Intelligent chemical laboratory mobile composite robot path planning method and device

Abstract

The invention relates to an intelligent chemical laboratory mobile composite robot path planning method and device. The method comprises the following steps: modeling an intelligent chemical laboratory environment by adopting a grid map, and setting an obstacle and a starting point and a target point of a mobile composite robot in the grid map; on the basis of the heuristic function of the traditional A * algorithm, introducing an obstacle index factor to dynamically weight the predicted cost of the heuristic function; according to the starting point, the target point and the improved heuristic function, a forward and reverse alternating bidirectional jump point searching mechanism is adopted for searching, and the optimal path of the mobile composite robot is obtained; and optimizing the optimal path by adopting a Floyd algorithm to obtain a smooth optimized path of the mobile composite robot. By adopting the method, the working efficiency of the mobile composite robot in the intelligent chemical laboratory in high-precision tasks such as pipetting can be improved, the path planning of the robot is optimized, and the robot can quickly and safely reach the target area to assist in completing the experimental task.

Description

[0001] This application relates to the technical field of robot path planning, and in particular, to an intelligent chemical laboratory mobile composite robot path planning method and device. Background Art

[0002] As the dimensions and complexity of the research objects in intelligent chemical laboratories continue to increase, traditional research methods still mainly rely on means such as trial and error and variable dimensionality reduction to reduce complexity, and the problem of low efficiency has become increasingly obvious. At the same time, artificial intelligence, with its ability to mine correlations in high-dimensional and high-complexity data, has promoted the rise of a new data-driven paradigm. This paradigm not only requires the support of a large amount of experimental data, but also emphasizes the reliability and repeatability of experimental results. Against this background, intelligent and automated robots, with their excellent data accuracy, reliability, and efficiency, have become an important technical support for laboratory research.

[0003] The experimental process of an intelligent chemical laboratory covers multiple links such as material out-of-warehouse, transportation, and experiments. Among them, the material out-of-warehouse operation is particularly complex and involves multiple steps, including dispatching a dedicated person or a mobile composite robot (specifically a mobile trolley with a robotic arm) to a designated location to perform the picking task according to experimental requirements, and transporting the sorted materials to a designated experimental bench by staff or the mobile composite robot for experiments. In order to improve the working efficiency of the intelligent laboratory, the reasonable planning of the mobile composite robot path has become the key. Specifically, it is to use an algorithm to plan an optimal path in a spatial environment with known starting points, target points, and obstacles, ensuring that the mobile composite robot can reach the target point safely and efficiently.

[0004] However, although traditional path planning algorithms (such as the A algorithm) are widely used, there are still many problems in practical applications, such as a large number of path turning times, non-smooth paths, and low search efficiency. These problems not only affect the operation efficiency of the mobile composite robot, but also reduce the overall smoothness of the experimental process. Therefore, aiming at the special needs of intelligent chemical laboratories, optimizing the mobile composite robot path planning algorithm, especially improving the path smoothness, has become a key problem to be solved urgently. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide an intelligent chemical laboratory mobile composite robot path planning method and device, which can improve the efficiency and path quality of the mobile composite robot path planning, and provide technical guarantee for the efficient operation of the intelligent chemical laboratory.

[0006] An intelligent chemical laboratory mobile composite robot path planning method, the method includes:

[0007] Model the intelligent chemical laboratory environment using a grid map, and set obstacles, starting points, and target points for mobile compound robots in the grid map.

[0008] Based on the heuristic function of the traditional A* algorithm, introduce an obstacle exponential factor to dynamically weight the estimated cost of the heuristic function to obtain an improved heuristic function.

[0009] According to the starting point, target point, and the improved heuristic function, use a bidirectional jump point search mechanism with alternating forward and backward directions to search for the optimal path of the mobile compound robot. Among them, the bidirectional jump point search mechanism with alternating forward and backward directions means that through multiple rounds of forward and backward searches, stop searching until the same expanded node appears in the forward and backward searches. In the first round, the forward search uses the set starting point and target point as the starting point and target point respectively, and the backward search uses the set target point as the starting point and the node with the minimum estimated cost among the forward expanded nodes in this round as the target point. In other rounds, the forward search uses the target point of the previous round's backward search as the starting point and the node with the minimum estimated cost among the previous round's backward expanded nodes as the target point, and the backward search uses the target point of the previous round's forward search as the starting point and the node with the minimum estimated cost among the forward search expanded nodes in this round as the target point.

[0010] Use the Floyd algorithm to optimize the optimal path to obtain a smooth optimized path for the mobile compound robot.

[0011] In one embodiment, based on the heuristic function of the traditional A* algorithm, introduce an obstacle exponential factor to dynamically weight the estimated cost of the heuristic function, and the improved heuristic function is:

[0012]

[0013] Among them, n represents the node where the robot is currently located, f(n) is the estimated cost for the robot to reach the target point from the starting point via node n, g(n) is the actual cost for the robot to reach node n from the starting point, h(n) is the estimated cost for the robot to reach the target point from the intermediate node n, and P(o) is the ratio of the area occupied by obstacles in the current point to the end area.

[0014] In one embodiment, the ratio of the area occupied by obstacles in the current point to the end area is:

[0015]

[0016] Among them, dx is the vertical displacement from the current expanded node to the target point, dy is the horizontal displacement from the current expanded node to the target point, n is the number of obstacle grids in the rectangular area composed of dx and dy, and S is the area of a single obstacle.

[0017] In one embodiment, according to the starting point, the target point, and the improved heuristic function, a bidirectional jump point search mechanism with positive and negative alternation is adopted for searching to obtain the optimal path of the mobile composite robot, including:

[0018] Initialize lists OpenList01, OpenList02, Close01, and Close02; where list OpenList01 is used to store the forward search expanded nodes, and list Close01 is used to store the forward search path nodes; list OpenList02 is used to store the backward search expanded nodes, and list Close02 is used to store the backward search path nodes.

[0019] Add the starting point to OpenList 01 and add the target point to OpenList 02.

[0020] Set the point with the minimum estimated cost in list OpenList 02 as the forward search target point, perform forward search, add the expanded nodes to list OpenList 01 and update their estimated costs; the estimated costs are calculated using the improved heuristic function.

[0021] Traverse all the nodes in list OpenList01, find the node with the minimum estimated cost, and add it to list CloseList01.

[0022] Set the point with the minimum estimated cost in list OpenList 01 as the backward search target point, perform backward search, add the expanded nodes to list OpenList 02 and update their estimated costs.

[0023] Traverse all the nodes in list OpenList 02, find the node with the minimum estimated cost, and add it to list CloseList 02.

[0024] Judge whether list CloseList01 intersects with list CloseList 02.

[0025] If they do not intersect, continue the bidirectional jump point search with positive and negative alternation.

[0026] If they intersect, generate the forward / backward optimal paths according to their respective parent nodes and merge them to obtain the optimal path of the mobile composite robot.

[0027] In one embodiment, the Floyd algorithm is used to optimize the optimal path to obtain the smooth optimized path of the mobile composite robot, including:

[0028] For the nodes in the optimal path except the path starting point and the target point, judge each node to determine whether it is an inflection point. If it is an inflection point, keep it; otherwise, delete it.

[0029] Divide the inflection points, starting point, and target point in the path into groups of three nodes each. Connect the first and third inflection points in each group and determine whether the line connecting these two points passes through an obstacle. If it does, the middle inflection point in this group cannot be deleted; otherwise, the middle inflection point in this group can be deleted, obtaining a smooth optimized path for the mobile composite robot.

[0030] An intelligent chemical laboratory mobile composite robot path planning device, which includes:

[0031] An environment modeling module for modeling the intelligent chemical laboratory environment using a grid map, setting obstacles, starting point, and target point of the mobile composite robot in the grid map.

[0032] A heuristic function improvement module for introducing an obstacle exponential factor to dynamically weight the estimated cost of the heuristic function based on the traditional A* algorithm heuristic function, obtaining an improved heuristic function;

[0033] A path exploration module for searching according to the starting point, target point, and the improved heuristic function, using a forward-backward alternating bidirectional jump point search mechanism to obtain the optimal path of the mobile composite robot; where the forward-backward alternating bidirectional jump point search mechanism means that through multiple rounds of forward and backward searches, stop searching until the same expanded node appears in the forward and backward searches; in the first round, the forward search uses the set starting point and target point as the starting point and target point respectively, and the backward search uses the set target point as the starting point and the node with the minimum estimated cost among the forward expanded nodes in this round as the target point; in other rounds, the forward search uses the target point of the previous round's backward search as the starting point and the node with the minimum estimated cost among the previous round's backward expanded nodes as the target point, and the backward search uses the target point of the previous round's forward search as the starting point and the node with the minimum estimated cost among the forward search expanded nodes in this round as the target point;

[0034] A path smoothing and optimization module for optimizing the optimal path using the Floyd algorithm to obtain a smooth optimized path for the mobile composite robot.

[0035] In one embodiment, the heuristic function improvement module is further used to introduce an obstacle exponential factor to dynamically weight the estimated cost of the heuristic function based on the traditional A* algorithm heuristic function, and the obtained improved heuristic function is:

[0036]

[0037] Among them, n represents the current node where the robot is located, f(n) is the estimated cost for the robot to reach the target point from the starting point via node n, g(n) is the actual cost from the starting point of the robot to node n, h(n) is the estimated cost for the robot to reach the target point from the intermediate node n, and P(o) is the ratio of the area occupied by obstacles in the current point to the end area.

[0038] In one embodiment, the ratio of the area occupied by obstacles in the current point to the end area is:

[0039]

[0040] Among them, dx is the vertical displacement from the current expanded node to the target point, dy is the horizontal displacement from the current expanded node to the target point, n is the number of obstacle grids in the rectangular area composed of dx and dy, and S is the area of a single obstacle.

[0041] In one embodiment, the path exploration module is further configured to initialize the lists OpenList01, OpenList02, Close01, and Close02; where the list OpenList01 is used to store the forward search expanded nodes, and the list Close01 is used to store the forward search path nodes; the list OpenList02 is used to store the backward search expanded nodes, and the list Close02 is used to store the backward search path nodes; add the starting point to OpenList 01 and add the target point to OpenList02. Set the point with the minimum estimated cost in the list OpenList 02 as the forward search target point, perform forward search, add the expanded nodes to the list OpenList 01 and update their estimated costs; the estimated cost is calculated using the improved heuristic function; traverse all the nodes in the list OpenList01, find the node with the minimum estimated cost, and add it to the list CloseList01; set the point with the minimum estimated cost in the list OpenList 01 as the backward search target point, perform backward search, add the expanded nodes to the list OpenList 02 and update their estimated costs; traverse all the nodes in the list OpenList 02, find the node with the minimum estimated cost, and add it to the list CloseList 02; determine whether the list CloseList01 intersects with the list CloseList 02; if they do not intersect, continue with the bidirectional jump point search with positive and negative alternation; if they intersect, generate the forward / backward optimal paths according to their respective parent nodes and merge them to obtain the optimal path of the mobile composite robot.

[0042] In one embodiment, the path smoothing and optimization module is further configured to judge each of the remaining nodes in the optimal path except for the path starting point and the target point to determine whether the node is an inflection point. If it is an inflection point, it is retained; otherwise, it is deleted. The inflection points, starting point, and target point in the path are grouped into sets of three nodes each. The first and third inflection points in each set are connected, and it is judged whether the connection line between these two points passes through an obstacle. If it does, the middle inflection point in this set cannot be deleted; otherwise, the middle inflection point in this set can be deleted, thereby obtaining the smooth optimized path of the mobile composite robot.

[0043] The above-mentioned path planning method and device for the mobile composite robot in the intelligent chemical laboratory, the method includes: modeling the environment of the intelligent chemical laboratory using a grid map, setting obstacles, the starting point, and the target point of the mobile composite robot in the grid map; on the basis of the traditional A* algorithm heuristic function, introducing an obstacle index factor to dynamically weight the estimated cost of the heuristic function to obtain an improved heuristic function; according to the starting point, the target point, and the improved heuristic function, using a forward-backward alternating bidirectional jump point search mechanism to perform a search to obtain the optimal path of the mobile composite robot; using the Floyd algorithm to optimize the optimal path to obtain the smooth optimized path of the mobile composite robot. Using this method can improve the working efficiency of the mobile composite robot in the intelligent chemical laboratory in high-precision tasks such as pipetting, and optimize its path planning to ensure that the robot can quickly and safely reach the target area to assist in completing experimental tasks. Description of the Drawings

[0044] Figure 1 It is a schematic flowchart of the path planning method for the mobile composite robot in the intelligent chemical laboratory in one embodiment;

[0045] Figure 2 It is the grid map in another embodiment;

[0046] Figure 3 It is a schematic diagram of linear motion in another embodiment;

[0047] Figure 4 It is a schematic diagram of diagonal motion in another embodiment;

[0048] Figure 5 It is a schematic diagram of the jump strategy in another embodiment;

[0049] Figure 6 It is a schematic diagram of the JPS algorithm in another embodiment, where Figure 6 (a) is a schematic diagram of jump judgment in the horizontal and vertical directions, Figure 6 (b) is a schematic diagram of diagonal jump judgment, Figure 6 (c) is a schematic diagram of detecting a key node with a forced neighborhood node, Figure 6(d) Schematic diagram of detecting a target node;

[0050] Figure 7 For positive and negative alternating search in another embodiment;

[0051] Figure 8 Schematic diagram of the improved A* algorithm process in another embodiment;

[0052] Figure 9 Schematic diagram of the principle of the Floyd algorithm in another embodiment;

[0053] Figure 10 For the path diagrams before and after the optimization of the Floyd algorithm in another embodiment, where Figure 10 (a) is the initial path diagram, Figure 10 (b) is the path diagram after Floyd optimization;

[0054] Figure 11 For the simulation result diagram in another embodiment, where Figure 11 (a) is the schematic diagram of path planning based on the A* algorithm (map a), Figure 11 (b) is the schematic diagram of path planning based on the improved A* algorithm (map a), Figure 11 (c) is the comparison diagram of path planning results based on the A* algorithm and the improved A* algorithm (map a), Figure 11 (d) is the schematic diagram of path planning based on the traditional A* algorithm (map b), Figure 11 (e) is the schematic diagram of path planning based on the improved A* algorithm (map b), Figure 11 (f) is the comparison diagram of path planning results based on the traditional A* algorithm and the improved A* algorithm (map b). Detailed implementation manner

[0055] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] In one embodiment, as Figure 1 shown, a path planning method for an intelligent chemical laboratory mobile composite robot is provided, and the method includes the following steps:

[0057] Step 100: Model the intelligent chemical laboratory environment using a grid map, and set obstacles, the starting point and the target point of the mobile composite robot in the grid map.

[0058] Specifically, taking the intelligent chemical laboratory as the research background, in the laboratory environment, facilities such as chemical test benches are usually regarded as obstacles. Therefore, path planning needs to fully consider the distribution of these obstacles. The grid method is particularly suitable for dealing with large-scale complex environments because of its simple information acquisition, convenient data processing, ability to display a large amount of detailed information, and low difficulty in feature extraction. It shows good adaptability in terms of the complexity and scale of the environment, and has strong feasibility and easy maintainability, and can accurately restore the working environment of the mobile composite robot. Therefore, this application selects the grid method to model the intelligent chemical laboratory environment, as Figure 2 shown. The map is represented by marking obstacles and obstacle-free spaces in the grid: black represents obstacles, and white is the passable free area. At the same time, considering the safety distance of the mobile composite robot during driving, in order to prevent the mobile composite robot from moving along the edge of the obstacle, all grids are inflated, and obstacles that do not occupy a full grid are inflated to one grid.

[0059] Step 102: On the basis of the heuristic function of the traditional A* algorithm, introduce an obstacle index factor to dynamically weight the estimated cost of the heuristic function to obtain an improved heuristic function.

[0060] Specifically, the traditional A* algorithm is a heuristic search algorithm. This algorithm starts searching from the starting point, estimates the cost of all nodes around the current node, and takes the adjacent node with the smallest total cost value as the next node to visit, and stops searching until it expands to the target point to obtain the optimal path. The heuristic function for the A* algorithm to estimate the cost is:

[0061] f(n) = g(n) + h(n)

[0062] where n represents the current node where the robot is located, f(n) is the estimated cost of the robot from the starting point through node n to the target point, g(n) is the actual cost of the robot from the starting point to node n, and h(n) is the estimated cost of the robot from the intermediate node n to the target point.

[0063] The condition for finding the optimal solution is to make f(n) as close as possible to the true path cost from the starting point S to the target point T. At this time, all the points searched are the path points of the shortest path. Since the value of g(n) is known when traversing the intermediate point N, only the estimation accuracy of h(n) can be improved. The mobile composite robot can move in any direction in the working environment, so the Euclidean distance is selected as the estimated cost function of h(n), and the function is expressed as:

[0064]

[0065] In the formula, (x n , y n ) is the center coordinate of the grid where the current node is located, (xg , y g ) is the center coordinate of the grid where the target point is located.

[0066] When running the A* algorithm, the actual cost g(n) of the node is known, and the estimated cost h(n) plays a dominant role in the search performance of the A* algorithm. By adjusting the estimated cost of the heuristic function, the search accuracy and speed can be further improved. Since the traditional A* algorithm only considers the distance cost and does not take into account the impact of obstacles on the path, it can search for the shortest path rather than a shorter and safer route. Therefore, an obstacle index factor is introduced to dynamically weight the estimated cost of the heuristic function, enabling the cost function to be adaptively adjusted according to the number of obstacles within the rectangular area from the node to the target point. In this way, while improving the search efficiency, the algorithm is encouraged to prefer paths with fewer obstacles, increasing the safety of the path.

[0067] Step 104: According to the starting point, the target point, and the improved heuristic function, use a two-way jump point search mechanism with forward and backward alternation to perform the search and obtain the optimal path of the mobile composite robot; among them, the two-way jump point search mechanism with forward and backward alternation means that through multiple rounds of forward and backward searches, the search stops until the same expanded node appears in the forward and backward searches; in the first round, the forward search uses the set starting point and target point as the starting point and target point respectively, and the backward search uses the set target point as the starting point and the node with the minimum estimated cost among the forward expanded nodes in this round as the target point; in other rounds, the forward search uses the target point of the previous round's backward search as the starting point and the node with the minimum estimated cost among the previous round's backward expanded nodes as the target point, and the backward search uses the target point of the previous round's forward search as the starting point and the node with the minimum estimated cost among the forward search expanded nodes in this round as the target point.

[0068] Specifically, although the A* algorithm based on the grid map has advantages such as small computational complexity and not getting stuck in local optima compared with sampling-based algorithms and various intelligent optimization algorithms, there are still problems such as many traversal points, many turning times, and long operation time. When the traditional A* algorithm performs path planning, it usually takes finding the shortest path as the main goal without considering the smoothness of the path and the search efficiency. However, in the actual operation scenario of the mobile composite robot, the high efficiency of the algorithm is often more important than finding the shortest path. Therefore, on the basis of maintaining the search quality, it is necessary to appropriately reduce the search accuracy to improve the search rate, so as to meet the real-time requirements of the system.

[0069] To address the above problems, the following improvement measures are proposed: First, introduce the jump point search algorithm to reduce the traversal points of the A* algorithm; second, draw on the two-way search mechanism in the bidirectional RRT algorithm to reduce the number of search nodes, shorten the algorithm operation time, and improve the search efficiency. Introduce the two-way jump point search technology: By starting the search simultaneously at the starting point and the end point, the search space is significantly reduced, and the search efficiency is improved.

[0070] Step 106: Optimize the optimal path using the Floyd algorithm to obtain a smooth optimized path for the mobile composite robot.

[0071] Specifically, introduce the idea of the Floyd algorithm to optimize the path. For the problems of many path turns and redundancy, use the Floyd algorithm to optimize the path, reduce the number of turns in the path planning process, and improve the smoothness of the path.

[0072] In the above path planning method for the intelligent chemical laboratory mobile composite robot, the method includes: modeling the intelligent chemical laboratory environment using a grid map, setting obstacles, starting points, and target points of the mobile composite robot in the grid map; on the basis of the traditional A* algorithm heuristic function, introduce an obstacle index factor to dynamically weight the estimated cost of the heuristic function to obtain an improved heuristic function; according to the starting point, target point, and the improved heuristic function, use a forward-backward alternating bidirectional jump point search mechanism to search to obtain the optimal path of the mobile composite robot; use the Floyd algorithm to optimize the optimal path to obtain a smooth optimized path for the mobile composite robot. Using this method can improve the working efficiency of the intelligent chemical laboratory mobile composite robot in high-precision tasks such as pipetting, and optimize its path planning to ensure that the robot can quickly and safely reach the target area to assist in completing experimental tasks.

[0073] In one embodiment, step 102 includes: on the basis of the traditional A* algorithm heuristic function, introduce an obstacle index factor to dynamically weight the estimated cost of the heuristic function to obtain an improved heuristic function; the expression of the improved heuristic function is:

[0074]

[0075] Among them, n represents the current node where the robot is located, f(n) is the estimated cost for the robot to reach the target point from the starting point via node n, g(n) is the actual cost for the robot to reach node n from the starting point, h(n) is the estimated cost for the robot to reach the target point from the intermediate node n, and P(o) is the ratio of the area occupied by obstacles in the current point to the end area.

[0076] In one embodiment, the expression for the ratio of the area occupied by obstacles in the current point to the end area is:

[0077]

[0078] Among them, dx is the vertical displacement from the current expanded node to the target point, dy is the horizontal displacement from the current expanded node to the target point, n is the number of obstacle grids in the rectangular area composed of dx and dy, and S is the area of a single obstacle.

[0079] Specifically, when running the A* algorithm, the actual cost g(n) of a node is known, and the estimated cost h(n) plays a dominant role in the search performance of the A* algorithm. The estimated cost value The closer it is to the actual required path value Q, the more accurate the search. Therefore, choosing an appropriate estimated cost is the key to the A* algorithm. Also, because the traditional A* algorithm only considers the distance cost and does not take into account the impact of obstacles on the path, it can find the shortest path rather than a shorter and safer route.

[0080] In summary, the present application introduces an obstacle index factor to dynamically weight the estimated cost of the heuristic function. When there are more obstacles in the area from the current node to the target point, the estimated cost value increases, the search speed becomes faster, and the algorithm tends to search for paths with fewer obstacles. When there are fewer obstacles in the area, the algorithm tends to search for the optimal path and slows down the search speed. In this way, while improving the search efficiency, the algorithm encourages itself to prefer paths with fewer obstacles, increasing the safety of the path. The improved heuristic function is as shown in the expression of the above-mentioned improved heuristic function.

[0081] In one of the embodiments, step 104 includes: initializing lists OpenList01, OpenList02, Close01, and Close02; where the list OpenList01 is used to store forward search expanded nodes, and the list Close01 is used to store forward search path nodes; the list OpenList02 is used to store backward search expanded nodes, and the list Close02 is used to store backward search path nodes; add the starting point to OpenList 01 and the target point to OpenList 02; set the point with the minimum estimated cost in the list OpenList 02 as the forward search target point, perform forward search, add the expanded nodes to the list OpenList 01 and update their estimated costs; the estimated cost is calculated using the improved heuristic function; traverse all the nodes in the list OpenList01, find the node with the minimum estimated cost, and add it to the list CloseList01; set the point with the minimum estimated cost in the list OpenList 01 as the backward search target point, perform backward search, add the expanded nodes to the list OpenList 02 and update their estimated costs; traverse all the nodes in the list OpenList 02, find the node with the minimum estimated cost, and add it to the list CloseList 02; determine whether the list CloseList01 intersects with the list CloseList 02; if they do not intersect, continue with the forward and backward alternating bidirectional jump point search; if they intersect, generate forward and backward optimal paths based on their respective parent nodes and merge them to obtain the optimal path of the mobile composite robot.

[0082] Specifically, the Jump Point Search (JPS) algorithm is used to find the shortest path between two points on a grid map. By screening out valuable nodes and reducing meaningless neighborhood expansion nodes, the JPS algorithm reduces the computational complexity in global path planning and improves the search efficiency. It mainly consists of two parts: forced neighborhood node and jump point judgment, and jumping strategy.

[0083] (1) Forced neighborhood node and jump point judgment

[0084] During the neighborhood search process of P(x), if there is an obstacle in any of the eight directions around its child node X, and the distance cost from node X to the target node G(x) is smaller than that of other paths, then G(x) is defined as the forced neighborhood node of X. At the same time, node X is the jump point from P(x) to G(x). Jump points are divided into four categories: start point, end point, straight-line jump point, and diagonal jump point. Among them, the start point and end point are defaulted to jump points, while the straight-line jump point and diagonal jump point need to be determined through jump point judgment.

[0085] For straight-line motion as Figure 3 shown, when the mobile composite robot moves straight in grid X, there are obstacles on both sides of the moving direction, and there is an accessible point G(X) in the diagonal direction, then grid X is a jump point and G(X) is a forced neighborhood node.

[0086] For diagonal motion as Figure 4 shown, when the mobile composite robot makes a diagonal motion in grid X, it is judged whether there is an obstacle in the side-rear direction of the moving direction. If there is an obstacle, then grid X is a jump point and G(X) is a forced neighborhood node.

[0087] (2) Jumping strategy

[0088] The process from P(x) to its child node X with a forced neighborhood node is called jumping. Jumping is divided into straight-line jumping and diagonal jumping. Straight-line jumping is further divided into horizontal jumping and vertical jumping.

[0089] The jumping strategy is as Figure 5 shown. The dotted line represents straight-line jumping, and the solid line represents diagonal jumping. Usually, straight-line jumping is performed first. If an obstacle or boundary is encountered, then return to the parent node P(x) for diagonal jumping. If jumping to a node with a forced neighborhood node, an obstacle, or a boundary, then stop jumping.

[0090] As Figure 6 shows the complete jumping process of the JPS algorithm. As Figure 6 (a), the algorithm starts from the current node and preferentially performs jumping judgment in the horizontal and vertical directions. If no key nodes are found in these two directions, then it will switch to the diagonal direction for jumping judgment, as Figure 6(as shown in (b)); when performing a horizontal jump judgment on the nodes explored for the fourth time, the algorithm detected a key node with forced neighborhood nodes, which is identified by 1 in the figure, as Figure 6 (as shown in (c)); subsequently, the algorithm uses the node identified by 1 as a new starting point and continues the jump judgment. First, it makes judgments in the horizontal and vertical directions. If no key nodes are detected in these two directions, it will switch to the diagonal direction for detection. During this process, the algorithm successfully finds another key node in the diagonal direction and then continues to explore along the new key node, finally successfully locating the target node, as Figure 6 (as shown in (d)).

[0091] Bidirectional search is a method of searching simultaneously from the starting point and the target point. Its basic idea is to search separately from the starting point and the target point. When a common node is encountered during the bidirectional search process, the path search is successful. However, it is also possible that the same expanded nodes are not found in both search directions throughout the exploration process, resulting in the failure of the path search.

[0092] This application adopts a bidirectional search mechanism of forward and backward alternating search, which can effectively prevent the failure of path search. The forward and backward alternating search mechanism is as Figure 7 shown, where S is the starting point of the forward search, that is, the starting node of the algorithm, and G is the starting point of the backward search, that is, the target point of the algorithm. First, start the forward search with S as the starting point and G as the target point to obtain the expanded node S1. Subsequently, start the backward search, with point G as the starting point and S1 as the target point for the backward search to obtain the expanded sub-node G1. Search in this cycle until the same expanded node appears in both the forward and backward directions, then stop the search. At this time, the obtained path is the optimal path.

[0093] Combining the jump point search and the bidirectional search into a new search method, namely the bidirectional jump point search, can reduce the traversed points, the number of searched nodes, shorten the running time of the algorithm, and improve the search efficiency of the algorithm.

[0094] The improved A* algorithm process is as Figure 8 shown.

[0095] In one embodiment, step 106 includes: for the nodes in the optimal path except the path starting point and the target point, judge each of these nodes to determine whether it is an inflection point. If it is an inflection point, retain it; if not, delete it; divide the inflection points, starting point, and target point in the path into groups of three nodes each, connect the first and third inflection points in each group, and judge whether the connection line between these two points passes through an obstacle. If it does, the middle inflection point in this group cannot be deleted; otherwise, the middle inflection point in this group can be deleted, to obtain the smooth optimized path of the mobile composite robot.

[0096] Specifically, after incorporating jump point search, the path planned by the A* algorithm will have a large number of turning points. Therefore, the Floyd algorithm is used to optimize the generated path, removing redundant points and reducing turning points. The Floyd algorithm, also known as the interpolation method, is used to solve the shortest distance between two points. Its principle is as Figure 9 shown. This algorithm determines the shortest path between any two points by gradually traversing the potential paths between all adjacent nodes and updating the path matrix. During this process, the algorithm removes redundant nodes and reduces the turning points in the path.

[0097] Define D(A,D) to represent the distance between two points, and P(A,B,D) to represent that point A needs to pass through node B to reach point D. If there is an obstacle between two points, the distance between the two points is set to infinity, i.e., D(A,D) = ∞. If there is an intermediate point C between B and D, and D(A,C) + D(C,D) < D(A,B) + D(B,D), then D(A,D) = D(A,C) + D(C,D), discard B as the passing point, replace it with C, and change P(A,B,D) to P(A,C,D).

[0098] The path diagrams before and after the Floyd algorithm optimization are as Figure 10 shown, where Figure 10 (a) is the initial path diagram, Figure 10 (b) is the path diagram after Floyd optimization. It can be seen from Figure 10 that the number of turning points in the optimized path is significantly reduced, and at the same time, the smoothness of the path is better.

[0099] It should be understood that although Figure 1 the steps in the flowchart are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 at least a part of the steps in Figure 1 can include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0100] In a verification embodiment, in order to verify the effectiveness of this method, a 30×30 grid map environment model is established to simulate the algorithm, and the simulations of the traditional A* algorithm and the improved A* algorithm proposed in this application under different maps are compared. The experimental results are as Figure 11 shown, where Figure 11 (a) is the path planning schematic diagram based on the A* algorithm (map a), Figure 11(b) Schematic diagram of path planning based on the improved A* algorithm (Map a). Figure 11 (c) Comparison chart of path planning results based on the A* algorithm and the improved A* algorithm (Map a). Figure 11 (d) Schematic diagram of path planning based on the traditional A* algorithm (Map b). Figure 11 (e) Schematic diagram of path planning based on the improved A* algorithm (Map b). Figure 11 (f) Comparison chart of path planning results based on the traditional A* algorithm and the improved A* algorithm (Map b).

[0101] Table 1 Performance comparison table of the traditional A* algorithm and the improved A* algorithm

[0102]

[0103] According to Figure 11 and the simulation results in Table 1, compared with the traditional A* algorithm, the improved A* algorithm (this method) has achieved significant improvements in terms of path length, path turning angle, and search efficiency. Especially in terms of path smoothness, the effect of the improved algorithm is particularly prominent, successfully achieving the expected optimization goal. This improvement not only significantly shortens the path length, but also significantly reduces the number of inflection points and the total turning angle in the path. At the same time, by optimizing the search strategy, the search efficiency is greatly improved. These optimization results provide strong support for the efficient path planning of mobile composite robots in intelligent chemical laboratories, ensuring that they can reach the target position quickly and smoothly in complex environments.

[0104] In one embodiment, a path planning device for a mobile composite robot in an intelligent chemical laboratory is provided, including: an environment modeling module, a heuristic function improvement module, a path exploration module, and a path planning module, where:

[0105] The environment modeling module is used to model the intelligent chemical laboratory environment using a grid map, and set obstacles, the starting point, and the target point of the mobile composite robot in the grid map.

[0106] The heuristic function improvement module is used to introduce an obstacle index factor to dynamically weight the estimated cost of the heuristic function on the basis of the heuristic function of the traditional A* algorithm, and obtain an improved heuristic function.

[0107] A path exploration module, which is used to search according to the starting point, the target point and the improved heuristic function, and adopt a two-way jump point search mechanism with alternating forward and backward directions to obtain the optimal path of the mobile composite robot; wherein, the two-way jump point search mechanism with alternating forward and backward directions means that through multiple rounds of forward and backward searches, the search stops until the same expanded node appears in the forward and backward searches; in the first round, the forward search uses the set starting point and target point as the starting point and target point respectively, and the backward search uses the set target point as the starting point and the node with the smallest estimated cost among the forward expanded nodes in this round as the target point; in other rounds, the forward search uses the target point of the previous round of backward search as the starting point and the node with the smallest estimated cost among the backward expanded nodes in the previous round as the target point, and the backward search uses the target point of the previous round of forward search as the starting point and the node with the smallest estimated cost among the forward search expanded nodes in this round as the target point.

[0108] A path smoothing and optimization module, which is used to optimize the optimal path by using the Floyd algorithm to obtain the smooth and optimized path of the mobile composite robot.

[0109] In one embodiment, the heuristic function improvement module is further used to introduce an obstacle index factor to dynamically weight the estimated cost of the heuristic function on the basis of the traditional A* algorithm heuristic function, so as to obtain the improved heuristic function as shown in the above improved heuristic function expression.

[0110] In one embodiment, the area ratio of obstacles in the current point to the end area is as shown in the above expression of the area ratio of obstacles in the previous point to the end area.

[0111] In one embodiment, the path exploration module is further configured to initialize the lists OpenList01, OpenList02, Close01, and Close02; where the list OpenList01 is used to store forward search expanded nodes, and the list Close01 is used to store forward search path nodes; the list OpenList02 is used to store backward search expanded nodes, and the list Close02 is used to store backward search path nodes; add the starting point to OpenList 01 and add the target point to OpenList02. Set the point with the minimum estimated cost in the list OpenList 02 as the forward search target point, perform forward search, add the expanded nodes to the list OpenList 01 and update their estimated costs; the estimated cost is calculated using an improved heuristic function; traverse all the nodes in the list OpenList01, find the node with the minimum estimated cost, and add it to the list CloseList01; set the point with the minimum estimated cost in the list OpenList 01 as the backward search target point, perform backward search, add the expanded nodes to the list OpenList 02 and update their estimated costs; traverse all the nodes in the list OpenList 02, find the node with the minimum estimated cost, and add it to the list CloseList 02; determine whether the list CloseList01 intersects with the list CloseList 02; if they do not intersect, continue with the forward and backward alternating bidirectional jump point search; if they intersect, generate forward and backward optimal paths based on their respective parent nodes and merge them to obtain the optimal path of the mobile composite robot.

[0112] In one embodiment, the path smoothing and optimization module is further configured to, for the nodes in the optimal path except the path starting point and the target point, respectively determine whether the node is an inflection point. If it is an inflection point, keep it; otherwise, delete it; divide the inflection points, starting point, and target point in the path into groups of three nodes each, connect the first and third inflection points in each group, and determine whether the line connecting these two points passes through an obstacle. If it does, the middle inflection point in this group cannot be deleted; otherwise, the middle inflection point in this group can be deleted, to obtain the smoothed and optimized path of the mobile composite robot.

[0113] For the specific limitations of the path planning device of the intelligent chemical laboratory mobile composite robot, reference can be made to the limitations of the path planning method of the intelligent chemical laboratory mobile composite robot in the above text, which will not be elaborated here. Each module in the above path planning device of the intelligent chemical laboratory mobile composite robot can be implemented in whole or in part through software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0114] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0115] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several variations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A path planning method for an intelligent chemical laboratory mobile composite robot, characterized in that, The method includes: Modeling the intelligent chemical laboratory environment using a grid map, setting obstacles, as well as the starting point and target point of the mobile composite robot in the grid map; Based on the traditional A* algorithm heuristic function, introducing an obstacle exponential factor to dynamically weight the estimated cost of the heuristic function to obtain an improved heuristic function; According to the starting point, the target point, and the improved heuristic function, using a forward-backward alternating bidirectional jump point search mechanism to search for the optimal path of the mobile composite robot; wherein, the forward-backward alternating bidirectional jump point search mechanism means that through multiple rounds of forward and backward searches, the search stops until the same expanded node appears in the forward and backward searches; in the first round, the forward search uses the set starting point and target point as the starting point and target point respectively, and the backward search uses the set target point as the starting point and the node with the minimum estimated cost among the forward expanded nodes in this round as the target point; in other rounds, the forward search uses the target point of the previous round of backward search as the starting point and the node with the minimum estimated cost among the backward expanded nodes in the previous round as the target point, and the backward search uses the target point of the previous round of forward search as the starting point and the node with the minimum estimated cost among the forward search expanded nodes in this round as the target point; Using the Floyd algorithm to optimize the optimal path to obtain a smooth optimized path for the mobile composite robot.

2. The path planning method of the intelligent chemical laboratory mobile composite robot according to claim 1, wherein, Based on the traditional A* algorithm heuristic function, introducing an obstacle exponential factor to dynamically weight the estimated cost of the heuristic function, and the improved heuristic function is: Where n represents the current node where the robot is located, f(n) is the estimated cost for the robot to reach the target point from the starting point via node n, g(n) is the actual cost for the robot to reach node n from the starting point, h(n) is the estimated cost for the robot to reach the target point from the intermediate node n, and P(o) is the ratio of the area occupied by obstacles from the current point to the end area.

3. The path planning method for the intelligent chemical laboratory mobile composite robot according to claim 2, characterized in that, The ratio of the area occupied by obstacles from the current point to the end area is: Where dx is the vertical displacement from the current expanded node to the target point, dy is the horizontal displacement from the current expanded node to the target point, n is the number of obstacle grids in the rectangular area composed of dx and dy, and S is the area of a single obstacle.

4. The path planning method of the intelligent chemical laboratory mobile composite robot according to claim 1, wherein According to the starting point, the target point, and the improved heuristic function, using a forward-backward alternating bidirectional jump point search mechanism to search for the optimal path of the mobile composite robot, including: Initializing lists OpenList01, OpenList02, Close01, and Close02; where list OpenList01 is used to store forward search expanded nodes, and list Close01 is used to store forward search path nodes; list OpenList02 is used to store backward search expanded nodes, and list Close02 is used to store backward search path nodes; Adding the starting point to OpenList 01 and adding the target point to OpenList 02; Set the point with the minimum estimated cost in the OpenList 02 as the forward search target point, perform forward search, add the expanded nodes to the OpenList 01 and update their estimated costs; the estimated cost is calculated using the improved heuristic function; Traverse all the nodes in the OpenList01, find the node with the minimum estimated cost, and add it to the CloseList01; Set the point with the minimum estimated cost in the OpenList 01 as the backward search target point, perform backward search, add the expanded nodes to the OpenList 02 and update their estimated costs; Traverse all the nodes in the OpenList 02, find the node with the minimum estimated cost, and add it to the CloseList 02; Determine whether the CloseList01 and the CloseList 02 intersect; If they do not intersect, continue the forward-backward alternating bidirectional jump point search; If they intersect, generate the forward / backward optimal paths according to their respective parent nodes and merge them to obtain the optimal path of the mobile composite robot.

5. The path planning method for the intelligent chemical laboratory mobile composite robot according to claim 1, wherein Optimize the optimal path using the Floyd algorithm to obtain the smooth optimized path of the mobile composite robot, including: For the nodes in the optimal path except the start point and the target point of the path, judge whether the node is an inflection point. If it is an inflection point, keep it; otherwise, delete it; Divide the inflection points, start point and target point in the path into groups of three nodes each. Connect the first and third inflection points in each group, and judge whether the line connecting these two points passes through an obstacle. If it does, the middle inflection point in this group cannot be deleted; otherwise, the middle inflection point in this group can be deleted to obtain the smooth optimized path of the mobile composite robot.

6. An intelligent chemical laboratory mobile composite robot path planning device, characterized in that, The device includes: An environment modeling module for modeling the intelligent chemical laboratory environment using a grid map, setting obstacles, the start point and the target point of the mobile composite robot in the grid map; A heuristic function improvement module for introducing an obstacle index factor to dynamically weight the estimated cost of the heuristic function on the basis of the traditional A* algorithm heuristic function to obtain the improved heuristic function; A path exploration module for searching using the start point, the target point and the improved heuristic function and adopting a forward-backward alternating bidirectional jump point search mechanism to obtain the optimal path of the mobile composite robot; wherein, the forward-backward alternating bidirectional jump point search mechanism means that through multiple rounds of forward and backward searches, stop the search until the same expanded node appears in the forward and backward searches; in the first round, the forward search uses the set start point and target point as the start point and the target point respectively, and the backward search uses the set target point as the start point and the node with the minimum estimated cost among the forward expanded nodes in this round as the target point; in other rounds, the forward search uses the target point of the previous round of backward search as the start point and the node with the minimum estimated cost among the previous round of backward expanded nodes as the target point, and the backward search uses the target point of the previous round of forward search as the start point and the node with the minimum estimated cost among the forward search expanded nodes in this round as the target point; A path smoothing and optimization module, which is used to optimize the optimal path by using the Floyd algorithm to obtain a smoothed and optimized path for the mobile composite robot.

7. The path planning device for the intelligent chemical laboratory mobile composite robot according to claim 6, characterized in that, The heuristic function improvement module is further used to introduce an obstacle exponential factor to dynamically weight the estimated cost of the heuristic function on the basis of the traditional A* algorithm heuristic function, and the improved heuristic function is obtained as follows: Where, n represents the current node where the robot is located, f(n) is the estimated cost for the robot to reach the target point from the starting point through node n, g(n) is the actual cost for the robot to reach node n from the starting point, h(n) is the estimated cost for the robot to reach the target point from the intermediate node n, and P(o) is the ratio of the area occupied by obstacles in the current point to the end area.

8. The path planning device for the intelligent chemical laboratory mobile composite robot according to claim 7, wherein, The ratio of the area occupied by obstacles in the current point to the end area is: Where, dx is the vertical displacement from the current expanded node to the target point, dy is the horizontal displacement from the current expanded node to the target point, n is the number of obstacle grids in the rectangular area composed of dx and dy, and S is the area of a single obstacle.

9. The path planning device for the intelligent chemical laboratory mobile composite robot according to claim 6, characterized in that, The path exploration module is further used to initialize the lists OpenList01, OpenList02, Close01, and Close02; among them, the list OpenList01 is used to store the forward search expanded nodes, and the list Close01 is used to store the forward search path nodes; the list OpenList02 is used to store the backward search expanded nodes, and the list Close02 is used to store the backward search path nodes; add the starting point to OpenList 01, and add the target point to OpenList 02; set the point with the minimum estimated cost in the list OpenList 02 as the forward search target point, perform forward search, add the expanded nodes to the list OpenList 01 and update their estimated costs; The estimated cost is calculated by using the improved heuristic function; traverse all the nodes in the list OpenList01, find the node with the minimum estimated cost, and add it to the list CloseList01; set the point with the minimum estimated cost in the list OpenList 01 as the backward search target point, perform backward search, add the expanded nodes to the list OpenList02 and update their estimated costs; traverse all the nodes in the list OpenList 02, find the node with the minimum estimated cost, and add it to the list CloseList 02; determine whether the list CloseList01 intersects with the list CloseList 02; if they do not intersect, continue the bidirectional jump point search with positive and negative alternation; if they intersect, generate the forward / backward optimal path according to their respective parent nodes and merge them to obtain the optimal path of the mobile composite robot.

10. The path planning device for the intelligent chemical laboratory mobile composite robot according to claim 6, wherein, The path smoothing and optimization module is further configured to judge the remaining nodes in the optimal path except for the path starting point and the target point respectively to determine whether the node is an inflection point. If it is an inflection point, it is retained; otherwise, it is deleted. The inflection points, starting point, and target point in the path are divided into groups of three nodes each. The first and third inflection points in each group are connected, and it is judged whether the connection line between these two points passes through an obstacle. If it does, the middle inflection point in this group cannot be deleted; otherwise, the middle inflection point in this group can be deleted, so as to obtain the smoothed and optimized path of the mobile composite robot.

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