Mobile robot path planning method and system

Abstract

The invention discloses a mobile robot path planning method and system, and belongs to the technical field of robots, the method comprises global path planning and local path planning; wherein the global path planning comprises the steps of enabling a path to be constantly away from an obstacle by improving an A * algorithm and introducing a bilateral obstacle detection algorithm and an offset obstacle avoidance method so as to avoid collision with the obstacle during turning, and performing optimization and smoothing processing on the path by combining a thinning algorithm and a middle point outward expansion method so as to generate a global optimal path; the local path planning comprises the steps of improving a TEB algorithm, introducing a sensing window and an evaluation function, proposing an acceleration and jerk double-constraint dynamic obstacle avoidance strategy, and ensuring stable movement of the robot in the obstacle avoidance process. According to the scheme, by combining the advantages of global path planning and local path planning, the path planning capability and the transportation efficiency of the robot in a complex and changeable environment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of robotics, and particularly to a path planning method and system for a mobile robot. Background Art

[0002] Mobile robots are increasingly widely used in various fields. Especially in scenarios such as industry, logistics, medical care, and scientific research laboratories, they have demonstrated great application potential and value. In the field of material synthesis experiments, the introduction of mobile robots has provided strong support for the development of laboratory automation and intelligence. However, in the complex and changeable environment of a material synthesis laboratory, the path planning of mobile robots faces many technical challenges.

[0003] In addition, an experimental mobile robot not only needs to complete the task of transporting items but also needs to ensure the stability and safety during transportation. Especially when transporting fragile or dangerous chemicals, minor path deviations or unstable movements may lead to damage to the items or the occurrence of safety accidents. Therefore, effective path planning is required.

[0004] Currently, the commonly used robot path planning methods are mainly global path planning methods. This method focuses on global path optimization, that is, based on the known map and environmental information, an optimal path from the starting point to the ending point is calculated through algorithms. This method performs well in scenarios where the environment is relatively stable and the positions of obstacles are fixed. However, in a material synthesis laboratory, due to the complex layout of experimental equipment, narrow channels, the movement of experimental personnel, and the dynamic changes of experimental equipment, the global path planning method is difficult to adapt to this highly dynamic environment. Summary of the Invention

[0005] The present invention provides a path planning method and system for a mobile robot to solve the technical problem that the existing robot path planning methods are difficult to adapt to the complex environment of a material synthesis laboratory.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] On the one hand, the present invention provides a path planning method for a mobile robot. The path planning method for the mobile robot includes: global path planning and local path planning; wherein,

[0008] The global path planning includes: obtaining the global information of the environment around the robot, and based on the global information, calculating an optimal path from the starting point to the ending point as the initial path,

[0009] The local path planning includes: using the initial path as the input of a preset local path planning algorithm, and optimizing the initial path by using the preset local path planning algorithm to obtain the optimal path.

[0010] Further, calculating an optimal path from the starting point to the ending point based on the global information as the initial path includes:

[0011] Improve the A* algorithm. Based on the improved A* algorithm, combine the bilateral obstacle detection algorithm, the offset obstacle avoidance method, the path thinning technique, and the middle point outward expansion method to generate a globally optimal initial path.

[0012] Further, improving the A* algorithm, based on the improved A* algorithm, combining the bilateral obstacle detection algorithm, the offset obstacle avoidance method, the path thinning technique, and the middle point outward expansion method to generate a globally optimal initial path includes:

[0013] Improve the A* algorithm, perform path planning through the improved A* algorithm; and use the bilateral obstacle detection algorithm to determine whether the robot is in a narrow passage, and apply the offset obstacle avoidance method to keep the path constantly away from obstacles to avoid collisions; where the definition of the narrow passage is adjusted according to the actual application scenario. When there are obstacles on both the left and right preset ranges of the robot, it is considered that the robot is in a narrow passage.

[0014] Use the Douglas - Peucker algorithm to simplify the path planned by the improved A* algorithm to remove redundant nodes in the path.

[0015] Use the middle point outward expansion method to smooth the simplified path to generate a globally optimal initial path.

[0016] Further, improving the A* algorithm includes:

[0017] Introduce the obstacle map cost into the cost function of the A* algorithm and set an adaptive weight; where the obstacle map cost is given the highest cost in the obstacle area, and as the distance between the robot and the obstacle gradually increases, the obstacle map cost decreases accordingly until it drops to zero in the obstacle - free area.

[0018] Further, the cost function of the A* algorithm after introducing the obstacle map cost is expressed as:

[0019] f(n) = g(n)+h(n)+c(n)

[0020] where f(n) represents the comprehensive priority of the node; g(n) represents the cost value from the starting point to the current node; h(n) represents the cost estimate value from the current node to the target point; c(n) represents the obstacle map cost of the current node.

[0021] Further, the expression of the cost function of the A* algorithm after introducing the obstacle map cost and setting the adaptive weight is as follows:

[0022] f(n) = g(n) + (w + p) * h(n) + k * c(n)

[0023] Where, w is the weight coefficient of the estimated cost; k is the weight coefficient of the obstacle map cost; when h(n) is higher than the set value, increase the value of w, and when h(n) is lower than the set value, decrease the value of w; p is a preset offset, and its function is to make the nodes closer to the target node have smaller cost function values.

[0024] Further, the bilateral obstacle detection algorithm is used to determine whether the robot is in a narrow passage, and the offset obstacle avoidance method is applied to keep the path constantly away from obstacles and avoid collisions, including:

[0025] Detect the obstacle information within a preset range on both sides of the robot through a preset obstacle detection algorithm, determine whether the robot is in a narrow passage, and dynamically adjust the parameters of the offset obstacle avoidance method according to the detection results to adapt to passages of different widths; among them, if the width of the passage is lower than the preset threshold, reduce the offset distance to avoid collisions between the robot and the obstacles on both sides; if the width of the passage is greater than the preset threshold or there is an obstacle on only one side, increase the offset distance to ensure that the robot maintains a sufficient safety distance from the obstacles.

[0026] Further, the preset local path planning algorithm is an improved Time Elastic Band (TEB) algorithm; the improvement of the TEB algorithm includes: introducing a perception window in the TEB algorithm and adding speed and jerk constraints;

[0027] The optimization of the initial path using the preset local path planning algorithm to obtain the optimal path includes:

[0028] Obtain the surrounding environment of the robot in real time, obtain the distribution information of the obstacles around the robot, establish a perception window and dynamically update the obstacle distribution information; among them, the range of the perception window is dynamically adjusted according to the robot speed and the distribution density of the surrounding obstacles to ensure that the perception window can cover the relevant area of the robot's movement;

[0029] Evaluate the path by dynamically updating the obstacle distribution information within the perception window and combining with an evaluation function. Further, the calculation function of the distribution density of the obstacles is:

[0030] I(n, d, s) = δ · n + ε · a d · a d + μ · b s

[0031] Among them, I(n, d, s) represents the distribution density function of obstacles; n is the number of obstacles; d is the distance between the two closest obstacles within the sensing window; s is the area of the polygon composed of all obstacles detected within the sensing window; δ, ε, μ are the weight coefficients of each part respectively; a, b are the base coefficients of the exponential function. The larger the value of I(n, d, s), the denser the distribution of obstacles within the sensing window.

[0032] Furthermore, the acceleration constraint is expressed as:

[0033]

[0034] The jerk constraint is expressed as:

[0035]

[0036] Among them, f acceleration (B) represents the acceleration constraint function; n waypoints represents the number of local path points; a i represents the acceleration of the i-th path point; a max represents the maximum acceleration; f jerk (B) represents the jerk constraint function; j i represents the jerk of the i-th path point; j max represents the maximum jerk.

[0037] On the other hand, the present invention also provides a mobile robot path planning system, including:

[0038] A sensing module, which is used to sense the obstacle information in the laboratory environment in real time. By collecting the dynamic and static obstacle data around the robot, it constructs an obstacle map of the laboratory environment, provides a reliable environmental perception basis for path planning, and supports the real-time update of dynamic obstacles. Specifically, the sensing module provides the basic environmental map data for global path planning by sensing the static obstacle information in the laboratory environment. The obstacle position, size, and distribution information collected by this module are used to improve the cost calculation and dynamic weight adjustment in the A* algorithm, so as to optimize the generation of the path, ensure that the path avoids obstacles, and dynamically adjust the offset parameters in narrow channels to generate a safer and more reliable global path. In addition, the sensing module also provides input information for the time elastic band (TEB) algorithm in local path planning by collecting dynamic obstacle data in real time.

[0039] The positioning module is used to obtain the real-time position information of the robot. By fusing inertial navigation, visual positioning, and other sensor data, it accurately determines the position and orientation of the robot in the laboratory environment. It provides the accurate coordinates of the starting point and the target position in the global path planning, and provides real-time position information support for trajectory tracking and dynamic path adjustment in the local path planning, thus providing accurate support for path planning and trajectory optimization.

[0040] The control module is used to control the movement of the robot in real time according to the trajectory output by the path planning module. At the same time, it performs feedback adjustment through sensors and the positioning module, and executes path tracking, dynamic obstacle avoidance adjustment, speed optimization, attitude stabilization, and trajectory tracking to ensure the safe operation of the robot in the environment.

[0041] The path planning module includes a global path planning module and a local path planning module, which are used to achieve the collaborative optimization of global path planning and local path planning methods. Among them, the global path planning module uses an improved A* algorithm for path planning, introduces path thinning and dynamic weight heuristic functions to improve the real-time performance and computational efficiency of the path; it identifies narrow channels through a bilateral obstacle detection algorithm and dynamically adjusts the obstacle avoidance parameters to optimize path generation. Thus, a global path is generated.

[0042] Furthermore, the global path planning module includes the following sub-modules: the Douglas-Peucker algorithm module, which is used to simplify path nodes. By removing redundant nodes in the path, it optimizes the path data volume and enhances the smoothness of the robot's movement. The intermediate point expansion module is used to smooth the path by adjusting the curvature of the path turning points, reduce the impact during turning, and further improve the stability and fluency of the robot's movement.

[0043] The local path planning module uses an improved Time Elastic Band (TEB) algorithm. By sensing the window to update the obstacle information in real time, it combines dynamic obstacle avoidance strategies and evaluation functions to achieve real-time adjustment of the robot's trajectory, thus optimizing the local trajectory and ensuring the real-time performance and effectiveness of obstacle avoidance.

[0044] On the other hand, the present invention also provides an electronic device, which includes a processor and a memory; wherein, at least one instruction is stored in the memory, and the instruction is loaded and executed by the processor to implement the above method.

[0045] On another hand, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the instruction is loaded and executed by the processor to implement the above method.

[0046] The technical solution provided by the present invention integrates a global path planning algorithm and a local path planning algorithm, aiming to improve the path planning efficiency of a mobile robot during a material experiment and ensure that the robot can safely and efficiently complete tasks such as sample transportation and equipment operation in a complex experimental environment; the beneficial effects it brings include at least:

[0047] 1. By combining the advantages of global path planning and local path planning, the present invention improves the path planning ability and transportation efficiency of the robot in the complex and changeable environment of a material synthesis laboratory;

[0048] 2. By introducing dynamic heuristic adjustment, path thinning technology, bilateral obstacle detection algorithm, offset obstacle avoidance method, and path optimization and smoothing processing technology, the present invention significantly improves the obstacle avoidance ability and movement smoothness of the robot in narrow channels and dynamic environments;

[0049] 3. The double-constraint dynamic obstacle avoidance strategy of acceleration and jerk proposed by the present invention effectively prevents items from being damaged due to vibration or impact during transportation, improves the safety and efficiency of the experiment, and provides an efficient solution for laboratory automation;

[0050] 4. The improvement of the obstacle avoidance method in the present invention effectively reduces the risk of collision with obstacles and ensures the transportation safety of fragile or dangerous items. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 is the logic flowchart of the mobile robot path planning method provided by the embodiment of the present invention;

[0053] Figure 2 is the logic flowchart of the global path planning method of the material experiment robot provided by the embodiment of the present invention;

[0054] Figure 3 is the logic flowchart of the local path planning method of the material experiment robot provided by the embodiment of the present invention;

[0055] Figure 4 is the system block diagram of the electronic device provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will further describe the embodiments of the present invention in detail with reference to the drawings.

[0057] First of all, it should be noted that in the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "exemplarily" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0058] First Embodiment

[0059] For the task of transporting experimental items in a complex laboratory environment, this embodiment provides a mobile robot path planning method, which combines the collaborative optimization of global path planning and local path planning to solve key problems such as path generation, dynamic obstacle avoidance, and motion smoothness in the task of transporting experimental supplies in a complex laboratory environment.

[0060] The execution process of this method is as Figure 1 shown. Among them, it should be noted that in an environment such as a materials laboratory where the space is narrow and the equipment is dense, especially in narrow passages, directly applying the expansion radius will significantly compress the space available for the robot to pass through. Therefore, in global path planning, in this embodiment, the A* algorithm is improved by introducing dynamic heuristic adjustment and path thinning techniques, and then a bilateral obstacle detection algorithm is introduced to determine whether it is in a narrow passage, and an offset obstacle avoidance method is introduced to make the path constantly stay away from obstacles, avoiding collisions with obstacles at moments such as turning. At the same time, the path is optimized and smoothed by combining the Douglas-Peucker thinning algorithm and the method of expanding outward from the intermediate point to generate a globally optimal path suitable for a complex experimental environment. In local path planning, this embodiment improves the time elastic band algorithm, based on real-time obstacle perception technology, introduces a perception window and an evaluation function method, and proposes a double-constraint dynamic obstacle avoidance strategy for acceleration and jerk to ensure the smooth movement of the robot during obstacle avoidance and prevent the experimental items being transported from being damaged due to vibration or impact.

[0061] This method can be implemented by an electronic device, which can be a terminal or a server. The mobile robot path planning method includes: global path planning and local path planning; among them,

[0062] Global path planning includes: obtaining the global information of the environment around the robot, and based on the global information, calculating an optimal path from the starting point to the ending point as the initial path;

[0063] Local path planning includes: using the initial path as the input of a preset local path planning algorithm, and optimizing the initial path by using the preset local path planning algorithm to obtain the optimal path.

[0064] Next, the global path planning and local path planning processes will be described in detail respectively.

[0065] As Figure 2 shown, in global path planning, path planning is performed through an improved A* algorithm. An additional cost is introduced, and an adaptive weight heuristic function is set to improve the real-time performance and computational efficiency of path planning. A bilateral obstacle detection algorithm is introduced to determine whether the robot is in a narrow passage, and an offset obstacle avoidance method is applied to keep the path constantly away from obstacles to avoid collisions. A path thinning technique is introduced, and the Douglas-Peucker algorithm is used, combined with the method of expanding outward from the intermediate point to optimize and smooth the path, reduce redundant nodes, and improve the smoothness of the robot's movement. A globally optimal path applicable to a complex experimental environment is generated. Its execution steps are as follows:

[0066] Improve the A* algorithm, and perform preliminary path planning through the improved A* algorithm. Based on global information, calculate a preliminary optimal path from the starting point to the ending point. The improvement method of the A* algorithm is as follows:

[0067] In the global path planning stage, this embodiment optimizes the A* algorithm in three aspects: First, an additional cost is introduced into the A* cost function to more comprehensively evaluate the quality of the path. Second, an adaptive weight heuristic function is set to dynamically adjust the weight of the heuristic estimated cost in the total cost, and according to the real-time movement state of the robot and environmental changes, locally adjust the path to avoid collisions and reduce the path length. Third, an offset p is introduced to effectively reduce unnecessary node searches and improve the search efficiency. The following will be described in detail one by one:

[0068] The form of the A* algorithm cost function:

[0069] f(n) = g(n) + h(n)

[0070] f(n) represents the comprehensive priority of the node, and this comprehensive priority is considered when selecting the node;

[0071] g(n) represents the cost value from the starting point to the current node;

[0072] h(n) represents the cost estimated value from the current node to the target point, that is, the prediction function.

[0073] First, to more accurately reflect various factors in the path planning process and accurately reflect the relationship between the path and obstacles in the path planning process, this embodiment introduces an additional cost into the cost function of the A* algorithm. The additional cost is the obstacle map cost, that is, this embodiment introduces the key element of the obstacle map cost. Among them, the additional cost is calculated based on the obstacle map cost. The obstacle area is assigned the highest cost, and as the distance from the obstacle gradually increases, this cost will decrease accordingly until it drops to zero in the obstacle-free area. The inclusion of this additional cost enables the algorithm to more effectively evaluate the distance between the path and the obstacles when measuring the quality of the path, fully consider the proximity of the path to the obstacles, and then plan a path that is more suitable for the actual application scenario and has higher safety. The form of the improved cost function of the A* algorithm:

[0074] f(n) = g(n) + h(n) + c(n)

[0075] Among them, c(n) represents the additional cost, that is, the obstacle map cost of the current node.

[0076] Secondly, in the A* algorithm, the cost function plays a crucial role, which determines the search direction and efficiency of the algorithm. Therefore, to further improve the performance of the algorithm and optimize the search direction and efficiency, this embodiment sets an adaptive weight cost function. We first express the cost function in the form of f(n) = g(n) + w * h(n) + k * c(n), where w is the weight coefficient of the estimated cost and k is the weight coefficient of the additional cost. By adjusting the value of w, the algorithm can be controlled to be more biased towards the actual cost or the estimated cost during the search process. To achieve adaptive weight adjustment, this embodiment introduces a dynamic threshold d, which is judged based on the heuristic estimated cost h(n). When h(n) is higher than a certain set value, it indicates that the current node is far from the target node. At this time, increase the value of the weight coefficient w to speed up the search and reduce the search points. On the contrary, when h(n) is lower than a certain set value, it indicates that the current node is already close to the target node. At this time, reduce the value of the weight coefficient w to give priority to the optimal path. In the path search problem, the value of k can reflect the degree of importance that the decision maker attaches to the obstacle map cost. If k is large, the search process will pay more attention to avoiding obstacles or high-cost areas, even if this will increase the total length of the path. On the contrary, a smaller value of k may make the search process pay more attention to the length or efficiency of the path and relatively ignore the existence of obstacles. Among them, the value of k is manually set according to the actual situation. In this embodiment, its value is 5.

[0077] Finally, during the search process of the A* algorithm, when multiple nodes have the same heuristic function value f(n), they will all be searched, which leads to a decrease in the algorithm's performance. To solve this problem, in this embodiment, a small offset p is added to the estimated function h(n). The role of this offset p is that when comparing nodes, the node closer to the target node has a smaller cost function value and is thus preferentially selected. Among them, the value of p is manually set according to the actual situation. In this embodiment, its value is 0.1.

[0078] By introducing the offset p, we can further optimize the cost function into the form of f(n) = g(n) + (w + p) * h(n) + k * c(n). When comparing nodes, if the heuristic function values of two nodes are the same, the node closer to the target node is preferentially selected. This optimization method can further improve the search efficiency of the algorithm and reduce unnecessary search points.

[0079] To sum up, by introducing additional costs and setting an adaptive weight heuristic function, the mobile robot path planning method in this embodiment can more comprehensively evaluate the advantages and disadvantages of the path. These optimization measures enable the algorithm to find the optimal path faster during the search process while ensuring the accuracy and safety of the path.

[0080] Furthermore, in this embodiment, through the bilateral obstacle detection algorithm, it is judged whether the robot is in a narrow passage, and the offset obstacle avoidance method is applied to keep the path constantly away from obstacles to avoid collisions.

[0081] Among them, the bilateral obstacle detection algorithm determines whether the robot is in a narrow passage by detecting obstacle information within a certain range on both sides of the robot, and dynamically adjusts the parameters of the offset obstacle avoidance method according to the detection results to adapt to passages of different widths. By calculating the positions and quantities of obstacles on the left and right sides, it can be judged whether the robot is in a narrow passage. Judge whether all obstacles are on the same side of the path point; when the obstacles are within the safe distance, translate the path point along the direction pointing from the path point to the nearest obstacle to maintain the preset safe distance; update the path point coordinates and check whether the translated path point is in free space.

[0082] Judge obstacle point C 1 (x 1 ,y 1 ) and C 2 (x 2 ,y 2 ) are on the same side of the path points A(x A ,y A ) and B(x B ,y B ), which can be based on the sign determination method of the straight-line equation. The following are the specific steps:

[0083] Determine the straight-line equation between waypoints A and B. The slope k and intercept b of the straight line AB are as follows:

[0084] b = y A -k·x A

[0085] Therefore, the straight-line equation can be expressed as:

[0086] y = kx + b

[0087] Calculate the algebraic distance (residual) from a point to the straight line;

[0088] Substitute point C 1 (x 1 , y 1 ) and C 2 (x 2 , y 2 )'s coordinates, and calculate their residuals with respect to the straight line AB respectively:

[0089] r 1 = y 1 -(kx 1 + b)

[0090] r 2 = y 2 -(kx 2 + b)

[0091] If r 1 ·r 2 > 0, that is, the signs of the two residuals are the same, then the obstacle points C 1 and C 2 are on the same side of the waypoints A and B. If r 1 ·r 2 < 0, that is, the signs of the two residuals are opposite, then C 1 and C 2 are on different sides of the waypoints A and B. If r 1 = 0 or r 2 = 0, then the point C 1 or C 2 is on the straight line.

[0092] The translation method of waypoints includes:

[0093] Determine the direction angle between the waypoint x A (x A , y A ) and the nearest obstacle point C 1 (x 1 , y 1 ):

[0094] θ = arctan2(yA -y 1 , x A -x 1 )

[0095] Calculate the translation amount according to the difference between the set safety distance safe_distance_ and the distance d to the nearest obstacle:

[0096] Δx = (safe_distance - d)·cos(θ)

[0097] Δy = (safe_distance - d)·sin(θ)

[0098] Update the path point coordinates:

[0099] x' A = x A + Δx

[0100] y' A = y A + Δy

[0101] Among them, the update condition for the path point is that the translated path point is within the free space.

[0102] The definition of a narrow passage can be adjusted according to the actual application scenario. When there are obstacles on both the left and right sides within a certain range, it can be considered that the robot is in a narrow passage. Once it is determined that the robot is in a narrow passage, the parameters of the offset obstacle avoidance method need to be dynamically adjusted according to the width of the passage. For example, if the passage is very narrow, the offset distance may need to be reduced to avoid the robot colliding with the obstacles on both sides. On the contrary, if the passage is wider or there is an obstacle on only one side, the offset distance can be increased to ensure that the robot maintains a sufficient safety distance from the obstacles.

[0103] After obtaining the preliminary path, use the Douglas - Peucker algorithm to simplify the path, remove the redundant nodes in the path, optimize the path geometry, and make the path more concise and efficient. And further apply the middle point outward expansion method to smooth the simplified path, reduce the curvature at the turning points of the path, thereby reducing the steering impact during the robot's movement, and thus enhancing the smoothness and movement efficiency of the robot's movement.

[0104] Using the Douglas - Peucker algorithm to process a large amount of redundant geometric data points can not only achieve the purpose of data reduction but also retain the shape of the geometry to a large extent. Specifically: represent the path generated by the global path planning as a sequence of geometric data points P 1 , P 2 , …, P n , where each point is represented by its coordinates (xi , y i ) is represented. Take the starting point P of the path 1 and the ending point P n , connect them with a straight line to form a virtual straight line, and calculate the perpendicular distance from all intermediate points to this straight line. Then find the maximum distance value d max , use d max to compare with the thinning threshold threshold: If d max < threshold, all the intermediate points on this curve are discarded, and only the starting point and the ending point are directly retained. If d max ≥ threshold, then taking the point with the maximum distance as the boundary, divide the curve into two parts, and repeat the above process for these two parts of the curve until all points are processed, retain the key nodes, discard the redundant points, and form an optimized sequence of path points. Among them, for the calculation of the distance from a point to a straight line, for any intermediate point P of the path i (x i , y i ), its perpendicular distance d to the straight line connecting the starting point and the ending point P 1 (x 1 , y 1 ) and P n (x n , y n ) is calculated by the formula:

[0105]

[0106] The core idea of the intermediate point outward expansion method is to change the curvature of the path by adjusting the positions of the turning points in the path. In path planning, the turning points are often the main sources of path roughness. By expanding these turning points outward, the path can become smoother at these points. During the path smoothing process, by adjusting the turning points in the path to expand them towards the midpoint, the effect of path smoothing can be achieved. Suppose there are three consecutive points P i-1 , P i and P i+1 in the path, and their coordinates are (x i-1 , y i-1 ), (x i , y i ) and (x i+1 , y i+1 ) respectively. Adjust the coordinates of the intermediate point to make it closer to the midpoint of the two adjacent points. The adjusted coordinates of P i ' are:

[0107]

[0108] This formula reflects the core idea of the intermediate point outward expansion method, that is, moving P i to P i-1and P i+1 at the midpoint position to reduce the curvature of the path and achieve path smoothing. If P i ′ satisfies condition A: 1. It is not an obstacle; 2. It is within the circle of the search range; 3. The angle difference between (P i-1 , P i ′) and (P i ′, P i+1 ) is less than the angle difference between (P i-1 , P i ) and (P i , P i+1 ); if condition A is satisfied, then use P i ′ to replace P i . If condition A is not satisfied, then with the midpoint P i ′ as the center, expand outward to search for a new path point N i . During the expansion process, increase the expansion radius with a fixed step size until the maximum radius S r , or find a point that meets the requirements, otherwise do not optimize this point. N i can be expressed as: N i (x, y) = P i ′(x) + Δx, N i (y, y) = P i ′(y) + Δy, where Δx and Δy represent the step size and direction of the search.

[0109] By reducing the curvature of the path turning points, optimizing the transition effect of sharp corners, and ensuring that the path is smoother at the turning points. For points that cannot be further optimized, keep their original positions to retain the overall shape of the path.

[0110] In local path planning, adopt the local path planning algorithm TEB (Time Elastic Band) based on performance constraints to optimize the path planning and obstacle avoidance capabilities of the robot in a complex dynamic environment, and ensure high stability and safety in the transportation task in the material synthesis experimental environment.

[0111] Specifically, as Figure 3As shown in the figure, in local path planning, this embodiment improves the Time Elastic Band (TEB) algorithm. The path generated by global path planning is used as the initial input, and the sequence of the starting point, ending point, and intermediate points is used as the initial path for TEB optimization. The real-time obstacle perception technology is utilized to perceive the environment around the robot in real time through the perception module, establish a perception window, and dynamically update the obstacle information. Thus, the perception window and evaluation function method are introduced. By updating the obstacle information in the perception window in real time and combining the evaluation function for path evaluation, the robot can quickly respond to changes in the environment, such as newly emerging obstacles or the movement of existing obstacles, and thus dynamically adjust its motion trajectory to ensure the real-time and effectiveness of obstacle avoidance. And a dynamic obstacle avoidance strategy with double constraints of acceleration and jerk is proposed to ensure the smooth movement of the robot during obstacle avoidance and prevent items from being damaged due to vibration or impact. Among them, the range of the perception window is dynamically adjusted according to the robot speed and the distribution density of surrounding obstacles (try values such as 0.1, 0.2, 0.3 in sequence, select the optimal parameter value, and then calculate the function value of I(n, d, s), and use the calculated function value as the circle for obstacle avoidance), ensuring that the perception window can cover the key areas of the robot's movement.

[0112] Among them, the pose of the mobile robot in the TEB algorithm is defined as:

[0113] s i =[x i ,y i ,θ i

[0114] Among them, x i ,y i represent the coordinates in the map established by the mobile robot based on SLAM, and θ i represents the heading angle of the mobile robot in the map coordinates.

[0115] The sequence of the pose S of the mobile robot i is defined as:

[0116] Q={s i}, i = 0…n

[0117] The TEB algorithm divides the path process into n - 1 time series with n pose sequences. Using ΔT i to represent the time step between two consecutive poses, then the time step sequence of the entire path process is defined as:

[0118] τ={ΔT i}, i = 0,…, n - 1

[0119] Combining the time series and the pose sequence into a set is the TEB sequence, and the pose based on the time step is obtained:

[0120] ​B = (Q, τ)

[0121] In path planning, multiple objectives to be optimized usually need to be considered. Under constraint conditions such as speed constraints, acceleration constraints, and safety limitations, the constraint function is defined according to the weights of each objective to be optimized as follows:

[0122]

[0123] Among them, γ k represents the weight of f k (B), and the weighted sum is used to obtain the final constraint f(B).

[0124] B * = arg B min f(B)

[0125] Among them, B * is the optimal trajectory after optimization. During the optimization process, the generated trajectory is the sequence of optimal path points after optimization. In this paper, the g2o non-linear optimization library is used to solve B * to obtain the optimal solution B * , that is, the local path point sequence. By analyzing and processing the local distribution state of obstacles in the perception window, the perception window can effectively improve the speed optimization efficiency of the algorithm, and dynamically set the obstacle avoidance weight according to the obstacle density in the perception window to adapt to the changing requirements of complex environments. Among them, the measurement of the obstacle density is described by a specific function I(n, d, s), and it is expressed as the obstacle weight:

[0126] I(n, d, s) = δ·n + ε·a d ·a d + μ·b s

[0127] In the formula, n is the number of obstacles; d is the distance between the two closest obstacles in the perception window; s is the area of the polygon formed by all obstacles detected in the perception window; δ, ε, μ are the weight coefficients of each part; a, b are the base coefficients of the exponential function. The larger the value of the density function I(n, d, s), the denser the distribution of obstacles in the perception window. Among them, δ, ε, μ, a, b are all manually set values.

[0128] It should be noted that after calculating the value of the function I(n, d, s), the calculated function value can be directly used as the obstacle avoidance weight (this embodiment adopts this scheme), or it can be used as the obstacle avoidance weight after multiplying by a certain set coefficient according to the actual situation. In this regard, this embodiment does not make specific limitations.

[0129] Furthermore, in the traditional TEB algorithm, the feasibility of the robot's trajectory is usually ensured by constraining the linear velocity and angular velocity. However, in order to further improve the smoothness and safety of the robot's movement, this embodiment introduces acceleration and jerk constraints to limit the amplitude and rate of change of the robot's speed, thereby optimizing the dynamic performance of the robot's movement trajectory. That is, the performance constraints in the local path planning algorithm of this embodiment include the maximum speed, maximum acceleration, maximum jerk, etc. of the robot, as well as avoiding actions such as sharp turning and sudden stop to ensure the smoothness of the robot during obstacle avoidance and the safety of transporting items.

[0130] During the movement of the mobile robot, the ideal situation is to ensure its driving stability and prevent excessive body shaking caused by sudden stop or rapid speed drop. Therefore, the introduction of acceleration constraints is crucial, which aims to regulate the degree of speed change and ensure that when the mobile robot suddenly encounters an obstacle not far ahead in the traveling direction, its linear velocity and angular velocity can make reasonable and smooth adjustments. In order to improve the comfort of the occupants and the smoothness of the obstacle avoidance action during obstacle avoidance, we specifically introduce the limitation of jerk in the optimization goal. This measure aims to effectively prevent sudden changes in acceleration and ensure that when the mobile robot faces an obstacle, the change in its motion state is more gentle and coherent, thereby reducing the impact and discomfort caused to the occupants. Assume ΔX i 、ΔX i+1 、ΔX i+2 、ΔX i+3 are 4 consecutive pose points in the local path. At the same time, the time intervals between the 4 pose points are ΔT j 、ΔT j+1 、ΔT j+2 . Then the linear acceleration a v-t and the angular acceleration a ω-t are respectively:

[0131]

[0132] where, v t+1 represents the linear velocity at the next moment; v t represents the linear velocity at the current moment; ΔT t represents the current time interval; ΔT t+1 represents the next time interval; ω t+1 represents the angular velocity at the next moment; ω t represents the angular velocity at the current moment;

[0133] The linear jerk j lim-t and the angular jerk j rot_t are respectively:

[0134]

[0135] Among them, a v-t+1 represents the linear acceleration at the next moment; ΔT t+2 represents the next two time intervals; a ω-t+1 represents the angular acceleration at the next moment;

[0136] To ensure the smoothness and safety of the movement, the changes in speed, acceleration, and jerk are restricted, and the objective function is increased:

[0137] Acceleration constraint:

[0138]

[0139] Jerk constraint:

[0140]

[0141] Among them, f acceleration (B) represents the acceleration constraint function; n waypoints represents the number of local path points; a i represents the acceleration of the i-th path point; a max represents the maximum acceleration; f jerk (B) represents the jerk constraint function; j i represents the jerk of the i-th path point; j max represents the maximum jerk.

[0142] In summary, to overcome the defects of the existing technology, this embodiment provides a path planning method for a mobile robot, which combines global path planning and local path planning. The global path planning is responsible for providing a general driving direction for the mobile robot at the macroscopic level, while the local path planning is responsible for precisely controlling the movement of the robot according to the real-time environmental information at the microscopic level. This method can ensure global optimality while flexibly coping with the dynamic changes and uncertain factors in the laboratory. Thus, it can effectively improve the autonomy and intelligence level of the robot in material experiments, significantly enhance the experimental efficiency and operation safety, meet the requirements of dynamic and changeable experimental scenarios, and provide an efficient solution for laboratory automation.

[0143] In the material synthesis laboratory, through the path planning method proposed by the present invention, the mobile robot can autonomously complete the item transportation task in a complex environment. In a dynamic environment, the robot combines sensor perception and real-time optimization of path planning to effectively avoid obstacles and ensure the safety and stability of the transportation process.

[0144] Second Embodiment

[0145] This embodiment provides a path planning system for a mobile robot, and the system includes the following modules:

[0146] The perception module is used to perceive the obstacle information in the laboratory environment in real time. By collecting the dynamic and static obstacle data around the robot, it constructs an obstacle map of the laboratory environment, providing a reliable environmental perception basis for path planning and supporting the real-time update of dynamic obstacles. Specifically, the perception module provides the basic environmental map data for global path planning by perceiving the static obstacle information in the laboratory environment. The obstacle position, size, and distribution information collected by this module are used to improve the cost calculation and dynamic weight adjustment in the A* algorithm, thereby optimizing the path generation, ensuring that the path avoids obstacles, and dynamically adjusting the offset parameters in narrow channels to generate a safer and more reliable global path. In addition, the perception module also provides input information for the Time Elastic Band (TEB) algorithm in local path planning by collecting dynamic obstacle data in real time.

[0147] The positioning module is used to obtain the real-time position information of the robot. By fusing inertial navigation, visual positioning, and other sensor data, it accurately determines the position and posture of the robot in the laboratory environment. It provides the accurate coordinates of the starting point and the target position in global path planning, and provides real-time position information support for trajectory tracking and dynamic path adjustment in local path planning, thereby providing accurate support for path planning and trajectory optimization.

[0148] The control module is used to control the movement of the robot in real time according to the trajectory output by the path planning module. At the same time, it performs feedback adjustment through the sensor and the positioning module, and executes path tracking, dynamic obstacle avoidance adjustment, speed optimization, attitude stabilization, and trajectory tracking to ensure the safe operation of the robot in the environment.

[0149] The path planning module includes a global path planning module and a local path planning module, which are used to realize the collaborative optimization of global path planning and local path planning methods. Among them, the global path planning module uses an improved A* algorithm for path planning, introduces path thinning and dynamic weight heuristic functions to improve the real-time performance and calculation efficiency of the path; it identifies narrow channels through a bilateral obstacle detection algorithm and dynamically adjusts the obstacle avoidance parameters to optimize the path generation. Thus, a global path is generated.

[0150] Furthermore, the global path planning module includes the following sub-modules: The Douglas-Peucker algorithm module is used to simplify path nodes. By removing redundant nodes in the path, it optimizes the path data volume and enhances the smoothness of the robot's movement. The intermediate point expansion module is used to smooth the path by adjusting the curvature of the path turning points, reducing the impact during turning, and further improving the stability and fluency of the robot's movement.

[0151] The local path planning module adopts an improved Time Elastic Band (TEB) algorithm, updates obstacle information in real time through a perception window, and combines a dynamic obstacle avoidance strategy and an evaluation function to achieve real-time adjustment of the robot's trajectory, thereby optimizing the local trajectory and ensuring the real-time performance and effectiveness of obstacle avoidance.

[0152] It should be noted that the mobile robot path planning system in this embodiment corresponds to the mobile robot path planning method in the above first embodiment; among them, the functions implemented by each functional module in the mobile robot path planning system in this embodiment correspond one-to-one to each process step in the mobile robot path planning method in the above first embodiment; therefore, it will not be elaborated here.

[0153] Third Embodiment

[0154] This embodiment provides an electronic device, as Figure 4 shown, the electronic device includes: a processor and a memory; wherein, the processor and the memory can be connected through a communication bus; at least one instruction is stored in the memory, and the instruction is loaded and executed by the processor to implement the method in the above first embodiment. In addition, the electronic device may further include a transceiver, and the processor and the transceiver can be connected through a communication bus, and the transceiver is used to communicate with other devices.

[0155] Next, in combination with Figure 4 specific introductions will be made to each component of the electronic device:

[0156] Among them, the processor is the control center of the electronic device. The electronic device may include multiple processors, and each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may be a single processor or a collective term for multiple processing elements. For example, the processor may be one or more central processing units (CPUs), or other general-purpose processors, application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of the present invention. For example: one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor can execute various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0157] In a specific implementation, as an embodiment, the processor may include one or more CPUs. For example Figure 4 CPU0 and CPU1 shown in, of course, this is only an illustrative example.

[0158] The memory is used to store the software program for executing the solution of the present invention and is controlled by the processor for execution. The specific implementation method may refer to the above method embodiments and will not be elaborated here.

[0159] Optionally, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or may exist independently and be coupled to the processor through the interface circuit ( Figure 4 not shown) of the electronic device. The embodiments of the present invention do not make specific limitations thereto.

[0160] The transceiver may include a receiver and a transmitter ( Figure 4 not shown separately). Among them, the receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function. The transceiver may be integrated with the processor or may exist independently and be coupled to the processor through the interface circuit ( Figure 4 not shown) of the electronic device. The embodiments of the present invention do not make specific limitations thereto.

[0161] In addition, it should be noted that Figure 4 the structure of the electronic device shown in does not constitute a limitation on the device. The actual device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In addition, the technical effects achieved by the electronic device when executing the method of the above first embodiment may refer to the technical effects described in the above first embodiment. Therefore, they will not be elaborated here.

[0162] Fourth Embodiment

[0163] This embodiment provides a computer-readable storage medium in which at least one instruction is stored. The instruction is loaded and executed by the processor to implement the method of the above first embodiment. Among them, the computer-readable storage medium may be a ROM, a random access memory, a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc. The instruction stored therein can be loaded and executed by the processor in the terminal to implement the above method.

[0164] In addition, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, the embodiments of the present invention can take the form of all or part of a hardware embodiment, all or part of a software embodiment, or an embodiment combining software and hardware aspects. Moreover, when implemented using software, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center containing one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0165] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing terminal device generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0166] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1the functions specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in one or more processes and / or boxes Figure 1 one process or more processes and / or boxes Figure 1 the steps of the functions specified in one box or more boxes.

[0167] It should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the element. In addition, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. Among them, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context. "At least one" means one or more, and "a plurality" means two or more. "At least one of the following (items)" or similar expressions refer to any combination of these items, including any combination of single (item) or plural items (items). For example, at least one of a, b or c can mean: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple.

[0168] In addition, it can be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0169] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0170] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of functional modules / units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. One can select some or all of the units according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0171] If the method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0172] Finally, it should be noted that the above description is only the preferred embodiment of the present invention. It should be pointed out that although the preferred embodiments of the present invention have been described, for those of ordinary skill in the art, once the basic creative concept of the present invention is known, several improvements and refinements can be made without departing from the principle described in the present invention. These improvements and refinements should also be regarded as the protection scope of the present invention. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A mobile robot path planning method, characterized in that: The mobile robot path planning method includes: global path planning and local path planning; wherein, The global path planning includes: obtaining global information of the robot's surrounding environment, and calculating an optimal path from a starting point to an end point based on the global information as an initial path; The local path planning includes: taking the initial path as an input of a preset local path planning algorithm, optimizing the initial path using the preset local path planning algorithm, and obtaining an optimal path.

2. The mobile robot path planning method according to claim 1, characterized in that: The method of calculating an optimal path from the starting point to the end point as the initial path based on the global information includes: The A* algorithm is improved. Based on the improved A* algorithm, the global optimal initial path is generated by combining the bilateral obstacle detection algorithm, the offset obstacle avoidance method, the path thinning technology and the midpoint outward expansion method.

3. The mobile robot path planning method according to claim 2, characterized in that: The A* algorithm is improved, based on the improved A* algorithm, combined with the bilateral obstacle detection algorithm, the offset obstacle avoidance method, the path thinning technology and the midpoint outward expansion method to generate the global optimal initial path, including: The A* algorithm is improved and the path planning is performed using the improved A* algorithm. The bilateral obstacle detection algorithm is used to determine whether the robot is in a narrow channel, and the offset obstacle avoidance method is applied to keep the path constant and away from obstacles to avoid collisions. The definition of a narrow channel is adjusted according to the actual application scenario. When there are obstacles within the preset range on both sides of the robot, the robot is considered to be in a narrow channel. Use the Douglas-Peucker algorithm to simplify the path planned by the improved A* algorithm to remove redundant nodes in the path; The simplified path is smoothed using the midpoint outward expansion method to generate the global optimal initial path.

4. The mobile robot path planning method according to claim 2, characterized in that: The improvement of the A* algorithm includes: The obstacle map cost is introduced into the cost function of the A* algorithm, and an adaptive weight is set; wherein the obstacle map cost is assigned the highest cost in the obstacle area, and as the distance between the robot and the obstacle gradually increases, the obstacle map cost decreases accordingly until it drops to zero in the obstacle-free area.

5. The mobile robot path planning method according to claim 4, characterized in that: The cost function of the A* algorithm after introducing the obstacle map cost is expressed as: f(n)=g(n)+h(n)+c(n) Among them, f(n) represents the comprehensive priority of the node; g(n) represents the cost from the starting point to the current node; h(n) represents the estimated cost from the current node to the target point; c(n) represents the obstacle map cost of the current node.

6. The mobile robot path planning method according to claim 5, characterized in that: The expression of the cost function of the A* algorithm after introducing the obstacle map cost and setting the adaptive weight is: f(n)=g(n)+(w+p)*h(n)+k*c(n) Among them, w is the weight coefficient of the estimated cost; k is the weight coefficient of the obstacle map cost; when h(n) is higher than the set value, increase the value of w, and when h(n) is lower than the set value, reduce the value of w; p is the preset offset, which is used to make the nodes closer to the target node have a smaller cost function value.

7. The mobile robot path planning method according to claim 3, characterized in that: The bilateral obstacle detection algorithm is used to determine whether the robot is in a narrow passage, and an offset obstacle avoidance method is applied to keep the path constant and away from obstacles to avoid collisions, including: The preset obstacle detection algorithm is used to detect obstacle information within a preset range on both sides of the robot to determine whether the robot is in a narrow channel, and the parameters of the offset obstacle avoidance method are dynamically adjusted according to the detection results to adapt to channels of different widths; if the channel width is lower than the preset threshold, the offset distance is reduced to avoid the robot from colliding with obstacles on both sides; if the channel width is greater than the preset threshold or there is an obstacle on only one side, the offset distance is increased to ensure that the robot maintains a sufficient safety distance from the obstacle.

8. The mobile robot path planning method according to claim 1, characterized in that: The preset local path planning algorithm is an improved time elastic band TEB algorithm; wherein the improvement of the TEB algorithm includes: introducing a perception window into the TEB algorithm, and adding speed and jerk constraints; The method of optimizing the initial path by using a preset local path planning algorithm to obtain an optimal path includes: Acquire the robot's surrounding environment in real time, obtain the distribution information of obstacles around the robot, establish a perception window and dynamically update the obstacle distribution information; the range of the perception window is dynamically adjusted according to the robot's speed and the distribution density of surrounding obstacles to ensure that the perception window can cover the relevant area of ​​the robot's movement; The obstacle distribution information in the perception window is updated in real time and the path evaluation is performed in combination with the evaluation function.

9. The mobile robot path planning method according to claim 8, characterized in that: The calculation function of the obstacle distribution density is: I(n,d,s)=δ·n+ε·a d ·a d +μ·b s Among them, I(n,d,s) represents the distribution density function of obstacles; n is the number of obstacles; d is the distance between the two nearest obstacles in the perception window; s is the area of ​​the polygon composed of all obstacles detected in the perception window; δ, ε, μ are the weight coefficients of each part respectively; a and b are the base coefficients of the exponential function; the larger the value of I(n,d,s), the denser the distribution of obstacles in the perception window.

10. The mobile robot path planning method according to claim 8, characterized in that: The acceleration constraint is expressed as: The jerk constraint is expressed as: Among them, f acceleration (B) represents the acceleration constraint function; n waypoints Indicates the number of local path points; a i represents the acceleration of the ith path point; a max Indicates the maximum acceleration; f jerk (B) represents the jerk constraint function; j i represents the acceleration of the i-th path point; j max Indicates the maximum value of jerk.

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