An optimal path planning method and system for unmanned aerial vehicles based on a dark warehouse

Abstract

The present invention provides a method and system for optimal path planning of a drone based on a dark warehouse. By acquiring the current position, target position and warehouse distribution information of the drone, multiple proposed paths from the current position to the target position are constructed, and the shortest path among the multiple proposed paths is selected as the optimal path. In a warehouse environment, when a certain position needs to be re-inspected or patrolled, the present solution can provide an optimal path for the drone, so that the drone does not need to fly around the warehouse shelves. According to the acquired warehouse distribution information, multiple proposed paths from the current position through the empty shelves to the target position are obtained, and the shortest path among the multiple proposed paths is selected as the optimal path of the drone, so that the drone patrols according to the optimal path, thereby shortening the patrol distance of the drone, improving the patrol efficiency of the drone, and reducing the unsafe factors that the drone may encounter in a long route, thereby increasing the safety of the drone patrol.

Description

Technical Field

[0001] The present application relates to the field of warehouse technology, and in particular to a method and system for optimal path planning for a drone based on a dark warehouse. Background Art

[0002] In today's industry, the warehouse system plays a vital role. The warehouse system consists of warehouses, inventory systems, material management systems and safety equipment, and each component plays a key role. First, the company is introducing advanced technologies such as the Internet of Things, big data analysis, artificial intelligence and automation technology to improve the level of intelligence in warehouse management. Through the application of intelligent technology, warehouse inventory, material positioning and cargo handling will be more efficient and accurate; secondly, the warehouse uses three-dimensional warehouse design and intelligent shelves and other technical means to maximize the utilization of warehouse space, improve storage efficiency and reduce storage waste; in addition, the warehouse management system introduces robots, automatic navigation vehicles, drones and other equipment to achieve automated warehouse operations, cargo handling and warehouse inspections, reduce manual operations, reduce operating costs and improve work efficiency.

[0003] However, in the process of drone inspection of warehouse systems, for inspections at designated locations, drones generally fly around from both sides of the shelves to the designated locations, causing the planned inspection path to become longer, reducing the drone's inspection speed, and thus affecting the efficiency and safety of drone inspections.

[0004] The information disclosed in the background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0005] The purpose of this application is to provide a method and system for optimal path planning of drones based on dark warehouses, which solves the technical problem that when inspecting a designated location, drones generally fly around the two sides of the shelf to the designated location, which makes the planned inspection path longer and reduces the inspection speed of the drone, thereby affecting the efficiency and safety of the drone inspection. To achieve the above purpose, the specific technical solution of the optimal path planning method of drones based on dark warehouses in this application is as follows:

[0006] An optimal path planning method for a drone based on a dark warehouse includes the following steps:

[0007] S1. Get the current location of the drone;

[0008] S2. Get the target location;

[0009] S3. Obtain warehouse distribution information, and construct multiple proposed paths from the current location to the target location based on the warehouse distribution information;

[0010] S4. Selecting the shortest path among the plurality of proposed paths;

[0011] S5. Using the shortest path as the optimal path for the UAV.

[0012] Preferably, S3 comprises the following steps:

[0013] S31. Assigning coordinate information to each storage unit for storing materials;

[0014] S32. Acquire multiple first coordinate information of the storage unit that does not store materials;

[0015] S33. Construct a plurality of the planned paths according to the current position, the target position and the first coordinate information.

[0016] Preferably, the S33 comprises the following steps:

[0017] S331. Obtain the second coordinate information of the current position and the third coordinate information of the target position;

[0018] S332. Connect the second coordinate information, any one of the first coordinate information, and the third coordinate information in sequence by straight lines to form a broken line segment, and use the broken line segment as the proposed path.

[0019] Preferably, S4 comprises the following steps:

[0020] S41. Calculate the lengths of the multiple broken line segments;

[0021] S42. Select the shortest path among the lengths of the plurality of broken line segments as the shortest path.

[0022] A drone optimal path planning system based on a dark warehouse, applying any one of the above-mentioned drone optimal path planning methods based on a dark warehouse, comprising:

[0023] The first acquisition module is used to obtain the current position of the drone;

[0024] The second acquisition module is used to acquire the target position;

[0025] The third acquisition module is used to obtain warehouse distribution information;

[0026] A first processing module, configured to construct a plurality of proposed paths from the current location to the target location according to the warehouse distribution information;

[0027] A judgment and selection module, used for selecting the shortest path among the plurality of proposed paths;

[0028] The planning module is used to use the shortest path as the optimal path for the UAV.

[0029] Preferably, the third acquisition module includes:

[0030] A first allocation unit is used to allocate coordinate information to each storage unit storing materials;

[0031] A first acquisition unit, used to acquire a plurality of first coordinate information of the storage unit that does not store materials;

[0032] The first processing module is used to construct a plurality of the planned paths according to the current position, the target position and the first coordinate information.

[0033] Preferably, the steps include:

[0034] The first acquisition module is used to acquire the second coordinate information of the current position, and the second acquisition module is used to acquire the third coordinate information of the target position;

[0035] The first processing module is used to sequentially connect the second coordinate information, any one of the first coordinate information, and the third coordinate information in a straight line to form a broken line segment, and the broken line segment is used as the proposed path.

[0036] Preferably, the judgment and selection module includes:

[0037] A calculation unit, used for calculating the lengths of the plurality of broken line segments;

[0038] The selection unit is used to select the shortest one among the lengths of the plurality of broken line segments as the shortest path.

[0039] A computer device comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein when the program or instruction is executed by the processor, the steps of any one of the above-mentioned methods for optimal path planning of a drone based on a dark warehouse are implemented.

[0040] A computer-readable storage medium stores a program or instruction on the readable storage medium, and when the program or instruction is executed by a processor, the steps of any one of the above-mentioned methods for optimal path planning of a drone based on a dark warehouse are implemented.

[0041] The technical solution adopted in this application can achieve the following beneficial effects:

[0042] It can be seen from the above technical scheme that the present invention provides a method and system for optimal path planning of a drone based on a dark warehouse, and its beneficial effects are: in a warehouse environment, when a certain location needs to be re-inspected or patrolled, the present scheme can provide the drone with the optimal planned patrol path, so that the drone does not need to fly around the warehouse shelves, and obtains multiple planned paths from the current position through the empty shelves to the target position based on the acquired warehouse distribution information, and selects the shortest path among the multiple planned paths as the optimal path for the drone, so that the drone patrols along the optimal path, thereby shortening the patrol distance of the drone, improving the patrol efficiency of the drone, and at the same time reducing the unsafe factors that the drone may encounter in long routes, thereby increasing the safety of drone patrols. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the proposed path for the drone to pass through the shelf.

[0044] Figure 2 Schematic diagram of the shelf storage unit structure.

[0045] Figure 3 Schematic diagram of shelf arrangement structure.

[0046] Among them: second coordinate information 100, third coordinate information 200, planned path 300, storage unit 400, first coordinate information 500. DETAILED DESCRIPTION

[0047] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present application are given in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thoroughly and comprehensively understood.

[0048] It should be noted that when a device is considered to be "connected" to another device, it may be directly connected to the other device or there may be an intermediate device at the same time. The terms "inside", "top", "upper", "lower", "upper", "lower" and similar expressions used herein are for illustrative purposes only and are not intended to be the only implementation method.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0050] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0051] Please refer to Figures 1 to 3 , an optimal path planning method for a UAV based on a black light warehouse, comprising the following steps:

[0052] S1. Obtain the current position of the drone: The current position of the drone is generally obtained using a GPS positioning system; specifically, during navigation, the drone sends information in real time through a communication system, and the background system continuously receives process information sent back by the drone, including real-time location information, warehouse inspection information, etc.

[0053] S2. Obtain the target location; wherein the user may input the longitude and latitude coordinates of the target location through a graphical interface or other interactive interface, or directly mark the target location on a map.

[0054] In a specific embodiment, the user can specify a location according to needs. For example, when there is a possibility of excessive humidity in a certain area of ​​the warehouse, a drone is needed to inspect the area to check for leaks or other conditions. At this time, a certain point in the area is selected as the target location. This target location is convenient for observing the environmental conditions of the area.

[0055] S3. Obtain warehouse distribution information, and construct multiple proposed paths 300 from the current position to the target position based on the warehouse distribution information; wherein the warehouse distribution information refers to the location information and storage status of each row of shelves and materials on the shelves in the warehouse. The shelves can be divided into shelves with material storage and empty shelves without material storage according to the storage status. The drone can choose to pass through adjacent empty shelves without material storage to the target position. Since there may be multiple empty shelves, multiple proposed paths 300 may be formed from the current position to the target position.

[0056] S4. Select the shortest path among the multiple proposed paths 300; wherein the lengths of the multiple proposed paths 300 in the previous step are compared to obtain the shortest path.

[0057] S5. Using the shortest path as the optimal path for the UAV.

[0058] In an embodiment of the present application, a method for optimal path planning for a drone based on a dark warehouse is disclosed. Specifically, for example, during a drone inspection of a warehouse, when we find an abnormality in a certain area, we need to check the area. Manual inspection is slow and requires the deployment of personnel. At this time, if a drone goes to inspect, the speed is faster, saving time and effort. Especially in an emergency, according to the warehouse distribution information, the drone can select the optimal path to pass through the storage unit 400 of the shelf without detouring the shelf, thereby shortening the drone's inspection path. In order to further confirm the corresponding environmental information of the abnormal area, the user specifies a point in the abnormal area as the target location. The drone needs to fly to the target location. First, the current location of the drone needs to be obtained. In this application, the current location of the drone is located by GPS, and the drone communication system sends the location information in real time. The system receives the location information of the drone in real time, and then determines the target location. The target location can be obtained by the system based on the abnormal area recommendation, or it can be obtained by the user-specified location. After the system determines the target location, it constructs multiple proposed paths 300 according to the warehouse distribution information. The proposed path 300 includes all possible proposed paths 300 from the current location of the drone to the target location. There may be one or more proposed paths 300. Then, the length of each proposed path 300 is compared by computer to obtain the shortest path, which is the optimal path. The optimal path is sent to the drone, so that the drone flies to the target location according to the calculated optimal path, thereby making up for the shortcomings of manual inspection being inflexible and wasting personnel.

[0059] By adopting the optimal path planning method for drones in this application, when the drone inspects the warehouse, it receives the designated target location and does not need to detour around the shelves. According to the acquired warehouse distribution information, it passes through the shelves where no materials are stored to reach the target location, thereby shortening the drone's inspection distance, increasing the inspection speed of the designated location in emergency situations, improving the drone's inspection efficiency, and at the same time, reducing the unsafe factors that the drone may encounter on long routes, thereby improving the safety of drone inspections.

[0060] Furthermore, the S3 comprises the following steps:

[0061] S31. Assign coordinate information to each storage unit 400 used to store materials; wherein the shelves in the warehouse are divided into different storage areas or storage units 400, and then a unique coordinate information is assigned to each storage unit 400, and this coordinate information uses specific three-dimensional space coordinates to facilitate management and positioning.

[0062] First, the entire warehouse space is divided according to a three-dimensional coordinate system. Each shelf has multiple storage units 400, and each storage unit 400 can be uniquely identified by a three-dimensional coordinate value (x, y, z). x and y can represent the left and right and front and back directions of the shelf in the horizontal direction, and the z axis represents the vertical direction of the shelf. For example, in a specific warehouse, there are x rows of shelves, and each row of shelves has y columns and z rows of storage units 400. The center position of each storage unit 400 is represented by a set of three-dimensional coordinates (x, y, z), so that each storage unit 400 has a unique three-dimensional coordinate.

[0063] S32. Obtain multiple first coordinate information 500 of the storage unit 400 that does not store supplies; wherein, among all the above storage units 400, some storage units 400 store supplies, and some storage units 400 do not store supplies. According to the storage status of the storage unit 400, the storage units 400 are divided into storage units 400 with supplies and empty storage units 400, and the coordinate information of each empty storage unit 400 is recorded and statistically marked as the first coordinate information 500 to obtain a set of first coordinate information 500. The number of first coordinate information 500 is determined by the number of empty storage units, and there may be one or more first coordinate information 500. For example, see Figures 2 to 3 In the figure, it is assumed that the intersection of the first column and the first row of the first row from left to right is the origin. Taking the first row of warehouse shelves as an example, there are 5 first coordinate information 500, and the three-dimensional coordinates of each first coordinate information 500 can be written as (1, 2, 3), (1, 4, 2), (1, 4, 5), (1, 5, 4), and (1, 6, 2) respectively.

[0064] S33. Construct multiple proposed paths 300 according to the current position, the target position and the first coordinate information 500. Figure 1 to Figure 2 , if from the current position (such as Figure 1 A) to the target location (such as Figure 1 In B), it is necessary to pass through 1 shelf, on which there are 35 storage units 400, and each of the 35 storage units 400 is assigned unique coordinate information, of which there are 30 storage units 400 with supplies and 5 empty storage units 400, which are marked with numbers 1 to 5, respectively, that is, there are 5 first coordinate information 500, which can generate 5 planned paths 300.

[0065] The above method is used to divide the entire warehouse space according to a three-dimensional coordinate system, and coordinate information is assigned to each storage unit 400 to facilitate the calculation of multiple planned paths 300. After obtaining the current position, the target position and the first coordinate information 500, the length of each planned path 300 can be calculated in the next step.

[0066] Furthermore, the step S33 constructs a plurality of the proposed paths 300, comprising the following steps:

[0067] The second coordinate information 100 of the current position and the first coordinate information 500 of the target position are obtained; wherein the second coordinate information 100 of the current position and the first coordinate information 500 of the target position are both known coordinates.

[0068] The second coordinate information 100, any one of the first coordinate information 500, and the first coordinate information 500 are connected in sequence to form a broken line segment, and the broken line segment is the proposed path 300. In which, taking the proposed path 300 passing through only one shelf as an example, the second coordinate information 100 is marked as (x 前 ,y 前 , z 前 ), the third coordinate information 200 is marked as (x 标 ,y 标 , z 标 ), there are i first coordinate information 500 marked as (x1, y1, z1)...(x i ,y i , z i ), the second coordinate information 100 and the multiple first coordinate information 500 are connected to form multiple first line segments, the multiple first coordinate information 500 and the third coordinate information 200 are connected to form multiple second line segments, the first line segments and the second line segments are connected to form multiple broken line segments, each of which is a proposed path 300, thereby obtaining multiple proposed paths 300, which is convenient for the subsequent calculation of the proposed paths 300.

[0069] Furthermore, if it is necessary to pass through more than one shelf from the current position of the drone to the target position, the first coordinate information 500 of the shortest path through the first shelf is obtained through the above steps, and the first coordinate information 500 is used as the first passing point of the drone through the first shelf. At this time, the first passing point is the starting point, that is, the current position through the second shelf, that is, the first passing point is the current position through the second shelf, and the multiple empty storage units 400 on the second shelf are the multiple first coordinate information 500 passing through the second shelf. The distance from the first passing point to the multiple first coordinate information 500 passing through the second row of shelves is calculated, and the shortest distance is compared. The first coordinate information 500 of the shortest distance from the first passing point to the second row of shelves is the second passing point, and the judgment of the third passing point repeats the above steps until the target position is obtained, and a broken line segment composed of multiple line segments is obtained. The broken line segment is the optimal path of the drone inspection route in this application.

[0070] Furthermore, the step S4 selects the shortest path among the plurality of proposed paths 300, comprising the following steps:

[0071] Calculate the length of multiple broken line segments; wherein, the distance from the current position of the drone to the target position, multiple broken line segments have been constructed in the previous step, and the length of each broken line segment is calculated.

[0072] The shortest one of the multiple broken line segments is selected as the shortest path. By comparing the lengths of each broken line segment, the shortest proposed path 300 is obtained, i.e., the optimal path of the drone, ensuring that the drone reaches the target location by the optimal path, saving flight time and energy consumption.

[0073] In summary, the present application provides an optimal path planning method for a drone based on a dark warehouse. According to the warehouse distribution information, one or more proposed paths 300 from the second coordinate information 100 to the third coordinate information 200 are calculated. The situation of one proposed path 300 may be that there is no need to pass through the shelf to reach the target location, or there is only one empty storage unit 400 on the shelf that just passes through, so there is only one proposed path 300; the situation of multiple proposed paths 300 is that there are multiple empty storage units 400 on the shelf that the proposed path 300 needs to pass through, thereby forming multiple proposed paths 300, and the drone finally flies along the optimal path, reducing the unsafe factors that the drone may encounter in long routes, thereby improving the safety of drone inspections.

[0074] The embodiment of the present application also discloses an optimal path planning system for a drone based on a dark warehouse, which is applied to any of the above-mentioned optimal path planning methods for a drone based on a dark warehouse, including: a first acquisition module for acquiring the current position of the drone; a second acquisition module for acquiring the target position; a third acquisition module for acquiring warehouse distribution information; a first processing module for constructing multiple proposed paths 300 from the current position to the target position according to the warehouse distribution information; a judgment and selection module for selecting the shortest path among the multiple proposed paths 300; and a planning module for using the shortest path as the optimal path for the drone. When working, the first acquisition module acquires the current position of the drone in real time, and the second acquisition module acquires the target position specified by the user or recommended by the system. Then, according to the warehouse distribution information of the warehouse acquired by the third acquisition module, multiple proposed paths 300 from the current position to the target position are constructed through the first processing module, which is conducive to the accuracy and immediacy of path planning.

[0075] In the specific working process, the drone inspects the warehouse according to the daily inspection route. For example, when an abnormally high temperature area suddenly appears in the warehouse, the drone is required to inspect the area to confirm the environmental information of the abnormal area. The user specifies a point in the abnormal area as the target location, and the drone is required to fly to the target location. First, the first acquisition module obtains the current position of the drone, and the second acquisition module obtains the target location. At the same time, the third acquisition module obtains the warehouse distribution information of the warehouse, and then the information obtained by the first acquisition module, the second acquisition module, and the third acquisition module are transmitted to the first processing module. The first processing module constructs multiple proposed paths 300 from the current position to the target position, and sends the multiple proposed paths 300 to the judgment and selection module. The judgment and selection module calculates the shortest path among the above-mentioned proposed paths 300, and sends the shortest path to the planning module. The planning module determines the shortest path as the optimal path and sends it to the drone. The drone navigates according to the optimal path sent by the planning module.

[0076] Furthermore, the third acquisition module includes: a first allocation unit for assigning coordinate information to each storage unit 400 for storing materials; a first acquisition unit for acquiring multiple first coordinate information 500 of the storage unit 400 that does not store materials; and the first processing module for constructing multiple planned paths 300 based on the current position, the target position and the first coordinate information 500.

[0077] Specifically, when the third acquisition module acquires the warehouse distribution information of the warehouse, the first allocation unit first assigns a unique coordinate information to each storage unit 400 of the storage shelf, and the first acquisition unit detects the storage status of each storage unit 400, records the coordinate information of each vacant storage unit 400, and statistically marks it as the first coordinate information 500 to obtain a set of first coordinate information 500; the first coordinate information 500 is sent to the first processing module, and at the same time, the current position and target position acquired by the above-mentioned first acquisition module and the second acquisition module are also sent to the first processing module, and the first processing module constructs multiple proposed paths 300.

[0078] Furthermore, the following steps are included: the first acquisition module is used to acquire the second coordinate information 100 of the current position, and the second acquisition module is used to acquire the third coordinate information 200 of the target position; the first processing module is used to sequentially connect the second coordinate information 100, any one of the multiple first coordinate information 500, and the third coordinate information 200 in a straight line to form a broken line segment, and the broken line segment is used as the proposed path 300.

[0079] Specifically, the first acquisition module acquires the second coordinate information 100 of the current position of the drone, the second acquisition module acquires the third coordinate information 200 of the target position through the user-specified position or the system-recommended position, and the first processing module forms a plurality of broken line segments based on the second coordinate information 100, a plurality of first coordinate information 500 and the third coordinate information 200, and uses the broken line segments as the proposed path 300.

[0080] Furthermore, the judgment and selection module includes: a calculation unit for calculating the lengths of the multiple broken line segments; and a selection unit for selecting the smallest length of the multiple broken line segments as the shortest path. The judgment and selection module ensures that the system can accurately obtain the distance information of each proposed path 300, providing accurate data support for the subsequent shortest path selection.

[0081] Specifically, when the judgment and selection module judges the proposed path 300, the calculation unit first calculates the lengths of the multiple proposed paths 300 mentioned above, and then sends the lengths of the multiple proposed paths 300 to the selection unit. The selection unit selects the shortest proposed path 300 among the multiple proposed paths 300, and finally sends the shortest path selected by the selection unit to the planning module.

[0082] The above-mentioned optimal path planning system for drones based on dark warehouses is adopted, the current position of the drone is obtained by the first acquisition module, the target position is obtained by the second acquisition module, and the warehouse distribution information is obtained by the third acquisition module, and the above information is sent to the first processing module. The first processing module constructs multiple proposed paths from the current position to the target position according to the warehouse distribution information, and then sends the proposed paths to the judgment and selection module. The judgment and selection module selects the shortest path among the multiple proposed paths and sends the shortest path to the planning module. The planning module uses the shortest path as the optimal path of the drone and sends it to the drone. The drone navigates according to the optimal path, thereby improving the flight efficiency of the drone, reducing the impact of human factors on the flight path, and improving navigation safety.

[0083] The present application also discloses a computer device, including a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements any of the steps of the above-mentioned method for optimal path planning of a drone based on a dark warehouse. The computer device can effectively improve the efficiency and accuracy of the drone path planning system, and provide a more intelligent and efficient solution for warehouse management and transportation.

[0084] In specific work, this embodiment provides a computer device dedicated to realizing the optimal path planning of the above-mentioned warehouse drone, aiming to efficiently handle emergencies. The drone can inspect or review along the optimal path. The processor is used to call the corresponding module for data processing and path planning, and the memory is used to store relevant information of the warehouse. Through the implementation of the above-mentioned computer equipment, the optimal path planning of the drone in the warehouse environment can be realized efficiently and accurately, significantly improving the ability to respond to abnormal areas.

[0085] A computer-readable storage medium, characterized in that a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of any one of the above-mentioned methods for optimal path planning of a drone based on a dark warehouse are implemented. The computer-readable storage medium provides a flexible storage and execution method. For example, the path planning method is stored in the readable storage medium and can be easily executed on different computer devices to realize the transplantation and sharing of the path planning function.

[0086] In the specific work, the program or instructions of the drone optimal path planning system based on the dark warehouse are stored on a readable storage medium, including obtaining information about the goods in the warehouse, including the location information and storage status of the shelves in the warehouse and the materials on the shelves, and obtaining the starting position and target position of the drone; according to the target position and the starting position of the drone, the optimal path planning of the drone's flight route is obtained by using the drone optimal path planning method, and the optimal path planning instructions are transmitted to the drone to implement the inspection task. The drone optimal path planning system based on the dark warehouse in this application can realize the transplantation and sharing of the drone path planning function through the program or instructions stored on the readable storage medium, thereby improving enterprise efficiency and reducing enterprise costs.

[0087] It should be noted that during the drone inspection process, the image sensor can be used to detect in real time whether there are obstacles; if there are obstacles, the drone can be controlled to make fine adjustments to avoid obstacles. For example, during the drone inspection process, the image sensor carried by the drone monitors the surrounding environment of the warehouse in real time to detect possible obstacles in time. Once an obstacle is found during the inspection, the system will immediately start the program to control the drone to make fine adjustments to avoid collision with the obstacle and ensure the smooth progress of the inspection task. Similarly, if the drone encounters an obstacle while inspecting along the optimal path 300, such as reinforcement ribs on the shelf or materials temporarily placed by the user, the drone obstacle avoidance system can be started to make fine adjustments to pass the obstacle.

[0088] In summary, the optimal path planning method for drones based on dark warehouses disclosed in this application can enable drones to obtain the shortest path to the target location by passing through the shelves when receiving temporary inspection tasks during warehouse inspections, obtain environmental information of the target location, and improve the work efficiency of drone inspections. Among them, the optimal path planning system for drones based on dark warehouses is used to support the above-mentioned optimal path planning method for drones based on dark warehouses and ensure the implementation of the above-mentioned method. Regarding computer-readable storage media, it can be applied to any aircraft or flight terminal.

[0089] What is disclosed above is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. A person skilled in the art can understand that all or part of the processes of the above embodiments and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for optimal path planning of a drone based on a dark warehouse, characterized in that: The following steps are involved: S1. Get the current location of the drone; S2. Get the target position; S3. Obtain warehouse distribution information, and construct multiple proposed paths from the current location to the target location based on the warehouse distribution information; S4. Selecting the shortest path among the plurality of proposed paths; S5. Using the shortest path as the optimal path for the UAV; Wherein, the S3 comprises the following steps: S31. Divide the shelves in the warehouse into different storage units, and assign coordinate information to each storage unit for storing materials; S32. Acquire multiple first coordinate information of the storage unit that does not store materials; S33. Construct a plurality of the planned paths according to the current position, the target position and the first coordinate information.

2. The optimal path planning method for unmanned aerial vehicles based on a dark warehouse according to claim 1 is characterized in that: The S33 comprises the following steps: S331. Obtain the second coordinate information of the current position and the third coordinate information of the target position; S332. Connect the second coordinate information, any one of the first coordinate information, and the third coordinate information in sequence by straight lines to form a broken line segment, and use the broken line segment as the proposed path.

3. The optimal path planning method for unmanned aerial vehicles based on a dark warehouse according to claim 2 is characterized in that: The S4 comprises the following steps: S41. Calculate the lengths of the multiple broken line segments; S42. Select the shortest path among the lengths of the plurality of broken line segments as the shortest path.

4. An optimal path planning system for drones based on a dark warehouse, characterized in that: The method for optimal path planning of a drone based on a dark warehouse as claimed in any one of claims 1 to 3 comprises: The first acquisition module is used to obtain the current position of the drone; The second acquisition module is used to acquire the target position; The third acquisition module is used to obtain warehouse distribution information; A first processing module, configured to construct a plurality of proposed paths from the current location to the target location according to the warehouse distribution information; A judgment and selection module, used for selecting the shortest path among the plurality of proposed paths; A planning module, used for taking the shortest path as the optimal path for the UAV; Wherein, the third acquisition module includes: A first allocation unit is used to divide the shelves in the warehouse into different storage units and allocate coordinate information to each storage unit storing materials; A first acquisition unit, used to acquire a plurality of first coordinate information of the storage unit that does not store materials; The first processing module is used to construct a plurality of the planned paths according to the current position, the target position and the first coordinate information.

5. The optimal path planning system for unmanned aerial vehicles based on a dark warehouse according to claim 4 is characterized in that: The following steps are involved: The first acquisition module is used to acquire the second coordinate information of the current position, and the second acquisition module is used to acquire the third coordinate information of the target position; The first processing module is used to sequentially connect the second coordinate information, any one of the first coordinate information, and the third coordinate information in a straight line to form a broken line segment, and the broken line segment is used as the proposed path.

6. The optimal path planning system for unmanned aerial vehicles based on a dark warehouse according to claim 5 is characterized in that: The judgment and selection module includes: A calculation unit, used for calculating the lengths of the plurality of broken line segments; The selection unit is used to select the shortest one among the lengths of the plurality of broken line segments as the shortest path.

7. A computer device, characterized in that: The invention comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of a method for optimal path planning of a drone based on a black light warehouse as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or the instruction is executed by the processor, the steps of the optimal path planning method for a drone based on a black light warehouse described in any one of claims 1 to 3 are implemented.

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