Global scan based mobile robot path planning method, device and medium

Abstract

The application relates to the technical field of industrial robot path planning, in particular to a mobile robot path planning method, equipment and medium based on global scanning, wherein the method comprises the following steps: acquiring environment data, and performing environment modeling through a grid method to determine free space grids and obstacle grids; in the determined environment model, an ant colony system based on global scanning is used to search a collision-free path from a starting point to an ending point; and based on the searched collision-free path, mobile robot path planning is performed. The application can adapt to complex environments and realize efficient and stable mobile robot path planning.

Description

Technical Field

[0001] This invention relates to the field of industrial robot path planning technology, and in particular to a method, device and medium for mobile robot path planning based on global scanning. Background Technology

[0002] The mobile robot path planning problem can be described as finding a collision-free path from the starting point to the destination. Generally, the shorter the planning time and the shorter and smoother the planned path, the better the path planning method.

[0003] Ant colony optimization is often used to solve path planning problems for mobile robots. However, traditional ant colony optimization methods suffer from slow convergence speed, are prone to getting trapped in local optima, have poor stability, and are not very adaptable to various complex maps, which limits the effectiveness of mobile robot path planning. Summary of the Invention

[0004] To address the problem that traditional ant colony-based mobile robot path planning methods are difficult to apply to complex environments, this invention provides a global scanning-based mobile robot path planning method, electronic device, and storage medium, which also has good adaptability to complex maps.

[0005] In a first aspect, embodiments of the present invention provide a mobile robot path planning method based on global scanning, comprising:

[0006] Acquire environmental data and perform environmental modeling using the raster method to determine the free space raster and obstacle raster;

[0007] In a defined environment model, a collision-free path from the starting point to the ending point is searched using an ant colony system based on global scanning; wherein, before selecting the next node, the ant colony system based on global scanning uses a global scanning method to determine the global field of view of the current node.

[0008] Based on the searched collision-free path, the mobile robot path planning is performed.

[0009] Optionally, the step of determining the global field of view of the current node using a global scan includes:

[0010] Using the geometric midpoint of the current node as the center, a rotational scan is performed using a straight line passing through this center to determine the centers of all free space grids swept by the straight line before it encounters an obstacle grid. The free space grids whose centers are swept by the straight line are taken as the global view of the current node.

[0011] Optionally, the step of using a global scanning-based ant colony system to search for a collision-free path from the starting point to the ending point includes:

[0012] Determine the initial parameters required for the ant colony method;

[0013] Set a preset number of ants at the starting point to begin this round of searching;

[0014] For each ant, the next node is selected from the global view of the current node using a roulette wheel method, and the tabu list is updated.

[0015] Determine if the current search round is complete. If yes, continue to the next step; otherwise, return to the previous step.

[0016] Perform a global pheromone update;

[0017] Determine if the search round has reached the maximum number of iterations. If so, output the current optimal path; otherwise, return to execute the step of setting the preset number of ants at the starting point and start a new round of search.

[0018] Optionally, the initial parameters required for the ant colony determination method include determining the initial pheromone distribution in the following manner:

[0019] The probability of a stimulus is determined based on the number of obstacle grids surrounding each grid in the environmental model.

[0020] Based on the stimulus probabilities and the positions of the start and end points in the environmental model, the initial pheromone matrix is ​​preliminarily determined.

[0021] The initial pheromone matrix is ​​preliminarily determined, and pheromone enhancement is performed on the connecting path; the connecting path is all the grids traversed by the line from the starting point to the ending point.

[0022] The initial pheromone matrix after path pheromone enhancement is then subjected to endpoint pheromone enhancement to obtain the final initial pheromone matrix; the endpoint pheromone enhancement includes incremental radiative enhancement centered on the endpoint.

[0023] Optionally, selecting the next node from the global view of the current node includes:

[0024] Using a random function, either a state transition rule or a distance-factor direction selection rule is used to determine the next node in the current node's global view. The expression is as follows:

[0025]

[0026] Where S represents the node selection function, i represents the current node, j represents a candidate node in the global view of the current node i, and τ ij (k) represents the pheromone between the current node i and the candidate node j in the k-th round of the search, η ijLet α represent the distance heuristic factor between the current node i and the candidate node j, α represent the weight coefficient of the pheromone concentration, and β represent the weight coefficient of the heuristic function. Let represent the state transition probability selection function between the current node i and the candidate node j in the k-th round of search; q0∈(0,1) is a preset constant, and q represents a random function between 0 and 1;

[0027] Distance heuristic factor η ij The expression is

[0028]

[0029] d si d represents the distance from the starting point s to the current node i. ij Let d represent the distance from the current node i to the candidate node j. je Q represents the distance from candidate node j to the endpoint e. L This is a preset constant.

[0030] Optionally, the selection of the next node from the global view of the current node uses adaptive weight coefficients, expressed as follows:

[0031]

[0032]

[0033] Where α represents the weight coefficient of pheromone concentration, β represents the weight coefficient of heuristic function, both of which are adaptive functions related to the current search round k, and Q1, Q2, q1, C1, C2, k0 are preset constants.

[0034] Optionally, the step of selecting the next node from the global view of the current node adopts a state transition probability selection function incorporating a directional constraint function, the expression of which is:

[0035]

[0036] Where Allowed(k) represents the set of candidate nodes for the current node i in the k-th round of search. The directional constraint function from the current node i to the candidate node j is expressed as:

[0037]

[0038] This represents the direction vector from the current node i to the candidate node j. Q represents the direction vector from the current node i to the endpoint e, and Q is a preset constant.

[0039] Optionally, selecting the next node from the global view of the current node further includes:

[0040] If the global view of any ant's current node is empty, then remove the current node from the ant's tabu list, set it as an obstacle grid, and make the ant return to the previous node and select the next node again.

[0041] Secondly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the mobile robot path planning method described in any embodiment of this specification.

[0042] Thirdly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the mobile robot path planning method described in any embodiment of this specification.

[0043] This invention provides a mobile robot path planning method, electronic device, and storage medium based on global scanning. In the process of searching for a collision-free path from the starting point to the ending point using an ant colony system, this invention adopts a global scanning method to determine the global field of view of the current node, and then searches for the next node to be moved by the ant in the global field of view. This can speed up the convergence speed, avoid getting trapped in local optima, and has good adaptability to complex maps. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic flowchart of a mobile robot path planning method based on global scanning provided in an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the process of searching for a collision-free path from the starting point to the ending point using an ant colony system based on global scanning, according to an embodiment of the present invention.

[0047] Figure 3 This is a schematic diagram of an environment model provided in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of a global scanning strategy provided in an embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of directional constraints provided in an embodiment of the present invention;

[0050] Figure 6 This is a schematic diagram of a rollback strategy provided in an embodiment of the present invention;

[0051] Figure 7(a) is a schematic diagram of the planning result of the path planning method provided by an embodiment of the present invention in scenario 1;

[0052] Figure 7(b) is a schematic diagram of the planning result of the path planning method provided by an embodiment of the present invention in scenario 2;

[0053] Figure 7(c) is a schematic diagram of the planning result of the path planning method provided by an embodiment of the present invention in scenario 3;

[0054] Figure 7(d) is a schematic diagram of the planning result of the path planning method provided by an embodiment of the present invention in scenario 4;

[0055] Figure 8 This is a comparison chart of the convergence curve trends of the path planning method provided in an embodiment of the present invention and some existing methods;

[0056] Figure 9(a) is a stability comparison diagram of the path planning method provided by an embodiment of the present invention and some existing methods in scenario 1;

[0057] Figure 9(b) is a stability comparison diagram of the path planning method provided by an embodiment of the present invention and some existing methods in scenario 2;

[0058] Figure 9(c) is a stability comparison diagram of the path planning method provided by an embodiment of the present invention and some existing methods in scenario 3;

[0059] Figure 9(d) is a stability comparison diagram of the path planning method provided by an embodiment of the present invention and some existing methods in scenario 4. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0061] As mentioned earlier, traditional ant colony optimization methods suffer from slow convergence, susceptibility to local optima, poor stability, and limited adaptability to complex maps, thus restricting the effectiveness of mobile robot path planning. Therefore, this invention proposes a method for mobile robot path planning using a global scanning-based ant colony system, which also exhibits good adaptability to complex maps.

[0062] The specific implementation of the above concept is described below.

[0063] Please refer to Figures 1 to 9(d) This invention provides a mobile robot path planning method based on global scanning, the method comprising:

[0064] Step 100: Acquire environmental data and perform environmental modeling using the grid method to determine the free space grid and obstacle grid;

[0065] Step 200: In the defined environment model, use a global scanning-based ant colony system to search for a collision-free path from the starting point to the ending point.

[0066] In the global scanning-based ant colony system, a global scan is used to determine the global field of view of the current node before selecting the next node; each node (i.e., grid) within the global field of view may become the next node.

[0067] Step 300: Based on the searched collision-free path, perform mobile robot path planning.

[0068] In this embodiment of the invention, a global scanning-based ant colony system is used for mobile robot path planning. For each ant in each iteration, a global scan is used to determine the global field of view of the current node where it is located. Then, the next node for each ant to move is searched in the corresponding global field of view. This breaks the limitations of step size and selection direction, has good adaptability to complex maps, and can improve the path planning processing speed and avoid getting trapped in local optima.

[0069] The following description Figure 1 The execution method of each step is shown.

[0070] For step 100, environmental modeling is performed using the raster method. For a two-dimensional map, the following can be obtained: Figure 3 The environmental model shown divides the environment into equally sized grids. Black grids represent obstacle grids, which are the spaces that the mobile robot needs to avoid. White grids represent free space grids, which are the spaces that the mobile robot can pass through.

[0071] In a two-dimensional plane, the correspondence between grid number and coordinates is expressed as follows:

[0072]

[0073] Here, mod() and int() represent the modulo and integer functions, respectively, x i The x-coordinate of the center point of the grid cell numbered i is represented by y. i This represents the ordinate of the center point of the grid cell numbered i.

[0074] Optionally, for step 200, a global scan is used to determine the global field of view of the current node, including:

[0075] Using the geometric midpoint of the current node as the center, a rotational scan is performed using a straight line passing through this center to determine the centers of all free space grids swept by the straight line before it encounters an obstacle grid. The free space grids whose centers are swept by the straight line are taken as the global field of view of the current node, i.e., the range that the next node can reach.

[0076] Figure 4 This is a schematic diagram of a global scanning strategy provided in an embodiment of the present invention. Figure 4 In this invention, the black grid represents obstacle grids, the dark gray grid represents the current node (i.e., the grid where the ant is currently located), and the light gray grid represents the free space grid formed by rotating a straight line passing through the geometric center of the current node without encountering any obstacles and sweeping across the center point; this represents the global field of view of the current node. In this embodiment, the set of blank grids swept across the geometric center by a straight line constitutes the set of reachable points for the current node's next step. This eliminates the limitations on the step size and direction of ant movement in traditional ant colony methods, allowing the ant's field of view to be adjusted in a timely manner according to map changes, thus improving the map adaptability and convergence speed of the path planning method.

[0077] Optionally, such as Figure 2 As shown, for step 200, an ant colony system based on global scanning is used to search for a collision-free path from the starting point to the ending point, including:

[0078] Step 200-0: Determine the initial parameters required for the ant colony method;

[0079] Ant colony system (ACS) is an improved ant colony method. The initial parameters required by the ant colony method include the initial pheromone distribution, distance heuristic factor and weight coefficients, etc.

[0080] Step 200-2: Set the preset number of ants at the starting point and begin this round of search;

[0081] Step 200-4: For each ant, select the next node from the global view of the current node using the roulette wheel method, and update the tabu table.

[0082] The taboo list is used to record the nodes that the ants have visited;

[0083] Step 200-6: Determine whether the current round of search is complete. If yes, continue to the next step, i.e., step 200-8; otherwise, return to the previous step, i.e., step 200-4.

[0084] Step 200-8: Perform a global pheromone update;

[0085] Step 200-10: Determine if the search round has reached the maximum number of iterations. If yes, output the current optimal path. Otherwise, return to the step of setting the preset number of ants at the starting point, that is, return to step 200-2 and start a new round of search.

[0086] Using the above embodiments, iterative search can be performed to find the optimal path. The criteria for judging the optimal path can be found in existing technologies and will not be further limited here. The determination of whether a round of search is complete in step 200-6 can also be found in existing technologies.

[0087] Optionally, for step 200-0, the initial parameters required for the ant colony method are determined, including determining the initial pheromone distribution in the following manner:

[0088] Step 200-0-0: Determine the stimulus probability based on the number of obstacle grids surrounding each grid in the environment model;

[0089] Step 200-0-2: Based on the stimulus probability and the positions of the start and end points in the environmental model, the initial pheromone matrix is ​​preliminarily determined;

[0090] Step 200-0-4: Perform pheromone enhancement on the initially determined initial pheromone matrix using connecting paths; the connecting path is all the grids traversed by the line connecting the starting point to the ending point.

[0091] Step 200-0-6: After enhancing the initial pheromone matrix through the connection path pheromone enhancement, perform endpoint pheromone enhancement to obtain the final initial pheromone matrix; the endpoint pheromone enhancement includes performing incremental radiative decreasing enhancement with the endpoint as the center.

[0092] The above embodiments introduce an initial pheromone uneven distribution based on stimulus probability and location information to avoid blindness in the initial search.

[0093] For step 200-0-0, for a two-dimensional map, the stimulus probability is determined based on the obstacle grid in the environment model, expressed as:

[0094]

[0095] in, Both are intermediate variables, δ sp(i) represents the stimulus probability of the current node i, 8 represents the eight possible directions for movement around the current node i, and N obs This represents the number of obstacle grid cells surrounding the current node i, 8-N. obs -1 indicates the remaining possible directions for the ant at the current node i after removing the obstacle grid and the previous node. The stimulus probability is essentially an indicator of the number of obstacles around a node; the more obstacles there are, the lower the stimulus probability, and the less attractive the node is to the ant.

[0096] For step 200-0-2, the initial pheromone matrix is ​​initially determined, and its expression is:

[0097]

[0098] Where, τ ij (0) represents the initial pheromone matrix, D, initially determined based on the stimulus probabilities and the positions of the start and end points in the environmental model. ie D represents the distance function from the current node i to the destination e, which is determined based on the positions of the current node i and the destination e in the environment model. si represents the distance function from the initial point s to the current node i, which is determined based on the positions of the initial point s and the current node i in the environment model, and j represents the candidate node.

[0099] In addition to introducing stimulus probabilities, this invention also applies a certain initial pheromone to the grid traversed by the line connecting the start and end points—that is, to the theoretically optimal path without obstructions—to accelerate path optimization, i.e., step 200-0-4. This step 200-0-4 enhances the pheromone of the connecting path, which can be done using a constant enhancement, i.e., within the aforementioned τ... ij Based on (0), a certain amount of pheromone is added to all the grids that the line connecting the start point and the end point passes through.

[0100] Because the principle of stimulus probability neglects the initial pheromone assignment at the edges of the grid map, selecting edge grids as endpoints can easily lead to ants repeatedly circling around the endpoint. This invention addresses this problem by artificially strengthening the initial pheromone of grids near the endpoint using a radially decreasing initial pheromone allocation method. Step 200-0-6 assigns pheromone values ​​to the endpoint and its surrounding grids using a radially decreasing method. This step enhances the endpoint pheromone using an incremental radially decreasing enhancement method, radiating outwards from the endpoint with decreasing pheromone increments. For example, the pheromone at the endpoint increases by F (where F is a positive number), the pheromone at a neighboring point one grid away from the endpoint increases by F / 2, and the pheromone at a node two grids away from the endpoint increases by F / 4, and so on. This endpoint pheromone enhancement effectively solves the problem of ants repeatedly circling around the endpoint, improving computational efficiency.

[0101] Optionally, for step 200-4, selecting the next node from the global view of the current node includes:

[0102] Using a random function, either a state transition rule or a distance-factor direction selection rule is used to determine the next node in the current node's global view. The expression is as follows:

[0103]

[0104] Where S represents the node selection function, i represents the current node, j represents a candidate node in the global view of the current node i, and τ ij (k) represents the pheromone between the current node i and the candidate node j in the k-th round of the search, η ij Let α represent the distance heuristic factor between the current node i and the candidate node j, α represent the weighting coefficient of the pheromone concentration, and β represent the weighting coefficient of the heuristic function; (τ ij (k)) α That is, τ ij (k) raised to the power of α, (η) ij ) β Indicates η ij beta power; Let q0η(0,1) be the state transition probability selection function between the current node i and the candidate node j in the k-th round of search; q0η(0,1) is a preset constant, where q represents a random function between 0 and 1. When q≥q0, the state transition rule is used; otherwise, the distance factor direction selection rule is used.

[0105] Distance heuristic factor η ij The expression is

[0106]

[0107] d si d represents the distance from the starting point s to the current node i. ij Let d represent the distance from the current node i to the candidate node j. je Q represents the distance from candidate node j to the endpoint e; L It is a preset constant that can be obtained based on experience or through repeated adjustments.

[0108] Compared to the previous state transition rules, the introduced distance-factor direction selection rule effectively shortens the optimal path and time, demonstrating certain advantages. Its principle is to use a random function to alternate between two rules, which is also one of the theoretical elements of a typical ant system (ACS).

[0109] Traditional ant colony optimization methods use a distance heuristic factor that depends only on the distance between the candidate nodes and the destination. If the distances from the candidate nodes to the destination are not significantly different, it cannot provide good guidance for the ant's choice of the next node. Therefore, this invention addresses this problem by improving the distance heuristic factor, making it related not only to the destination but also to the starting point and the current node. The improved scheme tends to guide ants to move along a straight line from the starting point to the destination, where the distance heuristic factor is maximized. Therefore, in roulette wheel optimization, ants have a greater probability of choosing this direction.

[0110] Optionally, for step 200-4, the next node is selected from the global view of the current node, using adaptive weight coefficients, expressed as:

[0111]

[0112]

[0113] Where α represents the weighting coefficient of pheromone concentration, β represents the weighting coefficient of heuristic function, both of which are adaptive functions related to the current search round k; Q1, Q2, q1, C1, C2, k0 are all preset constants, which can be obtained based on experience or through repeated adjustments.

[0114] In the early stages of the search path, the greater the weight of pheromone concentration, the faster the ant can find the optimal path from previous iterations. In the later stages of the search path, the greater the weight of the distance heuristic factor, the faster the convergence speed. Since fixed-value weight coefficients cannot effectively reflect the changes in the importance of pheromone concentration and distance heuristic factor at different stages of the ant's search path, the above embodiments of the present invention use adaptive function weight coefficients. The weight coefficients α and β can be adjusted in a timely manner according to the degree of ant search and the distance from the destination, thereby accelerating the convergence speed and avoiding getting trapped in local optima.

[0115] Optionally, for step 200-4, the next node is selected from the global view of the current node using a state transition probability selection function incorporating a directional constraint function, the expression of which is:

[0116]

[0117] Where Allowed(k) represents the set of candidate nodes for the current node i in the k-th round of search, that is, the global vision of the current node i in the k-th round of search. The directional constraint function from the current node i to the candidate node j is expressed as:

[0118]

[0119] This represents the direction vector from the current node i to the candidate node j. This represents the direction vector from the current node i to the endpoint e; Q is a preset constant, which can be obtained based on experience or through repeated adjustments. The direction constraint function increases the probability of exploring the area near the endpoint. In some cases, such as when the ant's search path gets deadlocked, the ant can still explore other areas again without compromising the integrity of the valid information.

[0120] Considering that this invention improves the ant's next node selection mechanism through global scanning, the number of possible nodes for the ant is significantly increased, and the differences between adjacent possible nodes are weakened. This can easily lead to blind selection by the ant. Therefore, to accelerate convergence, the above embodiments of this invention add a directional constraint function to the state transition probability selection function. Using directional constraints can improve the blindness of the ant colony's search and accelerate the convergence speed. Figure 5 As shown, when an ant moves from the starting point to the ending point, where A and B are the ant's next possible nodes, due to the difference between angle 1 and angle 2, based on the directional constraint, we can determine the node that the ant is more likely to choose.

[0121] Optionally, for step 200-4, selecting the next node from the global view of the current node further includes:

[0122] If the global view of any ant's current node is empty, meaning it cannot select the next node, then the current node is removed from the ant's tabu list, the current node is set as an obstacle grid, and the ant is made to retreat to its previous node. The next node is then selected for the ant from the global view of its previous node.

[0123] Ants often encounter situations where they enter a U-shaped trap or are surrounded by obstacles, making it impossible to choose the next node. To address this problem, the above embodiments of the present invention introduce a retreat strategy, allowing ants to return to the previous node when trapped in a U-shaped trap. For example... Figure 6 As shown, when an ant reaches grid number 10 (i.e., node 10), it finds no suitable next node. In this case, it needs to retreat to the previous node, choose a suitable next node, and remove grid number 10 from the taboo list, renaming it an obstacle grid to prevent other ants from reaching it. If, after retreating to the previous node (e.g., grid number 7), there is still no reachable node, the retreat continues. This retreat strategy greatly enhances the search ability of each ant, significantly improving the ant colony's ability to cope with complex environments.

[0124] In summary, the mobile robot path planning method provided by this invention introduces a distance-factor direction selection rule to improve the shortcomings of the roulette wheel method; improves the distance heuristic factor to make it easier to converge in the later stages; adopts adaptive weight coefficients, and the weight coefficients and distance heuristic factor can be adjusted in a timely manner according to the degree of ant search and the distance to the destination to avoid getting trapped in local optima; introduces stimulus probabilities and improves their shortcomings to better adapt to the method of this invention; unevenly distributes the initial pheromone according to the number of surrounding obstacles to avoid blindness in the early stage of search; adopts a distance-factor direction selection rule to improve the original single state transition rule; adopts global visual exploration so that the feasible domain of the ant is no longer limited to the surrounding nodes and has good adaptability to complex maps; adopts an orientation angle constraint function to filter out too many nodes with low distinguishability in the ant's field of vision, reducing the blindness of selection caused by the increase of optional nodes; finally, a backoff strategy is introduced to prevent the ant from getting trapped in deadlock and improve the efficiency of each ant.

[0125] To verify the mobile robot path planning method provided by this invention, such as... Figures 7(a) to 7(d) As shown, the present invention was also tested in four different scenarios (i.e., scenarios 1 to 4). The grid maps used were all 30×30 in size, with black grids representing obstacles and gray polylines representing the optimal path obtained by the mobile robot path planning method provided by the present invention. Figure 8 This is a comparison chart of the convergence curve trends of the path planning method provided in an embodiment of the present invention and some existing methods. Figures 9(a) to 9(d) This is a stability comparison diagram of the path planning method provided by an embodiment of the present invention and some existing methods in scenarios 1 to 4. Figures 8 to 9(d) In this invention, the path planning method is abbreviated as IACO; the path planning method based on the heterogeneous dual-population ant colony method is abbreviated as OACO; the method that only changes the global view to a local view compared to the path planning method provided by this invention is abbreviated as LVACO; the path planning method based on the max-min ant colony method is abbreviated as MMAS; the path planning method based on ant systems is abbreviated as ACS; and the path planning method based on the classic ant colony method is abbreviated as ACO. Figure 8 As can be seen, the method of this invention can achieve convergence relatively quickly, and the path length is optimal. (See also...) Figures 9(a) to 9(d) Algorithms with a higher overlap between optimal and worst paths tend to have better stability. However, ACO and MMAS failed to converge stably on more complex maps, so their values ​​are not recorded. Figures 9(a) to 9(d) It can be seen that the method provided by the present invention has good stability and surpasses the existing methods used for comparison.

[0126] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a mobile robot path planning method based on global scanning according to any embodiment of this invention.

[0127] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform a mobile robot path planning method based on global scanning according to any embodiment of this invention.

[0128] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0129] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0130] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0131] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0132] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0133] In summary, this invention proposes a mobile robot path planning method, device, and medium based on global scanning for complex path planning environments. It is highly versatile and exhibits high efficiency and robustness. The global scanning strategy is constructed using a straight-line rotation scanning method around a point, and the resulting optional node intervals are converged using an orientation angle formula. This approach adapts to complex maps and offers fast processing speed. The combination of global scanning and a backtracking strategy allows the ant to have multiple options and high fault tolerance during path search.

[0134] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0135] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A path planning method for a mobile robot based on global scanning, characterized in that, include: Acquire environmental data and perform environmental modeling using the raster method to determine the free space raster and obstacle raster; In a defined environment model, a collision-free path from the starting point to the ending point is searched using an ant colony system based on global scanning; wherein, before selecting the next node, the ant colony system based on global scanning uses a global scanning method to determine the global field of view of the current node. The method for selecting the next node is as follows: Using a random function, either a state transition rule or a distance-factor direction selection rule is used to determine the next node in the current node's global view. The expression is as follows: in, S This represents the node selection function. i Indicates the current node, j Indicates the location at the current node i Candidate nodes in the global view Indicates the first k Current node in round search i With candidate nodes j The pheromones between them Indicates the current node i With candidate nodes j Distance heuristic factor between The weighting coefficients represent the pheromone concentration. Represents the weight coefficients of the heuristic function; Indicates the first k Current node in round search i With candidate nodes j The state transition probability selection function between them; As a preset constant, This represents a random function between 0 and 1; Distance heuristic factor The expression is: Indicates the starting point s To the current node i distance, Represented as the current node i to candidate node j distance, Indicates candidate nodes j To the finish line e distance, This is a preset constant; The next node is selected using adaptive weight coefficients, expressed as follows: in, The weighting coefficients represent the pheromone concentration. The weight coefficients of the heuristic function are all related to the current search round. k Related adaptive functions, All are preset constants; The next node is selected using a state transition probability selection function with added directional constraint function, expressed as: in, Indicates the first k Current node in round search i The set of candidate nodes, Indicates starting from the current node i to candidate node j The directional constraint function is expressed as: Indicates starting from the current node i to candidate node j directional vector, Indicates starting from the current node i To the finish line e directional vector, These are preset constants; Based on the searched collision-free path, the mobile robot path planning is performed.

2. The mobile robot path planning method according to claim 1, characterized in that, The method of using a global scan to determine the global field of view of the current node includes: Using the geometric midpoint of the current node as the center, a rotational scan is performed using a straight line passing through this center to determine the centers of all free space grids swept by the straight line before it encounters an obstacle grid. The free space grids whose centers are swept by the straight line are taken as the global view of the current node.

3. The mobile robot path planning method according to claim 2, characterized in that, The method of using a global scanning-based ant colony system to search for a collision-free path from the starting point to the ending point includes: Determine the initial parameters required for the ant colony method; Set a preset number of ants at the starting point to begin this round of searching; For each ant, the next node is selected from the global view of the current node using a roulette wheel method, and the tabu list is updated. Determine if the current search round is complete. If yes, continue to the next step; otherwise, return to the previous step. Perform a global pheromone update; Determine if the search round has reached the maximum number of iterations. If so, output the current optimal path; otherwise, return to execute the step of setting the preset number of ants at the starting point and start a new round of search.

4. The mobile robot path planning method according to claim 3, characterized in that, The initial parameters required for the ant colony determination method include determining the initial pheromone distribution in the following manner: The probability of a stimulus is determined based on the number of obstacle grids surrounding each grid in the environmental model. Based on the stimulus probabilities and the positions of the start and end points in the environmental model, the initial pheromone matrix is ​​preliminarily determined. The initial pheromone matrix is ​​preliminarily determined, and pheromone enhancement is performed on the connecting path; the connecting path is all the grids traversed by the line from the starting point to the ending point. The initial pheromone matrix after path pheromone enhancement is then subjected to endpoint pheromone enhancement to obtain the final initial pheromone matrix; the endpoint pheromone enhancement includes incremental radiative decreasing enhancement centered on the endpoint. The initial pheromone matrix is ​​preliminarily determined, and its expression is: in, This represents the initial pheromone matrix, preliminarily determined based on stimulus probabilities and the positions of the start and end points in the environmental model. Indicates the current node i The probability of stimulation, Indicates the current node i To the finish line e The distance function, based on the current node. i and the end point e Location determination within the environmental model, Indicates the initial point s To the current node i The distance function, based on the initial point s and the current node i Location determination within the environmental model, j Indicates a candidate node.

5. The mobile robot path planning method according to claim 3, characterized in that, The step of selecting the next node from the global view of the current node also includes: If the global view of any ant's current node is empty, then remove the current node from the ant's tabu list, set it as an obstacle grid, and make the ant return to the previous node and select the next node again.

6. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the mobile robot path planning method based on global scanning as described in any one of claims 1-5.

7. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed in the computer, it causes the computer to perform the mobile robot path planning method based on global scanning as described in any one of claims 1-5.

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