Static path planning method and system for multiple unmanned vehicles

Abstract

The invention discloses a multi-unmanned vehicle static path planning method and system, and the method comprises the steps: building a 2D grid map based on the basic information of a to-be-planned region; improving an A * algorithm by using the basic information of the unmanned vehicle, and constructing a path planning model; and completing path planning in the 2D grid map by using the path planning model. According to the method, the adaptive grid map is generated according to the planning area, the method is suitable for multi-unmanned vehicle path planning in any size scene, the map memory occupation is reduced, and the algorithm operation efficiency is improved; time parameters are introduced in the planning process, simultaneous path planning of unmanned vehicles of different models and specifications is supported, and path planning of multiple unmanned vehicles in the same area at any time is achieved; the problem of excessive redundant points in the path is solved by utilizing a strategy for judging whether the line segments are crossed or not, and the global path is optimized.

Description

Technical Field

[0001] The present invention relates to the field of path planning for unmanned vehicles, and particularly to a method and system for static path planning of multiple unmanned vehicles. Background Art

[0002] At present, unmanned vehicles play a huge role in more and more fields such as people's daily production and scientific research. As one of the core fields of unmanned vehicle research, the path planning of unmanned vehicles has also attracted much attention. As a global path planning algorithm, the A* algorithm is widely used in path planning due to its stability and excellence. However, due to the principle of searching paths of the traditional A* algorithm, the generated paths have many redundant points, which affects the running efficiency of unmanned vehicles. Moreover, the A* algorithm has strong limitations and can only search for paths in an absolutely static environment. However, when multiple unmanned vehicles are driving in the same area, only local path planning algorithms combined with positioning and recognition modules can be used to complete real-time obstacle avoidance, or the driving paths can be formulated manually. However, when the number of unmanned vehicles is large and their running times are different, and there are many repeated path segments, it is more complicated to formulate the operation of each unmanned vehicle manually, and it will also reduce the working efficiency of unmanned vehicles. Summary of the Invention

[0003] To solve the above deficiencies of the traditional A* algorithm and the requirements of practical applications, the purpose of the present invention is to provide a method for path planning of multiple unmanned vehicles based on an improved A* algorithm. This algorithm introduces a time parameter in the path planning process to achieve path planning for multiple unmanned vehicles in the same area at any time; and uses a strategy for judging whether line segments cross to solve the problem of redundant path points in the planned paths, so as to optimize the global path.

[0004] To achieve the above purpose, the present invention provides a method for static path planning of multiple unmanned vehicles, and the steps include:

[0005] Construct a 2D grid map based on the basic information of the area to be planned;

[0006] Improve the A* algorithm using the basic information of the unmanned vehicle to construct a path planning model;

[0007] Use the path planning model to complete path planning in the 2D grid map.

[0008] Preferably, the basic information includes: prior map information and obstacle information; the method for constructing the 2D grid map includes: rasterizing the prior map of the area to be planned; deleting small obstacles that have no influence on the driving of the unmanned vehicle, screening obstacles that the unmanned vehicle cannot cross, and selecting the smallest obstacle size as the grid map size, and the height information in the 2D grid map corresponds to the coordinate points.

[0009] Preferably, the basic information of the driverless vehicle includes: volume characteristics, physical conditions, and time information; the method for constructing the path planning model includes:

[0010] In the first step, update the volume characteristic information of the first driverless vehicle into the A* algorithm according to the priority order of the driverless vehicles;

[0011] In the second step, after the path planning of the first driverless vehicle is completed, calculate the absolute time for this driverless vehicle to reach each path point according to the physical conditions of the first driverless vehicle;

[0012] In the third step, according to the sorting of the driverless vehicles, update the volume characteristic information of the second driverless vehicle into the A* algorithm. When planning the path, determine whether the newly planned path segment conflicts with the path of the first driverless vehicle and whether the arrival times of the two driverless vehicles at the conflict point are within the threshold range. If so, update this path segment of the first driverless vehicle as an obstacle into the grid map and re-plan the path segment of the second driverless vehicle. If not, add the newly planned path segment to the global path of the second driverless vehicle;

[0013] In the fourth step, after the path planning of the second driverless vehicle is completed, restore the grid map to its initial state;

[0014] In the fifth step, loop through the third step to the fourth step until the paths of all the driverless vehicles are planned.

[0015] Preferably, during the path planning process, the following steps are included:

[0016] In the first step, connect the first path point and the third path point as a new path segment according to the planned path;

[0017] In the second step, use the line segment intersection strategy to determine whether there are time and space conflicts between the new path segment and the obstacles and other driverless vehicle path segments. If so, retain the second path point, connect the second path point and the fourth path point as a new path segment, and continue to judge until the last path point is judged; if not, remove the second path point from the global path, organize the new global path, connect the first path point and the third path point as a new path segment, and continue to judge until the last path point is judged.

[0018] Preferably, the strategy for judging whether line segments intersect specifically includes calculating the shortest distance between two line segments and judging whether this distance meets the requirements of the safe distance of the driverless vehicle.

[0019] The present invention also provides a multi-driverless vehicle static path planning system, which is used for the above method and includes: a map construction module, a model construction module, and a path planning module;

[0020] The map construction module constructs a 2D grid map based on the basic information of the area to be planned;

[0021] The model construction module improves the A* algorithm using the basic information of the unmanned vehicle and constructs a path planning model;

[0022] The path planning module uses the path planning model to complete path planning in the 2D grid map.

[0023] Preferably, the basic information includes: prior map information and obstacle information; the workflow of the map construction module includes: rasterizing the prior map of the area to be planned; deleting small obstacles that have no impact on the driving of the unmanned vehicle, screening obstacles that the unmanned vehicle cannot cross, and selecting the smallest obstacle size as the grid map size, and the height information in the 2D grid map corresponds to the coordinate points.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] The present invention generates an adaptive grid map according to the planned area, which is suitable for multi-unmanned vehicle path planning in scenes of any size, reduces the memory occupancy of the map, and improves the algorithm operation efficiency; a time parameter is introduced in the planning process to support the simultaneous path planning of unmanned vehicles of different models and specifications, and realizes the path planning of multi-unmanned vehicles in the same area at any time; a strategy for judging whether line segments cross is used to solve the problem of too many redundant points in the path and realize the optimization of the global path. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0027] Figure 1 It is a schematic flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0030] Example 1

[0031] As Figure 1 shown, it is a schematic diagram of the method flow of this embodiment. The steps include:

[0032] S1. Based on the basic information of the area to be planned, construct a 2D grid map.

[0033] According to the prior map information and obstacle information of the planning area, construct a 2D grid map with height information, and the grid size in the grid map is determined by the obstacle size within the area, which can save the memory occupation of the map and improve the algorithm operation efficiency.

[0034] Among them, the grid map size is determined by the obstacle size within the area, specifically including screening obstacles that the unmanned vehicle cannot cross, and selecting the smallest obstacle size as the grid map size to reduce the memory occupied by the grid map and reduce the generation of redundant path points.

[0035] S2. Improve the A* algorithm using the basic information of the unmanned vehicle to construct a path planning model.

[0036] In this embodiment, the basic information includes volume characteristics, physical conditions, and time information. Adding this information to the A* algorithm to improve its performance and construct a path planning model to ensure the safety of the paths during the path planning of multiple unmanned vehicles. It includes the following steps:

[0037] The first step: Sort the unmanned vehicles, and update the volume characteristics of the first unmanned vehicle to the safe distance of the A* algorithm to avoid planning a path that is too narrow for the unmanned vehicle to drive normally;

[0038] The second step: After the path planning of the first unmanned vehicle is completed, calculate the time to reach each path point in the planned path according to the physical conditions of the first unmanned vehicle, that is, speed and acceleration, etc.;

[0039] The third step: Sort the unmanned vehicles, and update the volume characteristics of the second unmanned vehicle to the safe distance of the A* algorithm. When planning the path, judge whether the newly planned path segment conflicts with the path of the first unmanned vehicle and the arrival times of the two unmanned vehicles at the conflict point are the same. If so, update this path segment of the first unmanned vehicle as an obstacle to the grid map and re-plan this path segment of the second unmanned vehicle. If not, add this path segment to the global path of the second unmanned vehicle;

[0040] The fourth step: After the path planning of the second unmanned vehicle is completed, restore the grid map to its initial state;

[0041] The fifth step: Loop the third step and the fourth step until the path of the last unmanned vehicle is planned.

[0042] S3. Use the path planning model to complete path planning in the 2D grid map.

[0043] During the path planning process, use a strategy for judging whether line segments cross to solve the problem of redundant path points in the planned path. The steps are as follows:

[0044] The first step: According to the planned path, connect the first path point and the third path point as a new path segment;

[0045] The second step: Use the line segment crossing judgment strategy to determine whether there are time and space conflicts between the new path segment and obstacles and other unmanned vehicle path segments. If so, retain the second path point, connect the second path point and the fourth path point as a new path segment, and continue to judge until the last path point is judged. If not, remove the second path point from the global path, organize the new global path, connect the first path point and the third path point as a new path segment, and continue to judge until the last path point is judged.

[0046] The above strategy for judging whether line segments cross specifically includes calculating the shortest distance between two line segments and judging whether this distance meets the requirements of the unmanned vehicle safety distance.

[0047] Embodiment 2

[0048] This embodiment also provides a multi-unmanned vehicle static path planning system, including: a map construction module, a model construction module, and a path planning module; the map construction module constructs a 2D grid map based on the basic information of the area to be planned; the model construction module improves the A* algorithm using the basic information of the unmanned vehicle and constructs a path planning model; the path planning module uses the path planning model to complete path planning in the 2D grid map.

[0049] Next, in combination with this embodiment, it will be detailed how the present invention solves the technical problems in actual work.

[0050] Use the map construction module to construct a 2D grid map based on the basic information of the area to be planned.

[0051] According to the prior map information and obstacle information of the planned area, construct a 2D grid map with height information, and the grid size in the grid map is determined by the obstacle size in the area, which can save the memory occupied by the map and improve the algorithm operation efficiency.

[0052] Among them, the grid map size is determined by the obstacle size in the area, specifically including screening obstacles that the unmanned vehicle cannot cross, selecting the smallest obstacle size as the grid map size to reduce the memory occupied by the grid map and reduce the generation of redundant path points

[0053] After that, the model construction module improves the A* algorithm using the basic information of the driverless vehicle to construct a path planning model.

[0054] In this embodiment, the basic information includes volume characteristics, physical conditions, and time information. These information are added to the A* algorithm to improve its performance and construct a path planning model, ensuring the safety of the paths during the path planning of multiple driverless vehicles. The process includes the following steps:

[0055] The first step: According to the sorting of the driverless vehicles, update the volume characteristics of the first driverless vehicle to the safe distance of the A* algorithm to avoid planning a path that is too narrow for the driverless vehicle to drive normally;

[0056] The second step: After the path planning of the first driverless vehicle is completed, calculate the time to reach each path point in the planned path according to the physical conditions of the first driverless vehicle, that is, speed, acceleration, etc.;

[0057] The third step: According to the sorting of the driverless vehicles, update the volume characteristics of the second driverless vehicle to the safe distance of the A* algorithm. When planning the path, judge whether the newly planned path segment conflicts with the path of the first driverless vehicle and the arrival times of the two driverless vehicles at the conflict point are the same. If so, update this path segment of the first driverless vehicle as an obstacle to the grid map and re-plan this path segment of the second driverless vehicle. If not, add this path segment to the global path of the second driverless vehicle;

[0058] The fourth step: After the path planning of the second driverless vehicle is completed, restore the grid map to the initial state;

[0059] The fifth step: Loop the third step and the fourth step until the path of the last driverless vehicle is planned.

[0060] After that, the path planning module uses the path planning model to complete the path planning in the 2D grid map.

[0061] During the path planning process, a strategy for judging whether line segments cross is used to solve the problem of redundant path points in the planned path. The process includes:

[0062] The first step: According to the planned path, connect the first path point and the third path point as a new path segment;

[0063] The second step: Use the strategy for judging whether line segments cross to judge whether there are time and space conflicts between the new path segment and obstacles and other driverless vehicle path segments. If so, retain the second path point, connect the second path point and the fourth path point as a new path segment, and continue to judge until the last path point is judged. If not, remove the second path point from the global path, organize the new global path, connect the first path point and the third path point as a new path segment, and continue to judge until the last path point is judged.

[0064] The above strategy for determining whether line segments cross specifically includes calculating the shortest distance between two line segments and determining whether this distance meets the requirements of the safety distance for the driverless vehicle.

[0065] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for static path planning of multiple unmanned vehicles, characterized in that the steps include: Construct a 2D grid map based on the basic information of the area to be planned; Use the basic information of the unmanned vehicle to improve the A* algorithm and build a path planning model; Utilizing the path planning model, path planning is completed in the 2D grid map.

2. The static path planning method for multiple unmanned vehicles according to claim 1 is characterized in that: The basic information includes: prior map information and obstacle information; the method for constructing the 2D grid map includes: rasterizing the prior map of the area to be planned; deleting small obstacles that have no effect on the driving of the unmanned vehicle, screening obstacles that the unmanned vehicle cannot cross, and selecting the smallest obstacle size as the grid map size, and the height information in the 2D grid map corresponds to the coordinate point.

3. The static path planning method for multiple unmanned vehicles according to claim 1 is characterized in that: The basic information of the unmanned vehicle includes: volume characteristics, physical conditions and time information; the method for constructing the path planning model includes: The first step is to update the volume characteristic information of the first unmanned vehicle into the A* algorithm according to the priority order of the unmanned vehicles; The second step is to calculate the absolute time it takes for the first unmanned vehicle to reach each path point after the path planning of the first unmanned vehicle is completed according to the physical conditions of the first unmanned vehicle. The third step is to update the volume characteristic information of the second unmanned vehicle into the A* algorithm according to the order of the unmanned vehicles. When planning the path, determine whether the newly planned path segment conflicts with the path of the first unmanned vehicle and whether the time for the two unmanned vehicles to reach the conflict point is within the threshold range. If so, update this path segment of the first unmanned vehicle as an obstacle to the grid map and replan the path segment of the second unmanned vehicle. If not, add the newly planned path segment to the global path of the second unmanned vehicle. Step 4: The path planning of the second unmanned vehicle is completed, and the grid map is restored to its initial state; Step 5: Repeat steps 3 to 4 until the path of the last unmanned vehicle is planned.

4. The static path planning method for multiple unmanned vehicles according to claim 1 is characterized in that: The path planning process includes the following steps: The first step is to connect the first path point and the third path point into a new path segment according to the planned path; In the second step, the line segment intersection strategy is used to determine whether the new path segment has time and space conflicts with obstacles and other unmanned vehicle path segments. If so, the second path point is retained, and the second path point and the fourth path point are connected as a new path segment, and the judgment is continued until the last path point is judged; if not, the second path point is removed from the global path, a new global path is organized, and the first path point and the third path point are connected as a new path segment, and the judgment is continued until the last path point is judged.

5. The static path planning method for multiple unmanned vehicles according to claim 4 is characterized in that: The strategy for determining whether line segments intersect specifically includes finding the shortest distance between two line segments and determining whether the distance meets the safety distance requirement for the unmanned vehicle.

6. A static path planning system for multiple unmanned vehicles, the system being used to implement the method according to any one of claims 1 to 5, characterized in that: include: Map building module, model building module and path planning module; The map construction module constructs a 2D grid map based on basic information of the area to be planned; The model building module uses the basic information of the unmanned vehicle to improve the A* algorithm and build a path planning model; The path planning module uses the path planning model to complete path planning in the 2D grid map.

7. The static path planning system for multiple unmanned vehicles according to claim 6, characterized in that: The basic information includes: prior map information and obstacle information; the workflow of the map construction module includes: rasterizing the prior map of the area to be planned; deleting small obstacles that have no effect on the driving of the unmanned vehicle, screening obstacles that the unmanned vehicle cannot cross, and selecting the smallest obstacle size as the grid map size, and the height information in the 2D grid map corresponds to the coordinate point.

Images