Obstacle-clearing patrol vehicle system and control method in typhoon scenarios

Abstract

An obstacle-clearing patrol vehicle system for typhoon scenarios includes: an unmanned patrol vehicle group, an emergency obstacle-clearing module system, a cloud server, and a garbage collection system; wherein the unmanned patrol vehicle includes: a power system, a camera module, a laser radar module, a central processing module, an inertial navigation module, a wireless communication module, an identification module, and a positioning module; the garbage collection system includes an urban safety warning system and an urban garbage collection system. Through the cloud-based intelligent zoning of the disaster-stricken area under the typhoon environment, the unmanned patrol vehicle patrols the disaster-stricken area one by one and uploads the garbage and obstacle detection and positioning data. After the cloud-based data is identified, the unmanned patrol vehicle group is dispatched to clear obstacles of different sizes, thereby improving the utilization efficiency of the unmanned patrol vehicle group, saving resources, reducing labor costs, improving the road accessibility of rescue personnel after the typhoon, and reducing the difficulty of rescue work.

Description

Technical Field

[0001] The present invention belongs to the technical field of engineering vehicles, and in particular relates to an obstacle-clearing patrol vehicle system and a control method in a typhoon scenario. Background Art

[0002] Typhoons bring abundant rainfall to vast areas, becoming a rainfall system closely related to human life and production. However, typhoons also always bring various destructions. Their destructive power is mainly caused by three factors: strong winds, heavy rains and storm surges. They are characterized by strong suddenness and great destructive power. They are one of the most serious natural disasters in the world. At present, typhoon warnings in large coastal cities are mainly carried out in the early stage of the typhoon. Patrol cars automatically patrol the areas under their jurisdiction, and issue online warnings for phenomena such as road waterlogging. Security personnel in the jurisdiction then manually clean up the drainage blocked areas.

[0003] In the existing technical solution, when a typhoon approaches, patrols are carried out in the planned jurisdiction by manned patrol cars. When garbage and tree branches of different sizes and types are found in the road area of ​​the jurisdiction, the data is uploaded to the cloud and connected to the city's early warning system. The early warning system assigns tasks to on-duty personnel to manually check and solve the problem. Summary of the invention

[0004] The technical problem to be solved by the present invention is that in view of the shortcomings of the above-mentioned prior art, the present invention provides an obstacle-clearing patrol car system in a typhoon scene, characterized in that it includes a cloud server and an unmanned patrol car group, wherein the unmanned patrol car group includes unmanned patrol car 1, unmanned patrol car 2, unmanned patrol car 3, and unmanned patrol car 4, and the unmanned patrol car 1, unmanned patrol car 2, unmanned patrol car 3, and unmanned patrol car 4 are all provided with a camera, a laser radar module, a central processing module, an inertial navigation module, a wireless communication module, an identification module, and a positioning module. A plate-shaped counterweight block is connected to the side of the chassis, and the laser radar module, the inertial navigation module, and the identification module are connected to the central processing module through a CAN network communication, and the central processing module and the wireless communication module are electrically connected. The wireless communication module receives data and instructions transmitted without service from the cloud, and the central processing module sends the path planning data to the inertial navigation module through the CAN network. After the inertial navigation module identifies the path planned by the cloud, it issues a driving instruction through the central processing unit and drives according to the planned route. During the route, the laser radar module will detect obstacles and avoid pedestrians, etc. The identification module identifies garbage and obstacles according to the annotated picture data sent by the central processing unit;

[0005] The unmanned patrol vehicle 1 is responsible for detecting and identifying garbage and obstacles in the disaster area, and sending photos and locations of the garbage and obstacles and requests for breaking and clearing the garbage and obstacles to the server;

[0006] The front part of the chassis of the unmanned patrol vehicle 2 is connected with a bucket 2, which is responsible for clearing obstacles and clearing them to the designated garbage and obstacle stacking point;

[0007] The front part of the chassis of the unmanned patrol vehicle 3 is connected to a bucket 1, a grabbing mechanical arm is provided on the left side of the front end of the chassis top, a mechanical claw is provided at the end of the grabbing mechanical arm, a cutting mechanical arm is provided on the right side of the front of the chassis, a circular saw cutting machine is provided at the end of the cutting mechanical arm, and the unmanned patrol vehicle 3 is responsible for cutting obstacles;

[0008] A bucket is connected to the front part of the chassis of the unmanned patrol vehicle 4, and support arms extending to the front of the bucket are fixed on the left and right sides of the front end of the top of the chassis. A movable boot is rotatably connected to the bottom of the support arm, and the movable boot is driven by a motor fixed on the support arm to achieve pitch position changes in the direction of vehicle travel. The unmanned patrol vehicle 4 is responsible for heavy obstacle clearing operations and clearing obstacles to designated garbage and obstacle stacking points.

[0009] Preferably, the unmanned patrol car 2, the unmanned patrol car 3, and the unmanned patrol car 4 are all equipped with a volume sensing module, the volume sensor includes a light sensor and a controller, and the volume modules are respectively installed on the upper ends of the left and right sides of the bucket. When the light sensors on the left and right sides are blocked by garbage or obstacles at the same time, and the blocking time on both sides exceeds the set threshold, the controller of the volume sensing module will convert the light sensing signal into an electrical signal and send it to the central control module of the patrol car via wireless transmission.

[0010] The present invention also provides a path planning control method for an obstacle-clearing patrol vehicle system in a typhoon scenario, comprising:

[0011] Step 1: In the city grid map, the free grid represents the roads that patrol cars can drive to, and the filled grid represents the key areas. Priority is set for each AGV. During the planning process, the first, second, third, and fourth priorities of unmanned patrol cars decrease in sequence. When a low-priority unmanned patrol car encounters a high-priority unmanned patrol car, the former should give way to the latter;

[0012] Step 2: Construct the open list and closed list of the A-star algorithm, and initialize the parameters of the A-star algorithm and the ant colony algorithm.

[0013] Step 3: Use the K-means clustering algorithm based on the Canopy algorithm to partition the map, and use the A-star algorithm on the raster map to find the starting and ending points of the path crossing the boundary of each partition.

[0014] Step 4: If the disaster-stricken area is divided, find the starting and ending points of the area as the local starting and ending points, and use the ant colony algorithm to find the local optimal path.

[0015] Step 5: If there is no feasible area in the key area, the straight line path of the A-star algorithm path in step 3 is used as the local short path.

[0016] Step 6: Find all abstract paths from the starting point to the disaster-stricken area, refine them to form a complete path output, and command multiple convoys to rush to the disaster-stricken areas respectively.

[0017] Preferably, in step 3, it also includes:

[0018] Step 31: Use Canopy clustering to group all raster data and obtain K Canopy sets and the center of each Canopy set.

[0019] Step 32: Initialize the number of Canopy sets K as the number of partitions to be constructed by the K-means clustering algorithm; use the center of each Canopy set as the K cluster center of the partition; based on the above-determined number of clusters K and cluster centers, use the K-means clustering algorithm to perform clustering optimization processing on all original data, and output the clustering optimization results.

[0020] Step 33: Based on the clustering partition results, obtain the regional information of each disaster-stricken area and feasible area, and then divide the map into rectangular partitions of different sizes.

[0021] Calculate the α value of each partition. If α=0, the partition has no key area and no further subdivision is performed. Set the path planning parameter λ=1 and a straight line path can be used.

[0022] If there are fewer key areas in the 0<α<β<1 partition, further subdivision is required.

[0023] If there are many key areas in the partition 0<β≤α<1, the division can be stopped, the path planning parameter λ=0 can be set, and a more complex path search algorithm can be used in this partition;

[0024] Among them, α = key area / number of partition nodes, the partition threshold β is the boundary value of whether the partition needs to be further divided. The larger the β value, the finer the partition division and the longer the time consumed; the smaller the β value, the coarser the partition division and the shorter the time consumed.

[0025] The beneficial effects brought by the present invention are:

[0026] 1. The present invention realizes the tasks of cutting, breaking, clearing and recycling garbage and obstacles in a typhoon environment, eliminating the labor cost and risk of personal injury of personnel cleaning up garbage and obstacles in a typhoon environment.

[0027] 2. The present invention realizes the multifunctionality of the unmanned patrol vehicle, the universality and interchangeability of parts and components through the standardized and modular design of the power interface, controller, camera, patrol equipment, mechanical arm, bucket, anti-front rollover mechanism and counterweight equipment, thus ensuring that the system can be scaled up and industrialized.

[0028] 3. The present invention uses the cloud to intelligently divide the disaster-stricken areas under the typhoon environment into zones. Unmanned patrol vehicles patrol the disaster-stricken areas one by one and upload garbage and obstacle detection and positioning data. After cloud data recognition, the unmanned patrol vehicle group is dispatched to cut, clear and pile up garbage and obstacles of different sizes and types, thereby improving the utilization efficiency of the unmanned patrol vehicle group, saving resources, reducing labor costs, improving the road accessibility of rescue personnel after the typhoon, and reducing the difficulty of rescue work. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a structural diagram of the unmanned patrol vehicle of the present invention.

[0030] Figure 2 This is a structural diagram of the unmanned patrol vehicle 2 of the present invention.

[0031] Figure 3 It is a structural diagram of the unmanned patrol vehicle of the present invention.

[0032] Figure 4 It is an isometric view of the four structures of the unmanned patrol vehicle of the present invention.

[0033] Figure 5 Four side views of the unmanned patrol vehicle of the present invention.

[0034] Figure 6 The present invention is briefly described in the following working flow chart.

[0035] Figure 7 Detailed working flow chart of the present invention.

[0036] Figure 8 This is a flow chart of the principle of multi-patrol car path planning of the present invention.

[0037] In the figure: 1-central processing module, 2-positioning module, 3-wireless communication module, 4-camera module, 5-counterweight module, 6-patrol module, 7-lidar module, 8-inertial navigation module, 9-identification module, 10-volume sensing module, 11-bucket 2, 12-bucket 1, 13-grasping robot arm module, 14-cutting robot arm module, 15-support arm, 16-movable boot. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The present embodiment provides an obstacle-clearing patrol vehicle system in a typhoon scenario, including: a cloud server, an unmanned patrol vehicle group, wherein the unmanned patrol vehicle group includes unmanned patrol vehicle 1, unmanned patrol vehicle 2, unmanned patrol vehicle 3, and unmanned patrol vehicle 4, and the unmanned patrol vehicle 1, unmanned patrol vehicle 2, unmanned patrol vehicle 3, and unmanned patrol vehicle 4 are all equipped with a camera, a laser radar module 7, a central processing module 1, an inertial navigation module 8, a wireless communication module 3, an identification module 9, and a positioning module 2; a plate-shaped counterweight block is connected to the side of the chassis, and the laser radar module 7, the inertial navigation module 8, and the identification module 9 The central processing module 1 is connected to the wireless communication module 3 through the CAN network communication. The wireless communication module 3 receives the data and instructions transmitted by the cloud without service, and the central processing module 1 sends the path planning data to the inertial navigation module 8 through the CAN network. After the inertial navigation module 8 identifies the path planned by the cloud, it issues a driving instruction through the central processor and drives according to the planned route. During the route, the laser radar module 7 will detect obstacles and avoid pedestrians, etc. The recognition module 9 identifies garbage and obstacles according to the annotated image data sent by the central processor;

[0040] The unmanned patrol vehicle 1 is responsible for detecting and identifying garbage and obstacles in the disaster area, and sending photos and locations of the garbage and obstacles and requests for breaking and clearing the garbage and obstacles to the server;

[0041] The front part of the chassis of the unmanned patrol vehicle 2 is connected with a bucket 2, which is responsible for clearing obstacles and clearing them to the designated garbage and obstacle stacking point;

[0042] The front part of the chassis of the unmanned patrol vehicle 3 is connected to a bucket 1, a grabbing mechanical arm is provided on the left side of the front end of the chassis top, a mechanical claw is provided at the end of the grabbing mechanical arm, a cutting mechanical arm is provided on the right side of the front of the chassis, a circular saw cutting machine is provided at the end of the cutting mechanical arm, and the unmanned patrol vehicle 3 is responsible for cutting obstacles;

[0043] The front part of the chassis of the unmanned patrol vehicle is connected with a bucket 1, and the left and right sides of the front end of the top of the chassis are fixed with support arms 15 extending to the front of the bucket. The bottom of the support arm 15 is rotatably connected with a movable boot 16, and the movable boot 16 is driven by a motor fixed on the support arm 15 and changes its pitch position in the direction of vehicle travel. The driving mode of the movable boot 16: it can be driven by the motor on the side of the support arm 15 through a gearbox, or it can be driven by the motor set on the top of the support arm 15 after deceleration and direction change.

[0044] The movable boot 16 cooperates with the support arm 15 to not only prevent the vehicle from overturning, but also make real-time adjustments to the vehicle's rotation state. Unmanned patrol vehicle 4 is responsible for heavy obstacle removal operations and clearing obstacles to designated garbage and obstacle stacking points;

[0045] The unmanned patrol car 2, unmanned patrol car 3, and unmanned patrol car 4 are all equipped with a volume sensing module 10, which will monitor the volume of accumulated garbage and obstacles in the process of breaking and clearing obstacles. The volume sensor includes a light sensor and a controller. The volume modules are respectively installed on the upper ends of the left and right sides of bucket 1 and bucket 2. When the light sensors on the left and right sides are blocked by garbage or obstacles at the same time, and the blocking time on both sides exceeds the set threshold, the controller of the volume sensing module 10 will convert the light sensing signal into an electrical signal and send it to the central control module of the patrol car via wireless transmission.

[0046] like Figure 8 As shown, the present invention also provides a multi-patrol car path planning principle flow chart, including:

[0047] Step 1: In the city grid map, the free grid represents the roads that patrol cars can drive to, and the filled grid represents the key areas, including disaster area 1, disaster area 2...disaster area N. Set a priority for each AGV. During the planning process, the priorities of unmanned patrol cars 1, 2, 3, and 4 decrease in sequence. When a low-priority unmanned patrol car encounters a high-priority unmanned patrol car, the former should give way to the latter;

[0048] Step 2: Construct the open list and closed list of the A-star algorithm, and initialize the parameters of the A-star algorithm and the ant colony algorithm.

[0049] Step 3: Use the K-means clustering algorithm based on the Canopy algorithm to partition the map, and use the A-star algorithm on the raster map to find the starting and ending points of the path crossing the boundary of each partition.

[0050] Step 31: Use Canopy clustering to group all raster data and obtain K Canopy sets and the center of each Canopy set.

[0051] Step 32: Initialize the number of Canopy sets K as the number of partitions to be constructed by the K-means clustering algorithm; use the center of each Canopy set as the K cluster center of the partition; based on the above-determined number of clusters K and cluster centers, use the K-means clustering algorithm to perform clustering optimization processing on all original data, and output the clustering optimization results.

[0052] Step 33: Based on the clustering partition results, obtain the regional information of each disaster-stricken area and feasible area, and then divide the map into rectangular partitions of different sizes.

[0053] Calculate the α value of each partition. If α=0, the partition has no key area and no further subdivision is performed. Set the path planning parameter λ=1 and a straight line path can be used.

[0054] If there are fewer key areas in the 0<α<β<1 partition, further subdivision is required.

[0055] If there are many key areas in the partition with 0<β≤α<1, the division can be stopped, the path planning parameter λ=0 is set, and a more complex path search algorithm is used in this partition;

[0056] Among them, α = key area / number of partition nodes, the partition threshold β is the boundary value of whether the partition needs to be further divided. The larger the β value, the finer the partition division and the longer the time consumed; the smaller the β value, the coarser the partition division and the shorter the time consumed.

[0057] Step 4: If the disaster-stricken area is divided, find the starting and ending points of the area as the local starting and ending points, and use the ant colony algorithm to find the local optimal path.

[0058] Step 5: If there is no feasible area in the key area, the straight line path of the A-star algorithm path in step 3 is used as the local short path.

[0059] Step 6: Find all abstract paths from the starting point to the disaster-stricken area, refine them to form a complete path output, and command multiple convoys to rush to the disaster-stricken areas respectively.

[0060] Based on the above algorithm, Figure 6 , Figure 7 As shown, the present invention also provides an application working method of an unmanned patrol vehicle group in a typhoon scenario, comprising:

[0061] Step S1, the cloud server divides the key areas for obstacle removal in advance;

[0062] Step S2, the cloud server central processing module 1 performs calculations, the path planning module plans the best driving route, and sends the best driving route to the unmanned patrol vehicle group;

[0063] Step S3, the unmanned patrol vehicle 1 enters the disaster-stricken area 1 where the typhoon is approaching according to the optimal route provided by the cloud server to search for garbage and obstacles;

[0064] Step S4: After the unmanned patrol vehicle detects, identifies and takes photos of the garbage and obstacles in the disaster-stricken area 1, it simultaneously sends the photos and locations of the garbage and obstacles and requests for removing and clearing the garbage and obstacles to the server;

[0065] Step S5, the cloud server receives the photos and location data of the garbage and obstacles sent by the unmanned patrol car 1, the recognition module 9 identifies the size and type of the garbage and obstacles in the photos, and the positioning module 2 determines the specific coordinates of the garbage and obstacles;

[0066] Step S6, the central processing module 1 of the cloud server dispatches patrol cars 2, 3, and 4 in the unmanned patrol car group to issue obstacle removal instructions and optimal routes for different types of garbage and obstacles according to the obstacle removal request issued by the unmanned patrol car 1 and the previous path planning;

[0067] Step S7, the unmanned patrol vehicle 3 performs a cutting operation on the obstacle. When the volume sensing module 10 of the unmanned patrol vehicle 3 sends an alarm signal, that is, when the obstacles cut by the unmanned patrol vehicle 3 fill the bucket 1, the unmanned patrol vehicle 3 sends a request to exit the current working position to the cloud server through its own central processing module 1;

[0068] Step S8, after the cloud server receives the positioning data and exit request sent by the unmanned patrol car 3, it sends the best path to the exit position of the unmanned patrol car 3 to the unmanned patrol car 2 according to the previous path planning. After receiving the instruction, the unmanned patrol car 2 drives to the designated area and starts the obstacle removal operation, clearing the garbage and obstacles to the designated garbage and obstacle stacking point, and uploading the stacking point positioning data to the cloud server. When the volume sensor of the unmanned patrol car 2 sends an alarm signal, it sends photos of the current position and the oversized obstacles and a request to exit the current working position to the cloud server through its own central processing module 1;

[0069] Step S9, after receiving the positioning data, obstacle photos and exit request sent by the unmanned patrol car 2, the cloud server sends the best path to the exit position of the unmanned patrol car 2 to the unmanned patrol car 4 according to the previous path planning. After receiving the instruction, the unmanned patrol car 4 drives to the designated area and starts the obstacle removal operation, clearing the large garbage or obstacles to the designated garbage and obstacle stacking point, and uploading the stacking point positioning data to the cloud server;

[0070] Step S10: After the unmanned patrol car 2 completes the cleaning and transporting tasks of the fragmented garbage and obstacles in the disaster-stricken area 1, and the unmanned patrol car 4 completes the clearing of large obstacles in the disaster-stricken area 1 and uploads the positions of the obstacles after being moved to the cloud server, the cloud server shares the data received from the unmanned patrol car 2 and the unmanned patrol car 4 to the urban safety early warning system, and the early warning system sends the positions of the piled garbage and obstacles to the urban garbage recycling system, which assigns sanitation vehicles to recycle the temporarily piled garbage and obstacles;

[0071] In step S11, after completing the tasks of cutting, breaking and clearing the garbage and obstacles in the disaster-stricken area 1, the unmanned patrol car 2, the unmanned patrol car 3 and the unmanned patrol car 4 return to the service area and stand by according to the previous path planning until the unmanned patrol car group receives the request for breaking and clearing the garbage and obstacles in the disaster-stricken area 2 uploaded by the unmanned patrol car 1 sent by the cloud server again, and repeats steps S5 to S11 again to complete the cutting, breaking, clearing and recycling of the garbage and obstacles.

[0072] After completing the patrol mission of the disaster-stricken area 1 and uploading the garbage or obstacle photos and location data to the cloud server, the unmanned patrol vehicle 1 continues to start the patrol mission of the disaster-stricken area 2 according to the previous path planning, and then the other vehicles in the fleet repeat the above process.

[0073] The serial numbers of the embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments. The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are only illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all belong to the protection of the present invention.

Claims

1. An obstacle-clearing patrol vehicle system in a typhoon scenario, characterized by: It includes a cloud server and multiple unmanned patrol car groups, wherein the unmanned patrol car group includes unmanned patrol car 1, unmanned patrol car 2, unmanned patrol car 3 and unmanned patrol car 4. The unmanned patrol car 1, unmanned patrol car 2, unmanned patrol car 3 and unmanned patrol car 4 are all equipped with cameras, laser radar modules, central processing modules, inertial navigation modules, wireless communication modules and positioning modules. A plate-shaped counterweight is connected to the side of the chassis. The laser radar module, inertial navigation module and identification module are connected to the central processing module through CAN network communication. The central processing module and the wireless communication module are electrically connected. The wireless communication module receives data and instructions transmitted by the cloud without service, and the central processing module sends the path planning data to the inertial navigation module through the CAN network. After the inertial navigation module identifies the path planned by the cloud, it issues a driving instruction through the central processing unit and drives according to the planned route. During the driving process along the route, the laser radar module will detect obstacles and avoid pedestrians. The unmanned patrol car 1 is responsible for the disaster area. The garbage and obstacles in the domain are detected and identified, and photos and locations of the garbage and obstacles as well as requests for removing and clearing the garbage and obstacles are sent to the server; the front part of the chassis of the unmanned patrol vehicle 2 is connected with a bucket, which is responsible for clearing obstacles and clearing them to the designated garbage and obstacle stacking points; the front part of the chassis of the unmanned patrol vehicle 3 is connected with a bucket, a grabbing mechanical arm is provided on the left side of the front end of the top of the chassis, a mechanical claw is provided at the end of the grabbing mechanical arm, a cutting mechanical arm is provided on the right side of the front of the chassis, and a circular saw cutting machine is provided at the end of the cutting mechanical arm. The unmanned patrol vehicle 3 is responsible for cutting obstacles; the front part of the chassis of the unmanned patrol vehicle 4 is connected with a bucket, and support arms extending to the front of the bucket are fixed on the left and right sides of the front end of the top of the chassis, and the bottom of the support arm is rotatably connected with an active boot, which is driven by a motor fixed on the support arm to realize pitch position changes in the driving direction of the vehicle. The unmanned patrol vehicle 4 is responsible for heavy obstacle clearing operations and clearing obstacles to the designated garbage and obstacle stacking points.

2. The obstacle-clearing patrol vehicle system in a typhoon scenario as claimed in claim 1, characterized in that: The unmanned patrol car 2, unmanned patrol car 3, and unmanned patrol car 4 are all equipped with volume sensing modules. The volume sensors include light sensors and controllers. The volume modules are respectively installed on the upper ends of the left and right sides of the bucket. When the light sensors on the left and right sides are blocked by garbage or obstacles at the same time, and the blocking time on both sides exceeds the set threshold, the controller of the volume sensing module will convert the light sensing signal into an electrical signal and send it to the central control module of the patrol car via wireless transmission.

3. A path planning and control method for an obstacle-clearing patrol vehicle system in a typhoon scenario, applied to an obstacle-clearing patrol vehicle system in a typhoon scenario as claimed in any one of claims 1-2, comprising: Step 1: In the city grid map, the free grid indicates the roads that patrol cars can drive to, and the filled grid indicates the key areas. Priority is set for each AGV. During the planning process, the first, second, third, and fourth priorities of unmanned patrol cars decrease in sequence. When an unmanned patrol car with a low priority meets an unmanned patrol car with a high priority, the former should give way to the latter. Step 2: Construct the open list and close list of the A-star algorithm, and initialize the parameters of the A-star algorithm and the ant colony algorithm; Step 3: Use the K-means clustering algorithm based on the Canopy algorithm to partition the map, and use the A-star algorithm on the raster map to find the starting and ending points of the path that crosses the boundary of each partition; Step 4: If the disaster-affected area is divided, find the starting and ending points of the area as the local starting and ending points, and use the ant colony algorithm to find the local optimal path; Step 5: If there is no feasible area in the key area, the straight line path of the A-star algorithm path in step 3 is used as the local short path; Step 6: Find all abstract paths from the starting point to the disaster-stricken area, refine them to form a complete path output, and command multiple convoys to rush to the disaster-stricken areas respectively.

4. The path planning and control method for the obstacle-clearing patrol vehicle system in a typhoon scenario as claimed in claim 3 is characterized in that: In step 3, it also includes: Step 31: Use Canopy clustering to group all raster data and obtain K Canopy sets and the center of each Canopy set; Step 32: Initialize the number of Canopy sets K to the number of partitions to be constructed by the K-means clustering algorithm; use the center of each Canopy set as the K cluster center of the partition; perform clustering optimization processing on all original data using the K-means clustering algorithm based on the above-determined number of clusters K and cluster centers, and output the clustering optimization results; Step 33: According to the clustering partitioning results, the regional information of each disaster-stricken area and feasible area is obtained, and then the map is divided into rectangular partitions of different sizes; Calculate the α value of each partition. If α=0, the partition has no key area and no further subdivision is performed. Set the path planning parameter λ=1 and a straight line path can be used. If there are few key areas in the 0<α<β<1 partition, further subdivision is required; If there are many key areas in the partition 0<β≤α<1, the division can be stopped, the path planning parameter λ=0 can be set, and a more complex path search algorithm can be used in this partition; Among them, α = key area / number of partition nodes, the partition threshold β is the boundary value of whether the partition needs to be further divided. The larger the β value, the finer the partition division and the longer the time consumed; the smaller the β value, the coarser the partition division and the shorter the time consumed.

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