Unmanned aerial vehicle safety scheduling optimization algorithm

Abstract

The invention discloses an unmanned aerial vehicle safety scheduling optimization algorithm, and relates to the technical field of storage scheduling, and the algorithm comprises the steps: carrying out the modeling of a storage environment based on the position, shape and size information of goods shelves and mechanical equipment in a warehouse, and building a preliminary environment model; and scanning the environment by using a laser radar carried by the unmanned aerial vehicle to obtain environment data in the warehouse, and updating the environment model in real time in combination with the preliminary environment model. According to the unmanned aerial vehicle safety scheduling optimization algorithm, the accuracy and efficiency of a flight path are remarkably improved through an integrated path planning technology, the optimal flight path is customized for the unmanned aerial vehicle in combination with actual conditions of topography and landform of a storage area and obstacle distribution, redundancy and waste in the flight process are reduced, and meanwhile, the unmanned aerial vehicle safety scheduling optimization algorithm has good application prospects. And the path can be dynamically adjusted according to the real-time environment change to effectively deal with emergencies, so that the flight efficiency and safety are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse scheduling, and in particular to an optimization algorithm for the safe scheduling of unmanned aerial vehicles. Background Art

[0002] With the booming development of e-commerce and consumers' increasing demand for logistics speed, the logistics industry is facing unprecedented challenges. Digital transformation is an important trend that all industries are actively promoting. In the logistics industry, digital transformation means the introduction of advanced information technology and smart equipment to achieve intelligent, automated and visual warehousing, transportation, distribution and other links. As an important part of digital transformation, drone technology can be used in the warehousing field to significantly improve management efficiency and accuracy and reduce labor costs.

[0003] The storage environment is relatively complex. When drones are used to identify and dispatch goods in the storage environment, obstacles such as shelves, mechanical equipment, and personnel in the warehouse will interfere with or threaten the flight of drones. In addition, when drones encounter irregular obstacles during flight, the danger of drone flight will be further increased. Therefore, how to take warehouse safety as the ultimate goal, rely on high-precision positioning navigation and radar obstacle avoidance technology to achieve drone positioning and safe dispatch, and prevent collision accidents is the problem we need to solve. To this end, a drone safety dispatch optimization algorithm is proposed. Summary of the invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a drone safety scheduling optimization algorithm, comprising the following steps: Step 1: Model the storage environment based on the location, shape and size information of the shelves and mechanical equipment in the warehouse, and establish a preliminary environment model; Step 2: Use the laser radar carried by the drone to scan the environment, obtain the environmental data in the warehouse, and update the environmental model in real time based on the preliminary environmental model. The environmental data in the warehouse includes moving shelves or personnel; Step 3: Based on the real-time updated environment model and obstacle information, in conjunction with GPS and IMU positioning and navigation technology, obtain the position, speed and attitude information of the drone in real time, and use the path planning algorithm to formulate a flight path for the drone; Step 4: LiDAR-based radar obstacle avoidance technology monitors obstacles around the drone in real time, obtains distance, speed and direction information of obstacles through radar beam scanning and reflection of the LiDAR, and adjusts the flight speed and direction of the drone based on the preset safety distance threshold and radar data to avoid collision with obstacles; Step 5. Based on the results of path planning, navigate and control the UAV, monitor the flight status and environmental changes of the UAV in real time, and dynamically adjust the flight plan and obstacle avoidance strategy of the UAV to ensure warehouse safety.

[0005] Preferably, in step 1, the process of establishing the preliminary environment model includes: Collect detailed information on all fixed and mobile obstacles in the warehouse, including the location, shape and size of shelves, mechanical equipment and personnel passages; conduct on-site measurements and consult the warehouse layout to obtain accurate location information of shelves and mechanical equipment, and record the shapes, sizes and relative positions of shelves and mechanical equipment; The warehouse environment is divided into different areas, namely storage area, operation area and personnel passage, and the personnel passage is set as a no-fly zone; Preprocess the collected data to ensure the accuracy and completeness of the data. For each obstacle, create a three-dimensional model of warehouse elements such as shelves, mechanical equipment, and personnel passages in the modeling tool based on the preprocessed data to ensure that the shape, size, and position of the model are consistent with the actual situation; Grid the warehouse space and mark the shelves, machinery and other obstacles in each grid space, and then set the height limit, obstacle type and safety distance parameters for each grid space; All constructed three-dimensional models and grid cells are integrated into a unified model to obtain a preliminary environment model.

[0006] Preferably, in step 2, the process of updating the environment model in real time includes: Use the drone route planning software on the computer to plan the drone's flight route according to the warehouse layout and actual environment. The drone takes off according to the planned route and uses the laser radar to scan the warehouse environment during the flight. The laser radar measures the distance and obtains the three-dimensional information of the warehouse environment by emitting laser beams and receiving reflected signals. The scanned data of the warehouse environment is transmitted to the computer in real time for storage and processing, and the raw data is solved using Shuttle and gAirHawk software; In the Shuttle software, data sorting and POS solution steps are performed to obtain the POS result file, which contains the position and attitude information of the drone. In the gAirHawk software, the POS result file, Lidar original data file and photo file are imported to perform coordinate system setting, route division and point cloud computing steps to obtain 3D laser point cloud data, and then analyze the point cloud volume, slice area and height range of the 3D laser point cloud data. The solved 3D laser point cloud data is compared and fused with the preliminary environmental model, and the preliminary environmental model is updated in real time according to the position and shape information of the mobile shelves or personnel in the point cloud data to obtain an updated environmental model. The updated environmental model will more accurately reflect the actual situation in the warehouse and provide strong support for subsequent environmental monitoring and management.

[0007] Preferably, the point cloud volume is calculated using a slicing method, and its calculation expression is: ; Where V is the point cloud volume, is the slice area of ​​the ith slice, N is the number of slices, and h is the slice spacing; The calculation expression of the slice area is: ; in, is the slice area of ​​the ith slice, m is the number of boundary points, is the boundary point of slice i; The calculation expression of the height range of the point cloud is: ; Where H is the height range of the point cloud, is the maximum height of a point in the point cloud, is the minimum height of a point in the point cloud.

[0008] Preferably, in step 3, the process of formulating the flight path of the drone includes: Use GPS technology to obtain the location information of the drone, and use IMU (inertial measurement unit) to measure the angular velocity and acceleration of the drone. Combined with the initial attitude information, the real-time attitude and speed information of the drone are calculated. The GPS and IMU data are fused to improve the accuracy and stability of the drone's positioning and navigation. Among them, GPS provides the longitude, latitude and altitude position data of the drone; Based on the real-time updated environment model and obstacle information, as well as the current position, speed and attitude information of the drone, the Dijkstra algorithm is selected for path planning; In the path planning algorithm, the current position coordinates of the drone, the target position coordinates, and the real-time updated environmental model and obstacle information are input, and the steering angle and reference distance of the current position are analyzed to calculate an optimal path that avoids obstacles and meets flight safety requirements. The flight path includes the starting point, end point, intermediate nodes, flight altitude and speed parameters. The flight path is converted into control instructions for the UAV, and the flight mission is executed through the control system of the UAV. During the flight, the UAV adjusts the flight attitude and speed in real time through the closed-loop control algorithm of PID control to ensure stable flight along the planned path. By integrating path planning technology, the accuracy and efficiency of the flight path are significantly improved. Combined with the actual conditions of the topography and obstacle distribution in the storage area, the optimal flight path is customized for the UAV, reducing redundancy and waste during the flight process. At the same time, the path can be dynamically adjusted according to real-time environmental changes to effectively respond to emergencies, thereby further improving flight efficiency and safety.

[0009] Preferably, the cost function expression of the optimal path is: ; ; ; in, is the cost function of the optimal path, which represents the minimization of the cost function. is the actual cost from the starting point to the current position coordinates, is the heuristic estimated cost from the current position coordinates to the target position coordinates, is the current position coordinate, is the target position coordinate, is the steering angle of the current position coordinates, is the cost weight coefficient of power consumption, is the cost weight coefficient of the steering angle, is the base distance used for scaling in the logarithmic function, As the distance between nodes increases, As the distance from the current node to the target point increases, and A positive constant used to adjust the weights of power consumption and steering angle in the cost function.

[0010] Preferably, in step 4, the process of adjusting the flight speed and direction of the drone includes: During the flight of the drone, the laser radar emits a laser beam to the target area through its internal laser diode. By using phased array technology, the phase difference of the laser beam is adjusted to achieve rapid scanning of the beam, covering the target area, so that the laser radar can obtain three-dimensional information of the target area. The laser radar receiver receives the reflected laser signal, obtains obstacle information, and converts it into an electrical signal for subsequent signal amplification, filtering and demodulation steps to identify the location, shape and size of the obstacle; After identifying obstacles, the flight path is replanned based on the current environment information and the motion status of the drone to find the optimal path from the current location to the target location while avoiding all known obstacles. The surrounding obstacle information is monitored in real time, and the safety factor of the flight path is calculated based on the preset safety distance threshold to determine whether to replan the flight path to ensure flight safety. Pre-set the warning threshold, compare the safety factor with the warning threshold, and determine whether to issue a warning based on the comparison result to remind staff to intervene and adjust the drone's path planning; Based on the results of path planning, the UAV is navigated and controlled to achieve safe obstacle avoidance, including adjusting the flight speed and direction of the UAV to ensure that it flies stably along the planned path while avoiding all obstacles. The safety and reliability of the UAV in complex environments are significantly improved through real-time monitoring and dynamic adjustment of the flight path. Combined with the radar obstacle avoidance technology and high-precision positioning and navigation technology of the lidar, the position, speed and attitude information of the UAV, as well as detailed information on surrounding obstacles, can be obtained in real time, and the path can be re-planned when necessary, ensuring that the UAV can respond quickly when encountering unknown obstacles or environmental changes, thereby reducing the probability of accidents and improving the reliability of mission execution.

[0011] Preferably, the specific process of staff intervention includes: Monitor the safety factor of the flight path in real time and compare it with the preset warning threshold to analyze whether the safety factor is lower than the preset warning threshold; If the safety factor is lower than the warning threshold, it indicates that the safety of the current path is insufficient, and the current path is marked and a warning is issued; Based on the triggered early warning mechanism, a prompt is issued on the console to remind the staff to intervene and take action, and start the path replanning program, input the latest environmental information and obstacle data, and use the path planning algorithm to calculate a new safe path until the safety factor exceeds the early warning threshold, thus determining the result of the path planning.

[0012] Preferably, the calculation expression of the safety factor is: ; Among them, S is the safety factor, and the value range of S is between 0 and 1. It is a preset safety distance threshold, which is used to ensure that there is enough distance between the drone and obstacles to avoid collision. is the distance from the drone to the nearest obstacle, The smaller the value, the closer the drone is to the obstacle, and the lower the safety factor will be. v is the current speed of the drone, which affects the calculation of the safety factor. The faster the speed, the shorter the reaction time and the lower the safety factor. is the maximum speed of the drone, used to normalize the current speed.

[0013] Preferably, in step 5, the process of navigating and controlling the drone includes: Convert the path planning results into navigation instructions, send them to the UAV's flight control system, and adjust the UAV's flight attitude and speed through actuators (motors, propellers, and servos); Monitor the position, speed and attitude of the drone in real time to ensure that the drone flies along the planned path; Use lidar to perceive the environment around the drone in real time, including static and dynamic obstacles, and dynamically adjust the drone's flight plan based on real-time monitored environmental changes.

[0014] The present invention provides a drone safety scheduling optimization algorithm, which has the following beneficial effects: 1. The drone safety scheduling optimization algorithm significantly improves the accuracy and efficiency of the flight path by integrating path planning technology. It customizes the optimal flight path for the drone based on the actual conditions of the topography and obstacle distribution in the storage area, reducing redundancy and waste during the flight process. At the same time, it can also dynamically adjust the path according to real-time environmental changes, effectively respond to emergencies, and further improve flight efficiency and safety.

[0015] 2. The UAV safety scheduling optimization algorithm significantly improves the safety and reliability of UAVs in complex environments by real-time monitoring and dynamic adjustment of flight paths. Combined with the radar obstacle avoidance technology and high-precision positioning and navigation technology of lidar, it can obtain the UAV's position, speed and attitude information in real time, as well as detailed information on surrounding obstacles, and re-plan the path when necessary, ensuring that the UAV can respond quickly when encountering unknown obstacles or environmental changes, thereby reducing the probability of accidents and improving the reliability of mission execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a method flow chart of a drone safety scheduling optimization algorithm of the present invention; Figure 2 A flow chart of the present invention for updating the environment model in real time; Figure 3 Flowchart for developing a UAV flight path for the present invention; Figure 4 The figure is a flow chart for adjusting the flight speed and direction of the UAV of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0018] The first embodiment, as Figures 1 to 3 As shown, the present invention provides a technical solution: a drone safety scheduling optimization algorithm, comprising the following steps: Step 1: Based on the location, shape and size information of the shelves and mechanical equipment in the warehouse, the storage environment is modeled and a preliminary environmental model is established. Detailed information of all fixed and mobile obstacles in the warehouse is collected, including the location, shape and size of the shelves, mechanical equipment and personnel passages. Field measurements are taken and the warehouse layout diagram is consulted to obtain the precise location information of the shelves and mechanical equipment, and the shapes, sizes and relative positional relationships between the shelves and mechanical equipment are recorded. The warehouse environment is divided into different areas, namely the storage area, the operation area and the personnel passage, and the personnel passage is set as a no-fly zone. The collected data is preprocessed to ensure the accuracy and completeness of the data. For each obstacle, a three-dimensional model of the warehouse elements of the shelves, mechanical equipment and personnel passage is created in the modeling tool according to the preprocessed data to ensure that the shape, size and location of the model are consistent with the actual situation. The warehouse space is gridded and the shelves, mechanical equipment and other obstacles in each grid space are marked. Then, the height limit, obstacle type and safety distance parameters of each grid space are set. All constructed three-dimensional models and grid units are integrated into a unified model to obtain a preliminary environmental model. Step 2: Use the laser radar on the drone to scan the environment, obtain the environmental data in the warehouse, and update the environmental model in real time based on the preliminary environmental model. The environmental data in the warehouse includes moving shelves or personnel. Use the drone route planning software on the computer to plan the flight route of the drone according to the layout of the warehouse and the actual environment. The drone takes off according to the planned route and uses the laser radar to scan the warehouse environment during the flight. The laser radar measures the distance and obtains the three-dimensional information of the warehouse environment by emitting laser beams and receiving reflected signals. The scanned data of the warehouse environment is transmitted to the computer in real time for storage and processing, and the original data is solved using Shuttle and gAirHawk software. In the Shuttle software, the data is processed. Sorting and POS solution steps, get the POS result file, the POS result file contains the position and attitude information of the drone, and import the POS result file, Lidar original data file and photo file into the gAirHawk software, perform coordinate system setting, route division and point cloud computing steps, get the 3D laser point cloud data, and then analyze the point cloud volume, slice area and height range of the 3D laser point cloud data, compare and fuse the solved 3D laser point cloud data with the preliminary environment model, and update the preliminary environment model in real time according to the position and shape information of the mobile shelves or personnel in the point cloud data, and get the updated environment model, which will more accurately reflect the actual situation in the warehouse and provide strong support for subsequent environmental monitoring and management; Furthermore, the point cloud volume is calculated using the slicing method, and its calculation expression is: ; Where V is the point cloud volume, is the slice area of ​​the ith slice, N is the number of slices, and h is the slice spacing; The calculation expression of the slice area is: ; in, is the slice area of ​​the ith slice, m is the number of boundary points, is the boundary point of slice i; The calculation expression of the height range of the point cloud is: ; Where H is the height range of the point cloud, is the maximum height of a point in the point cloud, is the minimum height of a point in the point cloud; Step 3. According to the real-time updated environmental model and obstacle information, in conjunction with the positioning and navigation technology of GPS and IMU, the position, speed and attitude information of the UAV is obtained in real time, and a path planning algorithm is used to formulate a flight path for the UAV. The position information of the UAV is obtained using GPS technology, and the angular velocity and acceleration of the UAV is measured using IMU (inertial measurement unit). Combined with the initial attitude information, the real-time attitude and speed information of the UAV is deduced, and the data of GPS and IMU are fused to improve the accuracy and stability of the UAV positioning and navigation. Among them, GPS provides the longitude, latitude and altitude position data of the UAV. According to the real-time updated environmental model and obstacle information, as well as the current position, speed and attitude information of the UAV, the Dijkstra algorithm is selected for path planning. In the path planning algorithm, the current position coordinates of the UAV, the target position coordinates and the speed coordinates are input. The system can update the environmental model and obstacle information in real time, analyze the steering angle and reference distance of the current position, and calculate an optimal path that avoids obstacles and meets flight safety requirements. The flight path includes the starting point, end point, intermediate nodes, flight altitude and speed parameters, and convert the flight path into control instructions for the UAV. The flight mission is performed through the control system of the UAV. During the flight, the UAV adjusts the flight attitude and speed in real time through the closed-loop control algorithm of PID control to ensure stable flight along the planned path. The accuracy and efficiency of the flight path are significantly improved through the integrated path planning technology. Combined with the actual conditions of the topography and obstacle distribution in the storage area, the optimal flight path is customized for the UAV, reducing redundancy and waste during the flight process. At the same time, the path can be dynamically adjusted according to real-time environmental changes to effectively respond to emergencies, thereby further improving flight efficiency and safety. Furthermore, the cost function expression of the optimal path is: ; ; ; in, is the cost function of the optimal path, which represents the minimization of the cost function. is the actual cost from the starting point to the current position coordinates, is the heuristic estimated cost from the current position coordinates to the target position coordinates, is the current position coordinate, is the target position coordinate, is the steering angle of the current position coordinates, is the cost weight coefficient of power consumption, is the cost weight coefficient of the steering angle, is the base distance used for scaling in the logarithmic function, As the distance between nodes increases, As the distance from the current node to the target point increases, and is a positive constant used to adjust the weights of power consumption and steering angle in the cost function; Step 4: LiDAR-based radar obstacle avoidance technology monitors obstacles around the drone in real time, obtains distance, speed and direction information of obstacles through radar beam scanning and reflection of the LiDAR, and adjusts the flight speed and direction of the drone based on the preset safety distance threshold and radar data to avoid collision with obstacles; Step 5. Based on the results of path planning, navigate and control the UAV, monitor the flight status and environmental changes of the UAV in real time, and dynamically adjust the flight plan and obstacle avoidance strategy of the UAV to ensure warehouse safety.

[0019] The second embodiment is based on the first embodiment. Figure 4 As shown, in step 4, the process of adjusting the flight speed and direction of the drone includes: During the flight of the UAV, the laser radar emits a laser beam to the target area through its internal laser diode. By using phased array technology, the laser beam is quickly scanned by adjusting the phase difference of the laser beam to cover the target area, so that the laser radar obtains three-dimensional information of the target area. The laser radar receiver receives the reflected laser signal, obtains obstacle information, and converts it into an electrical signal for subsequent signal amplification, filtering and demodulation steps to identify the position, shape and size information of the obstacle. After identifying the obstacle, the path is re-planned according to the current environmental information and the motion state of the UAV to find an optimal path from the current position to the target position, while avoiding all known obstacles, and monitoring the surrounding obstacle information in real time. The safety factor of the flight path is calculated in combination with the preset safety distance threshold to determine whether to re-plan the flight path to ensure the safety of the flight. The warning threshold is pre-set, and the safety factor is compared with the warning threshold. According to the comparison result, it is determined whether to issue a warning to remind the staff to intervene, and then adjust the path planning of the UAV. According to the result of the path planning, the UAV is navigated and controlled to achieve safe obstacle avoidance, including adjusting the flight speed and direction of the UAV to ensure that it flies stably along the planned path while avoiding all obstacles. Furthermore, the specific process of staff intervention includes: Monitor the safety factor of the flight path in real time and compare it with the preset warning threshold to analyze whether the safety factor is lower than the preset warning threshold. If the safety factor is lower than the warning threshold, it indicates that the safety of the current path is insufficient. The current path is marked and warned. Based on the triggered warning mechanism, a prompt is issued on the console to remind the staff to intervene and take action, and start the path replanning program. Enter the latest environmental information and obstacle data, and use the path planning algorithm to calculate a new safe path until the safety factor exceeds the warning threshold. The result of path planning is determined. Through real-time monitoring and dynamic adjustment of flight paths, the safety and reliability of drones in complex environments are significantly improved. Combined with the radar obstacle avoidance technology and high-precision positioning and navigation technology of lidar, the position, speed and attitude information of the drone, as well as detailed information on surrounding obstacles, can be obtained in real time, and the path can be replanned when necessary, ensuring that the drone can respond quickly when encountering unknown obstacles or environmental changes, thereby reducing the probability of accidents and improving the reliability of mission execution. The calculation expression of safety factor is: ; Among them, S is the safety factor, and the value range of S is between 0 and 1. It is a preset safety distance threshold, which is used to ensure that there is enough distance between the drone and obstacles to avoid collision. is the distance from the drone to the nearest obstacle, The smaller the value, the closer the drone is to the obstacle, and the lower the safety factor will be. v is the current speed of the drone, which affects the calculation of the safety factor. The faster the speed, the shorter the reaction time and the lower the safety factor. is the maximum speed of the drone, used to normalize the current speed to ensure that the safety factor calculation is not affected by the speed unit. Much greater than When the drone is far away from obstacles, Close to 0, the safety factor S is close to 1, indicating that it is very safe. Close to When , the safety factor S decreases rapidly, indicating that the safety is reduced. Part, when the drone speed v approaches the maximum speed When , the value of the logarithmic function increases, and the safety factor S decreases, indicating that the safety is reduced when flying at high speed; In step 5, the process of navigating and controlling the drone includes: The path planning results are converted into navigation instructions and sent to the UAV's flight control system. The UAV's flight attitude and speed are adjusted through actuators (motors, propellers and servos). The UAV's position, speed and attitude flight status information are monitored in real time to enable the UAV to fly along the predetermined path. LiDAR is used to perceive the environment around the UAV in real time, including static and dynamic obstacles, and the UAV's flight plan is dynamically adjusted according to the environmental changes monitored in real time.

[0020] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A drone safety scheduling optimization algorithm, characterized in that: The following steps are involved: Step 1: Model the storage environment based on the location, shape and size information of the shelves and mechanical equipment in the warehouse, and establish a preliminary environment model; Step 2: Use the laser radar carried by the drone to scan the environment, obtain the environmental data in the warehouse, and update the environmental model in real time based on the preliminary environmental model. The environmental data in the warehouse includes moving shelves or personnel; Step 3: Based on the real-time updated environment model and obstacle information, in conjunction with GPS and IMU positioning and navigation technology, obtain the position, speed and attitude information of the drone in real time, and use the path planning algorithm to formulate a flight path for the drone; Step 4: Obtain the distance, speed and direction information of the obstacle through the laser radar beam scanning and reflection, and adjust the flight speed and direction of the drone based on the preset safety distance threshold and radar data; Step 5: Navigate and control the UAV based on the results of path planning, monitor the flight status and environmental changes of the UAV in real time, and dynamically adjust the flight plan and obstacle avoidance strategy of the UAV.

2. The UAV safety scheduling optimization algorithm according to claim 1 is characterized by: In the step 1, the process of establishing the preliminary environment model includes: Collect detailed information on all fixed and mobile obstacles in the warehouse, including the location, shape and size of shelves, mechanical equipment and personnel passages; conduct on-site measurements and consult the warehouse layout diagram to obtain the location information of shelves and mechanical equipment, and record the shape, size and relative position relationship between shelves and mechanical equipment; The warehouse environment is divided into different areas, namely storage area, operation area and personnel passage, and the personnel passage is set as a no-fly zone; Preprocess the collected data, and for each obstacle, create a three-dimensional model of warehouse elements such as shelves, mechanical equipment, and personnel passages in the modeling tool based on the preprocessed data; Grid the warehouse space and mark the shelves, machinery and other obstacles in each grid space, and then set the height limit, obstacle type and safety distance parameters for each grid space; All constructed three-dimensional models and grid cells are integrated into a unified model to obtain a preliminary environment model.

3. The unmanned aerial vehicle safety scheduling optimization algorithm according to claim 2 is characterized in that: In the step 2, the process of updating the environment model in real time includes: Use the drone route planning software on the computer to plan the drone's flight route according to the warehouse layout and actual environment. The drone takes off according to the planned route and uses the laser radar to scan the warehouse environment during the flight. The laser radar measures the distance and obtains the three-dimensional information of the warehouse environment by emitting laser beams and receiving reflected signals. The scanned data of the warehouse environment is transmitted to the computer in real time for storage and processing, and the raw data is solved using Shuttle and gAirHawk software; In the Shuttle software, data sorting and POS solution steps are performed to obtain the POS result file, which contains the position and attitude information of the drone. In the gAirHawk software, the POS result file, Lidar original data file and photo file are imported to perform coordinate system setting, route division and point cloud computing steps to obtain 3D laser point cloud data, and then analyze the point cloud volume, slice area and height range of the 3D laser point cloud data. The solved 3D laser point cloud data is compared and fused with the preliminary environmental model, and the preliminary environmental model is updated in real time according to the position and shape information of the mobile shelves or personnel in the point cloud data to obtain an updated environmental model.

4. The unmanned aerial vehicle safety scheduling optimization algorithm according to claim 3 is characterized in that: The point cloud volume is calculated using the slicing method, and its calculation expression is: ； Where V is the point cloud volume, is the slice area of ​​the ith slice, N is the number of slices, and h is the slice spacing; The calculation expression of the slice area is: ； in, is the slice area of ​​the ith slice, m is the number of boundary points, is the boundary point of slice i; The calculation expression of the height range of the point cloud is: ； Where H is the height range of the point cloud, is the maximum height of a point in the point cloud, is the minimum height of a point in the point cloud.

5. The unmanned aerial vehicle safety scheduling optimization algorithm according to claim 4 is characterized in that: In step 3, the process of formulating the flight path of the drone includes: Use GPS technology to obtain the location information of the drone, and use IMU to measure the angular velocity and acceleration of the drone. Combined with the initial attitude information, the real-time attitude and speed information of the drone are calculated. Among them, GPS provides the longitude, latitude and altitude position data of the drone; Based on the real-time updated environment model and obstacle information, as well as the current position, speed and attitude information of the drone, the Dijkstra algorithm is selected for path planning; In the path planning algorithm, the current position coordinates of the drone, the target position coordinates, and the real-time updated environmental model and obstacle information are input, and the steering angle and reference distance of the current position are analyzed to calculate the optimal path that avoids obstacles and meets flight safety requirements. The flight path includes the starting point, end point, intermediate nodes, flight altitude and speed parameters; The flight path is converted into control instructions for the drone, and the flight mission is performed through the drone's control system. During the flight, the drone adjusts its flight attitude and speed in real time through a closed-loop control algorithm controlled by PID.

6. The unmanned aerial vehicle safety scheduling optimization algorithm according to claim 5 is characterized in that: The cost function expression of the optimal path is: ； ； ； in, is the cost function of the optimal path, is the actual cost from the starting point to the current position coordinates, is the heuristic estimated cost from the current position coordinates to the target position coordinates, is the current position coordinate, is the target position coordinate, is the steering angle of the current position coordinates, is the cost weight coefficient of power consumption, is the cost weight coefficient of the steering angle, is the reference distance.

7. The unmanned aerial vehicle safety scheduling optimization algorithm according to claim 6 is characterized by: In step 4, the process of adjusting the flight speed and direction of the drone includes: During the flight of the drone, the laser radar emits a laser beam to the target area through its internal laser diode. By using phased array technology, the phase difference of the laser beam is adjusted to achieve rapid scanning of the beam, covering the target area, so that the laser radar can obtain three-dimensional information of the target area. The laser radar receiver receives the reflected laser signal, obtains obstacle information, and converts it into an electrical signal for subsequent signal amplification, filtering and demodulation steps to identify the location, shape and size of the obstacle; After identifying obstacles, the path is replanned based on the current environmental information and the motion status of the drone to find the optimal path from the current location to the target location, while avoiding all known obstacles, and monitoring the surrounding obstacle information in real time. Combined with the preset safety distance threshold, the safety factor of the flight path is calculated to determine whether to replan the flight path; Pre-set the warning threshold, compare the safety factor with the warning threshold, and determine whether to issue a warning based on the comparison result to remind the staff to intervene and adjust the drone's path planning; Based on the results of path planning, the UAV is navigated and controlled to achieve safe obstacle avoidance, including adjusting the flight speed and direction of the UAV.

8. The unmanned aerial vehicle safety scheduling optimization algorithm according to claim 7 is characterized in that: The specific process of staff intervention includes: Monitor the safety factor of the flight path in real time and compare it with the preset warning threshold to analyze whether the safety factor is lower than the preset warning threshold; If the safety factor is lower than the warning threshold, it indicates that the safety of the current path is insufficient, and the current path is marked and a warning is issued; Based on the triggered early warning mechanism, a prompt is issued on the console to remind the staff to intervene and take action, and start the path replanning program, input the latest environmental information and obstacle data, and use the path planning algorithm to calculate a new safe path until the safety factor exceeds the early warning threshold, thus determining the result of the path planning.

9. The unmanned aerial vehicle safety scheduling optimization algorithm according to claim 8 is characterized in that: The calculation expression of the safety factor is: ； Where S is the safety factor, is the preset safety distance threshold, is the distance from the drone to the nearest obstacle, v is the current speed of the drone, is the maximum speed of the drone.

10. The drone safety scheduling optimization algorithm according to claim 9 is characterized in that: In step 5, the process of navigating and controlling the drone includes: The path planning results are converted into navigation instructions, sent to the UAV's flight control system, and the UAV's flight attitude and speed are adjusted through the actuator; Monitor the position, speed and attitude of the drone in real time to ensure that the drone flies along the planned path; Use lidar to perceive the environment around the drone in real time, including static and dynamic obstacles, and dynamically adjust the drone's flight plan based on real-time monitored environmental changes.

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