CN116878527A - Hybrid path planning method and device based on improved adaptive window algorithm - Google Patents

Hybrid path planning method and device based on improved adaptive window algorithm Download PDF

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CN116878527A
CN116878527A CN202310824466.2A CN202310824466A CN116878527A CN 116878527 A CN116878527 A CN 116878527A CN 202310824466 A CN202310824466 A CN 202310824466A CN 116878527 A CN116878527 A CN 116878527A
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余兰林
赵世钰
都海波
孙宝奕
莫远秋
陈飞
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Westlake University
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Abstract

The invention discloses a hybrid path planning method and a device based on an improved self-adaptive window algorithm, comprising the following steps: (1) obtaining an environment-initiated grid map through SLAM; (2) Determining a starting point S and a target point G of the robot in the grid map; (3) global path planning using an improved a-star algorithm; (4) Finding a target point G through an improved A star algorithm to obtain a global Path; (5) Sequentially selecting a starting point, an inflection point and an end point from the global Path as key points, and taking the starting point, the inflection point and the end point as a sub-target point set in local Path planning; (6) Performing local path planning by using an improved adaptive window algorithm; (7) When the robot searches the final target point G, the local path planning is completed, and the hybrid path planning from the starting point S to the target point G is completed. By utilizing the invention, the reliability and the safety of the robot in the obstacle avoidance and road finding process can be improved.

Description

Hybrid path planning method and device based on improved adaptive window algorithm
Technical Field
The invention belongs to the field of path planning, and particularly relates to a hybrid path planning method and device based on an improved self-adaptive window algorithm.
Background
Path planning is a key technology of robots moving autonomously and is also a hotspot problem of navigation research of mobile robots. Path planning can be classified into global path planning (Global Path Planning, abbreviated as GPP) and local path planning (Local Path Planning, abbreviated as LPP) according to the nature of the environment and the target. For global path planning, common algorithms include an a x (a star) algorithm, a Dijkstra algorithm, and the like. For local path planning, common algorithms include an artificial potential field method, a dynamic window algorithm, an adaptive window algorithm and the like. Although the purpose of obstacle avoidance and path finding and tracking in path planning can be achieved by using a global path planning algorithm and a local path planning algorithm respectively, the safety level in the running process of the robot is still insufficient. Under some practical application conditions and environments, such a level of security is insufficient. Therefore, how to improve the safety and reliability of the path planning algorithm in the complex environment and find the optimal path is a technical problem.
The conventional a-algorithm is mainly used in a static state and a known surrounding environment, and is a heuristic traversal search algorithm based on Dijkstra algorithm and an optimal priority search algorithm (Best First Search, abbreviated as BFS), and when a path is planned, not only the distance between itself and the nearest node position (Dijkstra implementation) but also the distance between itself and a target point (optimal priority search algorithm implementation) are required to be considered. As disclosed in chinese patent document CN115046557a, an AGV path planning method combining a B-spline curve and an a-star algorithm obtains starting point information, end point information and constraint conditions of a path to be planned from an upper computer, generates a starting point direction control node according to the starting point information and a starting point control constraint distance, and generates an end point direction control node according to the end point information and the end point control constraint distance.
Compared with the traditional traversal search algorithm, the traditional A algorithm compensates a greedy mechanism in the search process, so that the task of path planning is well completed, but the method is not applicable to the situations of dynamic environment, unknown environment or unknown partial environment.
The adaptive window algorithm (Adaptive Window Approach, abbreviated as AWA) is improved on the basis of the dynamic window algorithm (Dynamic Window Approach, abbreviated as DWA), so that the planning window range of the dynamic window algorithm can be adjusted according to the environment condition and the setting requirement of path planning in an adaptive manner, and a better path planning result is obtained. The dynamic window algorithm is a local path planning algorithm, and can avoid obstacles in an unknown environment. The method comprises the following steps of determining a sampling speed space meeting the hardware constraint of the mobile robot in a speed space (v, omega) according to the current position state and the speed state of the mobile robot, calculating a track of the mobile robot moving for a certain time under the condition of the sampling speed space, evaluating the track through an evaluation function, and finally selecting the speed corresponding to the track with the optimal evaluation as the movement speed of the mobile robot, and circulating until the mobile robot reaches a target point. However, in the process of searching for the sub-target points to perform path planning, the dynamic window algorithm only expands one robot radius on each side of the obstacle to avoid the obstacle as shown in fig. 6, so that the safety is insufficient. While passing through a narrow passage, the robot passes through a certain risk. Meanwhile, in practical application, the travelling route of the robot and the planned route have a certain deviation, and the obstacle avoidance logic of the dynamic window algorithm is insufficient to fully ensure the safety of the robot path planning. The adaptive window algorithm only improves the self-adaptive change of the range of the planning window on the basis of the dynamic window algorithm, and does not improve the safety problem. In other words, the security deficiency problem of the dynamic window algorithm still exists in the adaptive window algorithm.
Disclosure of Invention
The invention provides a hybrid path planning method and device based on an improved self-adaptive window algorithm, which can improve the reliability and safety of a robot obstacle avoidance and path finding process.
A hybrid path planning method based on an improved adaptive window algorithm, comprising the steps of:
(1) Acquiring an environment-initiated grid map by an immediate localization and mapping method SLAM;
(2) Determining a starting point S and a target point G of the robot in the grid map;
(3) Performing global path planning by using an improved A star algorithm; introducing a risk function into a cost function of the improved A star algorithm, estimating a risk value of a grid node in the path searching process, and selecting a node with low cost value for expansion;
(4) Finding a target point G through an improved A star algorithm to obtain a global Path;
(5) Sequentially selecting a starting point, an inflection point and an end point from the global Path as key points, and taking the starting point, the inflection point and the end point as a sub-target point set in local Path planning; wherein, the starting point in the first sub-target point set is S, and the end point in the last sub-target point set is G;
(6) Performing local path planning by using an improved adaptive window algorithm; the improved self-adaptive window algorithm combines the self-adaptive window algorithm with an artificial potential field method, and designs U-shaped trap escape logic;
(7) When the robot searches the final target point G, the local path planning is completed, and the hybrid path planning from the starting point S to the target point G is completed.
Further, in the step (3), the specific process of global path planning using the improved a star algorithm is as follows:
(3-1) establishing an open table and a close table which are respectively used for storing nodes to be expanded and expanded nodes;
(3-2) calculating a cost function value of the starting point S, and putting S into the open table; wherein, the cost function is as follows:
F(n)＝G(n)+H(n)+R(n)
where G (n) is the actual distance from the starting point S to the current point n, H (n) is the estimated distance from the current point n to the target point G, and R (n) is the risk value of the current point n.
Where α is a risk factor greater than zero and r is the distance of the current point n from the nearest obstacle.
(3-3) comparing the cost function values of each point in the open table, if the open table is empty, the algorithm fails; if the open table is not empty, selecting a node P with the minimum cost function value, and moving the node P out of the open table to a close table;
(3-4) judging whether the point P is the target point G: if yes, carrying out the step (3-7); if not, carrying out the step (3-5);
(3-5) respectively expanding and processing the adjacent nodes of different types P according to the logic of the expanded adjacent nodes; the logic of the extended neighbor node specifically comprises:
the neighboring nodes have three cases: if the adjacent node is an obstacle point or in the close table, not processing; if the neighbor node is not expanded, calculating a cost function value of the neighbor node and putting the cost function value into an open table; if the neighbor node is already in the open table, comparing the G (n) size, and judging whether to update the parent node;
(3-6) adding or updating information to the nodes in the open table according to the situation in the step (3-5), and jumping to the step (3-3);
and (3-7) finding a target point, reversely pushing back to the starting point according to the father node, finding a path, completing global path planning, and ending the circulation.
In the step (6), the specific process of using the improved adaptive window algorithm to perform local path planning is as follows:
(6-1) the robot obtains local environment information through a laser radar, determines a laser radar range d, and equally divides the radar range into n parts;
(6-2) adaptively adjusting a planning window range according to the environmental information detected by the laser radar; when planningWhen the feasible area in the window is larger, the range of the planning window is enlarged; when the feasible area is smaller, the planning window range is reduced; determining window range r win Angular resolution delta Φ Robot orientation G R Etc.
(6-3) the sub-target Point of the current search is G t The points are searched according to the sub-target point set sequence;
(6-4) judging whether the final target point G is searched; if yes, ending the cycle, and jumping to the step (10); if not, carrying out the step (6-5);
(6-5) determining whether G exists in the planning window t Sub-target point G after point t+x Or have G t Point and G t+1 And (3) if the point R on the point connecting line exists, performing the step (6-6); if not, jumping to (6-7);
(6-6) changing the sub-target point of the current search to G t+x Performing the step (6-7) on the point or the R point, and continuously searching the changed sub-target point;
(6-7) introducing a repulsive force potential field in an artificial potential field method, expanding obstacles in the range of a planning window, and determining a feasible region in the range of the planning window; the specific process is as follows:
according to radius r of robot rob Expanding the edge of the obstacle, and considering the obstacle and the expansion part thereof as an infeasible area; at the same time, a repulsive force potential field in an artificial potential field method is introduced, and a repulsive force value F req Directions exceeding a preset value are also considered as infeasible areas; other portions of the planning window are viable areas, except for non-viable areas.
(6-8) judging whether a feasible region exists in the range of the planning window: if yes, carrying out (6-9); if not, jumping to (6-11);
(6-9) after determining the feasible region, combining the closest sub-target point G within the feasible region t Determining the advancing direction of the robot at the next moment, and jumping to (6-1) for circulation;
(6-10) ending the local path planning cycle, and jumping to the step (7);
(6-11) determining whether the robot is trapped in the U-trap and executing the U-trap escape logic. The U-trap escape logic is specifically as follows:
the current position of the robot is taken as the origin of coordinates, the current direction is taken as the positive direction, a rectangular coordinate system is established, and a sub-target point G searched at present is used t Carrying out local path planning with the position relation of the robot;
when the sub-target point G t The robot is positioned in the first quadrant, falls into the U-shaped trap, and is enabled to turn right to a certain angle until a feasible region exists in the planning window;
when the sub-target point G t The robot is positioned in the second quadrant, falls into the U-shaped trap, and turns left by a certain angle until a feasible region exists in the planning window;
when the sub-target point G t In the third and fourth quadrants, the robot does not sink into the U-shaped trap, and the robot autonomously searches for the sub-target point G according to the logic of the improved adaptive window algorithm t 。
A hybrid path planning apparatus based on an improved adaptive window algorithm, comprising a memory and one or more processors, the memory having executable code stored therein, the one or more processors, when executing the executable code, being configured to implement the hybrid path planning method described above.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention researches the problem of robot path planning in a complex environment, and divides the path planning in the complex dynamic environment into two parts, namely global (static) path planning and local (dynamic) path planning, which are independent and auxiliary by considering different environments and robot information. The algorithm logic of the two parts together form the invention, so that the invention can realize road finding and obstacle avoidance with higher reliability and safety under complex dynamic environment.
2. Compared with the traditional A-type algorithm, the improved A-type algorithm is used in global path planning, the cost function is modified, and a risk function is introduced to estimate the risk value of the grid points in the path searching process, so that the grid points with smaller cost value and lower risk value are selected for expansion. The algorithm can be directly used on an environment initial grid map obtained by an instant positioning and map construction method (SLAM for short), so that the complexity of map processing is reduced, and the safety of a final road finding result is improved.
3. The invention is in the local path planning: by combining the adaptive window algorithm with the artificial potential field method, the obstacle suddenly appearing in the local environment can be processed. In the course of seeking, the range of the planning window is adjusted automatically according to the size of the feasible region, so that the path planning efficiency is improved. The repulsive force potential field in the artificial potential field method can be used for judging the repulsive force value in the range of the planning window, so that a feasible region can be further screened, the repulsive force value can be set autonomously according to the practical application environment and the purpose requirement, and the region with smaller repulsive force value and farther distance from the obstacle is selected as the feasible region, so that the safety in the road searching process is improved.
4. In the invention, the consideration of the situation of the U-shaped trap is added, in a complex environment, the suddenly-appearing obstacle and the original obstacle possibly form the U-shaped trap, and when the robot enters the U-shaped trap, path planning fails because no feasible area exists in the range of the planning window. Therefore, the invention designs the U-shaped trap escape logic, and the robot is respectively enabled to make corresponding actions for three conditions of no feasible region in the range of the planning window to escape from the U-shaped trap, thereby ensuring that the robot can continue to normally find a path and improving the reliability of finding the path.
Drawings
FIG. 1 is a flow chart of a hybrid path planning method based on an improved adaptive window algorithm of the present invention;
FIG. 2 is a flow chart of global path planning using a modified A-algorithm in accordance with the present invention;
FIG. 3 is a flow chart of the present invention for local path planning using a modified adaptive window algorithm;
FIG. 4 is a flow chart of the present invention using U-shaped trap escape logic;
fig. 5 is a schematic diagram comparing the path planning result based on the modified a-algorithm with the path planning result of the conventional a-algorithm in the present invention;
FIG. 6 is a schematic diagram of adaptive planning window and obstacle edge extension in the present invention.
Detailed Description
The invention will be described in further detail with reference to the drawings and examples, it being noted that the examples described below are intended to facilitate the understanding of the invention and are not intended to limit the invention in any way.
As shown in fig. 1, a hybrid path planning method based on an improved adaptive window algorithm includes the following steps:
s1, obtaining an initial grid map by processing picture information through SLAM; inputting basic information of the robot, such as: radius r of robot rob Maximum speed v max Maximum angular velocity w max Etc.
S2, determining a starting point S and a target point G in the grid map.
And S3 (GPP), performing global path planning by using a secure A star algorithm (modified A star algorithm).
As shown in fig. 2, the specific flow of the path planning of the secure a-algorithm is:
and GPP (1) establishes an open list and a close list which are respectively used for storing the nodes to be expanded and the nodes which are expanded.
GPP (2) calculates a cost function value for starting point S, and places S in open table. The cost function is as follows:
F(n)＝G(n)+H(n)+R(n)
where G (n) is the actual distance from the starting point S to the current point n, H (n) is the estimated distance from the current point n to the target point G, and R (n) is the risk value of the current point n.
Where α is a risk factor greater than zero and r is the distance of the current point n from the nearest obstacle.
GPP (3) compares the cost function value of each point in open list, if open list is empty, algorithm fails; if the open table is not empty, the node P with the smallest cost function value is selected and removed from the open table to the close table.
GPP (4) determines whether or not point P is target point G: if yes, go to step GPP (7); if not, go to step GPP (5).
And GPP (5) respectively expanding and processing the adjacent nodes of different P types according to the logic of the expanded adjacent nodes.
GPP (6) adds or updates information to the nodes in the open table according to the situation in step GPP (5), and jumps to step GPP (3).
GPP (7) finds out target point, and pushes back the starting point according to father node, finds out path, completes global path planning, and ends cycle.
And S4, finding a target point G through a safe A-algorithm to obtain a global Path. The global path obtained by the secure a-algorithm is compared with the path pair of the conventional a-algorithm, such as shown in fig. 5. The left graph is the path of the traditional A-algorithm, the right graph is the path of the safe A-algorithm, and the safe A-algorithm can be seen to always keep a certain distance from an obstacle after being moved into a risk function in a cost function, so that the safety and the reliability are improved when the path is planned in comparison with the traditional A-algorithm.
S5, sequentially selecting a starting point, an inflection point and an end point from the global path as key points, and integrating sub-target points when planning the local path (the first integrating point is the starting point S and the last integrating point is the target point G);
s6 (LPP), local path planning is performed using an improved adaptive window algorithm.
As shown in fig. 3, the improved adaptive window algorithm path planning procedure is as follows:
LPP (1), the robot obtains local environment information by the laser radar, determines the laser radar range d, equally divides the radar range into n parts, as shown in fig. 6.
And the LPP (2) is used for adaptively adjusting the range of the planning window according to the environmental information detected by the laser radar. When the feasible area in the planning window is larger, the range of the planning window is enlarged; and when the feasible area is smaller, the planning window range is narrowed. Determining window range r win Angular resolution delta Φ Orientation θ of robot R Etc.
LPP (3), the sub-target point of the current search is G t A dot; searching is carried out according to the sequence of the sub-target point sets.
The LPP (4) judges whether the final target point G is searched; if yes, the loop is ended, and the step (10) is skipped; if not, go to step LPP (5).
LPP (5) for judging whether G exists in the planning window range t Sub-target point G after point t+x Or have G t Point and G t+1 Point R on the point connection: if so, proceeding to step LPP (6); if not, jump to LPP (7).
LPP (6) changing the sub-target point of the current search to G according to the situation t+x And (3) performing LPP (7) on the point or the R point, and continuously searching for the changed sub-target point.
LPP (7) determines the feasible region within the planning window according to the modified adaptive window algorithm. According to radius r of robot rob The edge of the obstacle is expanded, and the obstacle and its expanded portion are considered to be an infeasible area. Namely: and a line segment perpendicular to the tangent line is made at the tangent point of the robot and the obstacle, the length is the radius of the robot, a right triangle is formed, and the tangent point at the other side is operated in the same way. At the same time, a repulsive force potential field in an artificial potential field method is introduced, and a repulsive force value F req Larger directions are also considered to be infeasible areas. Other portions of the planning window are viable areas, except for non-viable areas.
LPP (8), judging whether a feasible region exists in the planning window range: if so, performing LPP (9); if not, judging whether to enter the U-shaped trap or not, and jumping to the LPP (11).
LPP (9), after determining the feasible region, combines the nearest sub-target point G in the feasible region t The direction of the robot is determined at the next moment, and the robot jumps to the LPP (1) for circulation.
The LPP (10) ends the local path planning cycle and jumps to step S7.
LPP (11) judges whether the robot is trapped in the U-shaped trap, and executes the U-shaped trap escape logic.
As shown in fig. 4, the U-trap escape logic is specifically as follows:
the current position of the robot is taken as the origin of coordinates, the current direction is taken as the positive direction, a rectangular coordinate system is established, and a sub-target point G searched at present is used t Carrying out local path planning with the position relation of the robot;
when the sub-target point G t The robot is positioned in the first quadrant, falls into the U-shaped trap, and is enabled to turn right to a certain angle until a feasible region exists in the planning window;
when the sub-target point G t The robot is positioned in the second quadrant, falls into the U-shaped trap, and turns left by a certain angle until a feasible region exists in the planning window;
when the sub-target point G t In the third and fourth quadrants, the robot does not sink into the U-shaped trap, and the robot autonomously searches for the sub-target point G according to the logic of the improved adaptive window algorithm t 。
And S7, when the robot searches the final target point G, completing local path planning and completing mixed path planning from the starting point S to the target point G.
In summary, the robot completes the whole hybrid path planning process from global to local. The two parts are mutually independent and mutually auxiliary, so that the processes of obstacle avoidance, road finding and tracking of the robot in a complex dynamic environment are realized together, and the safety and reliability of the robot in the motion process are improved.
The foregoing embodiments have described in detail the technical solution and the advantages of the present invention, it should be understood that the foregoing embodiments are merely illustrative of the present invention and are not intended to limit the invention, and any modifications, additions and equivalents made within the scope of the principles of the present invention should be included in the scope of the invention.

Claims (8)

1. A hybrid path planning method based on an improved adaptive window algorithm, comprising the steps of:
(1) Acquiring an environment-initiated grid map by an immediate localization and mapping method SLAM;
(2) Determining a starting point S and a target point G of the robot in the grid map;
(3) Performing global path planning by using an improved A star algorithm; the improved A star algorithm introduces a risk function into a cost function, estimates a risk value of grid nodes in the path searching process, and selects nodes with low cost value for expansion;
(4) Finding a target point G through an improved A star algorithm to obtain a global Path;
(5) Sequentially selecting a starting point, an inflection point and an end point from the global Path as key points, and taking the starting point, the inflection point and the end point as a sub-target point set in local Path planning; wherein, the starting point in the first sub-target point set is S, and the end point in the last sub-target point set is G;
(6) Performing local path planning by using an improved adaptive window algorithm; the improved self-adaptive window algorithm combines the self-adaptive window algorithm with an artificial potential field method, and designs U-shaped trap escape logic;
(7) When the robot searches the final target point G, the local path planning is completed, and the hybrid path planning from the starting point S to the target point G is completed.
2. The hybrid path planning method based on the improved adaptive window algorithm according to claim 1, wherein in the step (3), the specific process of global path planning using the improved a star algorithm is as follows:
(3-1) establishing an open table and a close table which are respectively used for storing nodes to be expanded and expanded nodes;
(3-2) calculating a cost function value of the starting point S, and putting S into the open table;
(3-3) comparing the cost function values of each point in the open table, if the open table is empty, the algorithm fails; if the open table is not empty, selecting a node P with the minimum cost function value, and moving the node P out of the open table to a close table;
(3-4) judging whether the point P is the target point G: if yes, carrying out the step (3-7); if not, carrying out the step (3-5);
(3-5) respectively expanding and processing the adjacent nodes of different types P according to the logic of the expanded adjacent nodes;
(3-6) adding or updating information to the nodes in the open table according to the situation in the step (3-5), and jumping to the step (3-3);
and (3-7) finding a target point, reversely pushing back to the starting point according to the father node, finding a path, completing global path planning, and ending the circulation.
3. The hybrid path planning method based on the improved adaptive window algorithm according to claim 2, wherein in step (3-2), a cost function value of the starting point S is calculated by constructing a cost function, wherein the cost function is as follows:
F(n)＝G(n)+H(n)+R(n)
wherein G (n) is the actual distance from the starting point S to the current point n, H (n) is the estimated distance from the current point n to the target point G, and R (n) is the risk value of the current point n;
where α is a risk factor greater than zero and r is the distance of the current point n from the nearest obstacle.
4. The hybrid path planning method based on the improved adaptive window algorithm as claimed in claim 1, wherein in the step (6), the specific process of using the improved adaptive window algorithm for local path planning is:
(6-1) the robot obtains local environment information through a laser radar, determines a laser radar range d, and equally divides the radar range into n parts;
(6-2) adaptively adjusting a planning window range according to the environmental information detected by the laser radar;
(6-3) the sub-target Point of the current search is G t The points are searched according to the sub-target point set sequence;
(6-4) judging whether the final target point G is searched; if yes, ending the cycle, and jumping to the step (10); if not, carrying out the step (6-5);
(6-5) determining whether G exists in the planning window t Sub-target point G after point t+x Or have G t Point and G t+1 And (3) if the point R on the point connecting line exists, performing the step (6-6); if not, jumping to (6-7);
(6-6) changing the sub-target point of the current search to G t+x Performing the step (6-7) on the point or the R point, and continuously searching the changed sub-target point;
(6-7) introducing a repulsive force potential field in an artificial potential field method, expanding obstacles in the range of a planning window, and determining a feasible region in the range of the planning window;
(6-8) judging whether a feasible region exists in the range of the planning window: if yes, carrying out (6-9); if not, jumping to (6-11);
(6-9) after determining the feasible region, combining the closest sub-target point G within the feasible region t Determining the advancing direction of the robot at the next moment, and jumping to (6-1) for circulation;
(6-10) ending the local path planning cycle, and jumping to the step (7);
(6-11) determining whether the robot is trapped in the U-trap and executing the U-trap escape logic.
5. The hybrid path planning method based on an improved adaptive window algorithm of claim 4, wherein in step (6-2), when the feasible region within the planning window is large, the planning window range is enlarged; when the feasible area is smaller, the planning window range is reduced; determining window range r win Angular resolution delta Φ Robot orientation G R 。
6. The hybrid path planning method according to claim 4, wherein the step (6-7) is a specific process comprising:
according to radius r of robot rob Expanding the edge of the obstacle, and considering the obstacle and the expansion part thereof as an infeasible area; at the same time, the repulsive force potential field in the artificial potential field method is introduced for repulsionForce value F req Directions exceeding a preset value are also considered as infeasible areas; other portions of the planning window are viable areas, except for non-viable areas.
7. The hybrid path planning method according to claim 4, wherein in step (6-11), the U-shaped trap escape logic is specifically as follows:
the current position of the robot is taken as the origin of coordinates, the current direction is taken as the positive direction, a rectangular coordinate system is established, and a sub-target point G searched at present is used t Carrying out local path planning with the position relation of the robot;
when the sub-target point G t The robot is positioned in the first quadrant, falls into the U-shaped trap, and is enabled to turn right to a certain angle until a feasible region exists in the planning window;
when the sub-target point G t The robot is positioned in the second quadrant, falls into the U-shaped trap, and turns left by a certain angle until a feasible region exists in the planning window;
when the sub-target point G t In the third and fourth quadrants, the robot does not sink into the U-shaped trap, and the robot autonomously searches for the sub-target point G according to the logic of the improved adaptive window algorithm t 。
8. A hybrid path planning apparatus based on an improved adaptive window algorithm, comprising a memory and one or more processors, the memory having executable code stored therein, the one or more processors, when executing the executable code, being configured to implement the hybrid path planning method of any of claims 1-7.
CN202310824466.2A 2023-07-06 2023-07-06 Hybrid path planning method and device based on improved adaptive window algorithm Pending CN116878527A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117308965A (en) * 2023-11-28 2023-12-29 华南农业大学 Harvester robot autonomous grain unloading path planning method based on sliding window algorithm

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117308965A (en) * 2023-11-28 2023-12-29 华南农业大学 Harvester robot autonomous grain unloading path planning method based on sliding window algorithm
CN117308965B (en) * 2023-11-28 2024-03-12 华南农业大学 Harvester robot autonomous grain unloading path planning method based on sliding window algorithm

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