduced to adaptively adjust the time resolution parameter, that is, when a dynamic obstacle is detected, the time resolution is reduced.

8. A mobile robot path planning system based on dual-region path combination is characterized by: include: The global path planning module is configured to: construct a grid map and select a target point from it; The A* algorithm is used to perform global path planning based on the selected target point and generate a globally optimal path; wherein the A* algorithm introduces a heuristic function with dynamic domain expansion and consideration of obstacle distribution and the current position of the node; The node processing module is configured to: remove redundant turning points from the obtained global optimal path using a two-stage method, and perform a path smoothing operation on the global optimal path after removing the redundant turning points; The local path planning module is configured to: intercept the global optimal path after path smoothing to obtain the initial trajectory of the local path; use the lidar to perceive the surrounding environment in real time and perform local path planning based on the TEB algorithm; and cyclically execute the above planning process from global path to local path until the global target point is reached, and the path formed at this time is used as the final planned path of the robot.

9. 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 path planning method based on dual-region path combination as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps in the mobile robot path planning method based on dual-region path combination as described in any one of claims 1 to 7 are implemented.

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