A multi-task cooperation-oriented unmanned aerial vehicle path planning method

Abstract

The application discloses a kind of unmanned plane path planning methods for multi-task cooperation, comprising the following steps: step 1, according to the attribute of task, multiple tasks are grouped, and divided into task group and single task;Step 2, according to the attribute of unmanned plane, task group and single task are allocated to unmanned plane;Step 3, global path planning is carried out to single task;Step 4, path sharing and collaborative path optimization are carried out to the same task group;Step 5, local dynamic path adjustment unmanned plane path is used in flight process.The application integrates the advanced method of allocation mechanism, path planning and energy management and multiple fields, and proposes a kind of unmanned plane multi-task cooperation path planning method suitable for complex environment.The scheme has high innovation, and can effectively solve the problem of efficient task cooperation of unmanned plane under the condition of limited energy and time.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles (UAVs), and in particular relates to a UAV path planning method for multi-task collaboration. Background Art

[0002] With the rapid development of drone technology, its application scope has gradually expanded to areas such as logistics and delivery, disaster relief, environmental monitoring, and intelligent inspection. In these application scenarios, drones must not only efficiently perform multiple tasks, but also avoid obstacles in complex and dynamic environments and complete their tasks within limited energy and time constraints. However, due to challenges faced by drones during flight, such as path conflicts, energy constraints, time pressures, and dynamic obstacle changes, the key challenge in current research is to rationally plan multi-task collaborative paths to improve mission completion efficiency.

[0003] The problem of collaborative multi-tasking UAV path planning is essentially a typical combinatorial optimization problem. It requires not only addressing the complexities of task allocation and path planning, but also considering the dynamic obstacle avoidance and energy management issues of the UAVs during mission execution. During task allocation, the UAV's energy and mission requirements must be properly matched to ensure efficient mission completion without failure due to energy shortages. Furthermore, when multiple UAVs are performing tasks simultaneously, they face the risk of path conflicts. Path planning must not only minimize energy consumption but also dynamically adjust the path to avoid collisions with other UAVs or dynamic obstacles.

[0004] Most existing path planning algorithms focus on executing a single UAV mission, with limited research on global path optimization and task grouping for collaborative multi-UAV missions. Furthermore, dynamically adjusting and optimizing multi-tasking paths for UAVs in complex environments is both a key and challenging area of ​​current technological development. Summary of the Invention

[0005] In view of this, the object of the present invention is to propose a multi-task collaborative UAV path planning method, comprising the following steps:

[0006] Step 1: Group multiple tasks according to their attributes and divide them into task groups and ungrouped tasks;

[0007] Step 2: Assign drones to task groups and ungrouped tasks based on their attributes;

[0008] Step 3: Perform global path planning for ungrouped tasks;

[0009] Step 4: Path sharing and collaborative path optimization are performed for the same task group;

[0010] Step 5: Use local dynamic path to adjust the drone path during flight.

[0011] Specifically, multiple tasks are grouped according to task attributes, including grouping tasks according to criteria such as the geographical location of the tasks and time windows, and tasks with similar locations and overlapping time windows are divided into the same group, so that the drone can efficiently complete the tasks through collaborative optimization. The task division criteria include spatial proximity and time window overlap. Spatial proximity means that the geographical distance between tasks is close, which is conducive to the drone completing multiple tasks through one flight. Time window overlap means that the time requirements of the tasks are similar, ensuring that the drone can perform multiple tasks in the same time period. Tasks are grouped by clustering algorithm, and each task group Represents a set of tasks with geographical and temporal proximity.

[0012] Specifically, the allocation of drones to task groups and ungrouped tasks is performed using an auction mechanism based on the attributes of the drones. Based on the capabilities of the drones and the attributes of the tasks or task groups, suitable drones are matched to perform specific tasks or task groups. The allocation of drones is based on the following factors:

[0013] Energy limitation: Based on energy consumption estimation, ensure that the UAV has enough energy to complete all tasks within the task group;

[0014] Mission load: Consider the payload capacity of the drone. If the mission load in the mission group is heavy, choose a drone with a strong payload capacity.

[0015] Task allocation formula: Use auction mechanism to allocate tasks and select the most suitable drone to perform the task group. The task allocation formula is:

[0016]

[0017] in, It's a drone , It is a task force The tasks in It's a drone Execute the task The total cost includes energy consumption and time cost.

[0018] Furthermore, the energy consumption estimation includes the following steps:

[0019] The energy consumption includes flight energy consumption and mission execution energy consumption;

[0020] The flight energy consumption calculation formula is: ,in, Indicates flight power in watts, estimated by the flight speed, payload, and altitude of the drone; Indicates flight time in seconds, calculated by flight distance and flight speed;

[0021] The energy consumption of the mission execution depends on the type of mission, mission duration and the actions that the UAV needs to perform during the mission: , It is the power when the task is executed, which is related to the task type; It is the time for task execution, which is determined by the complexity and execution time of the task;

[0022] The energy consumption is: ; is the drag coefficient of the drone; is the cross-sectional area of ​​the drone; is the air density; is the flight speed, is the distance the drone flew; is the total mass of the UAV and its payload; is the acceleration due to gravity; is the propeller efficiency of the drone.

[0023] Specifically, the global path planning for ungrouped tasks includes the following steps:

[0024] Get the starting point and end point ;

[0025] Set the obstacles in the environment as polygon collections , each obstacle By its vertex set express;

[0026] Initial path search, assuming there are no obstacles, the drone will follow the path from the starting point To the end A straight line is defined as: ;

[0027] Obstacle Detection: Checking the Path Whether to gather with obstacles If there is no intersection, the path This is the optimal path, no further planning is required; if there is an intersection, proceed to the next step;

[0028] Find guidance points and identify intersecting obstacles: For all paths The set of intersecting obstacles;

[0029] Calculate the feasible vertices of the obstacle: For each intersecting obstacle , extract the vertex set of obstacles , and define the feasible vertex set : , that is, the vertex Is the starting point The points where the line does not intersect any obstacles;

[0030] Select the optimal guidance point, for each feasible vertex of the obstacle , select an optimal guidance point , so that from the starting point arrive The path and To the end The paths do not intersect with obstacles. The selection criteria for the optimal guiding point can be the point with the shortest distance to the target point: ,in, is the vertex To the end The Euclidean distance of

[0031] Path update, find the optimal guidance point Finally, the global path is divided into two segments: and ,path is the path from the starting point to the guidance point, the path It is the path from the guiding point to the destination;

[0032] Check if the updated path intersects with other obstacles and check the path and Whether it intersects with other obstacles. If so, continue to select new guidance points until the path does not intersect with any obstacles.

[0033] When the final generated path When it does not intersect with any obstacles, the path is output as the optimal path, and m represents the number of obstacles.

[0034] Furthermore, the path sharing and collaborative path optimization for the same task group includes the following steps:

[0035] For tasks in the same task group, a path sharing mechanism is adopted. The principle of path sharing is that the UAV can maximize the coverage of the task points in the task group during the flight process to form a coherent path;

[0036] The goal of path sharing is to minimize the total path length:

[0037]

[0038] in, is the total path length between task points within the task group, It's a mission point location, It's a mission point and The distance between is the number of task points in the task group;

[0039] Based on the path sharing in the task group, the optimization algorithm of the traveling salesman problem is used to plan the optimal path of the UAV;

[0040] When the UAVs perform a task group, they use local dynamic path adjustment to ensure that they avoid collisions with other UAVs or dynamic obstacles during the collaborative flight.

[0041] Preferably, the method of adjusting the drone path using a local dynamic path during flight includes the following:

[0042] Step 1: Use the Markov decision process to predict the future trajectory of the obstacle;

[0043] State definition: Define the current state of the obstacle as , that is, the obstacle at time Location ,speed and acceleration ;

[0044] State transition model: The future state of the obstacle is represented by a probability model, assuming that the obstacle is The status is , its state transition probability Predictions are made using the following model:

[0045]

[0046] in is the state transfer function, based on the current velocity, acceleration and time step , predict future positions and states;

[0047] Step 2: By iterating the state transition model multiple times, the probability trajectory of the obstacle is obtained and the possible location of the obstacle in the future is predicted;

[0048] Step 3: Collision risk assessment: Based on the probability distribution of the obstacle's motion trajectory, the drone assesses the risk of collision with the dynamic obstacle.

[0049] Assume that the current position of the drone is , calculate the future time according to the speed and planned path of the drone Drone location , the collision risk is expressed as the probability that the obstacle and the UAV are at the same spatial position:

[0050]

[0051] Quantify the collision risk by calculating the probability of overlap between the drone and the obstacle in the future time period;

[0052] Safety distance detection: set a minimum safety distance When the distance between the drone and the obstacle is less than this value, it is considered that there is a collision risk. The collision judgment formula is: ;

[0053] Step 4: Local path adjustment. When the system detects a potential collision risk, the drone will dynamically adjust the current local path to avoid obstacles. The adjustment steps are as follows:

[0054] Path deviation calculation: Based on the probability trajectory of the obstacle and the collision risk assessment, the direction and distance that the drone needs to deviate are calculated; the direction of the path deviation is the direction away from the obstacle, and the deviation is To meet the minimum distance for safety distance;

[0055] Path update: The new position of the drone is:

[0056] Step 5: Real-time dynamic adjustment and feedback: During flight, the drone continuously monitors environmental changes and the trajectory of obstacles, updates the collision risk assessment model in real time, and recalculates the local path when a significant change in the obstacle trajectory is detected.

[0057] Each time step Repeat steps 3 and 4 to ensure the drone can fly safely in a dynamic environment.

[0058] The beneficial effects of the present invention are as follows: The present invention takes into account the allocation mechanism of comprehensive energy and time constraints: Based on the traditional allocation mechanism, this scheme adds energy and time constraints to ensure that the task allocation of drones not only optimizes the task completion time, but also takes into account its energy limitations; a path planning algorithm based on a multi-guidance point strategy is proposed, which can quickly handle obstacle avoidance problems in large-scale environments and significantly reduce the amount of calculation; combined with a real-time feedback mechanism, the system can adaptively adjust task allocation and path planning to ensure that drones can still complete tasks efficiently in emergency situations, and propose a collaborative optimization strategy for tasks and paths. Through task grouping and path sharing, conflicts between drones are reduced and the overall efficiency of the task is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1Overall flowchart of the video space conversion method. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions, and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the embodiments described herein are merely some, rather than all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0061] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] This embodiment proposes a multi-task collaborative UAV path planning method, including the following steps:

[0063] Step 1: Group multiple tasks according to their attributes and divide them into task groups and ungrouped tasks;

[0064] Step 2: Assign drones to task groups and ungrouped tasks based on their attributes;

[0065] Step 3: Perform global path planning for ungrouped tasks;

[0066] Step 4: Path sharing and collaborative path optimization are performed for the same task group;

[0067] Step 5: Use local dynamic path to adjust the drone path during flight.

[0068] Specifically, multiple tasks are grouped according to task attributes, including grouping tasks according to criteria such as the geographical location of the tasks and time windows, and tasks with similar locations and overlapping time windows are divided into the same group, so that the drone can efficiently complete the tasks through collaborative optimization. The task division criteria include spatial proximity and time window overlap. Spatial proximity means that the geographical distance between tasks is close, which is conducive to the drone completing multiple tasks through one flight. Time window overlap means that the time requirements of the tasks are similar, ensuring that the drone can perform multiple tasks in the same time period. Tasks are grouped by clustering algorithm, and each task group Represents a set of tasks with geographical and temporal proximity.

[0069] Specifically, the allocation of drones to task groups and ungrouped tasks is performed using an auction mechanism based on the attributes of the drones. Based on the capabilities of the drones and the attributes of the tasks or task groups, suitable drones are matched to perform specific tasks or task groups. The allocation of drones is based on the following factors:

[0070] Energy limitation: Based on energy consumption estimation, ensure that the UAV has enough energy to complete all tasks within the task group;

[0071] Mission load: Consider the payload capacity of the drone. If the mission load in the mission group is heavy, choose a drone with a strong payload capacity.

[0072] Task allocation formula: Use auction mechanism to allocate tasks and select the most suitable drone to perform the task group. The task allocation formula is:

[0073]

[0074] in, It's a drone , It is a task force The tasks in It's a drone Execute the task The total cost includes energy consumption and time cost.

[0075] Furthermore, the energy consumption estimation includes the following steps:

[0076] The energy consumption includes flight energy consumption and mission execution energy consumption;

[0077] The flight energy consumption calculation formula is: ,in, Indicates flight power in watts, estimated by the flight speed, payload, and altitude of the drone; Indicates flight time in seconds, calculated by flight distance and flight speed;

[0078] The energy consumption of the mission execution depends on the type of mission, mission duration and the actions that the UAV needs to perform during the mission: , It is the power when the task is executed, which is related to the task type; It is the time for task execution, which is determined by the complexity and execution time of the task;

[0079] The energy consumption is: ; is the drag coefficient of the drone; is the cross-sectional area of ​​the drone; is the air density; is the flight speed, is the distance the drone flew; is the total mass of the UAV and its payload; is the acceleration due to gravity; is the propeller efficiency of the drone.

[0080] The flight power is estimated based on the drone's speed, payload, air resistance, and other parameters. The flight time is estimated based on the path length and speed. The energy consumption of the task is estimated based on the task type and execution time. The flight energy consumption is added to the task energy consumption to obtain the total energy consumption of the drone for the task. This energy consumption estimation process allows the system to dynamically calculate the energy cost of each drone during task allocation, ensuring that the drone can efficiently complete the task within its energy capacity.

[0081] Specifically, the global path planning for ungrouped tasks includes the following steps:

[0082] Get the starting point and end point ;

[0083] Set the obstacles in the environment as polygon collections , each obstacle By its vertex set express;

[0084] Initial path search, assuming there are no obstacles, the drone will follow the path from the starting point To the end A straight line is defined as: ;

[0085] Obstacle Detection: Checking the Path Whether to gather with obstacles If there is no intersection, the path This is the optimal path, no further planning is required; if there is an intersection, proceed to the next step;

[0086] Find guidance points and identify intersecting obstacles: For all paths The set of intersecting obstacles;

[0087] Calculate the feasible vertices of the obstacle: For each intersecting obstacle , extract the vertex set of obstacles , and define the feasible vertex set : , that is, the vertex Is the starting point The points where the line does not intersect any obstacles;

[0088] Select the optimal guidance point, for each feasible vertex of the obstacle , select an optimal guidance point such that the path from the start point to and the path from to the end point do not intersect with the obstacles, the selection criterion of the optimal guide point can be the point with the shortest distance to the target point: wherein, is the Euclidean distance from the vertex to the end point ;

[0089] After the path is updated, the optimal guide point is found, the global path is divided into two segments: and , the path is from the start point to the guide point, and the path is from the guide point to the end point;

[0090] It is detected whether the updated path intersects with other obstacles, and it is checked whether the paths and intersect with other obstacles, if they intersect, a new guide point is selected until the path does not intersect with any obstacle;

[0091] When the finally generated path does not intersect with any obstacle, the path is output as the optimal path, and m represents the number of obstacles.

[0092] Further, the path sharing and collaborative path optimization for the same task group include the following steps:

[0093] For the tasks in the same task group, a path sharing mechanism is adopted, and the principle of path sharing is that the unmanned aerial vehicle can maximize the coverage of the task points in the task group during flight to form a continuous path;

[0094] The goal of path sharing is to minimize the total path length:

[0095]

[0096] wherein, is the total path length between the task points in the task group, is the position of the task point , is the distance between the task points and , and is the number of task points in the task group;

[0097] Based on the path sharing in the task group, an optimization algorithm of the traveling salesman problem is used to plan the optimal path of the unmanned aerial vehicle;

[0098] When the UAVs perform a task group, they use local dynamic path adjustment to ensure that they avoid collisions with other UAVs or dynamic obstacles during the collaborative flight.

[0099] Preferably, the method of adjusting the drone path using a local dynamic path during flight includes the following:

[0100] Step 1: Use the Markov decision process to predict the future trajectory of the obstacle;

[0101] State definition: Define the current state of the obstacle as , that is, the obstacle at time Location ,speed and acceleration ;

[0102] State transition model: The future state of the obstacle is represented by a probability model, assuming that the obstacle is The status is , its state transition probability Predictions are made using the following model:

[0103]

[0104] in is the state transfer function, based on the current velocity, acceleration and time step , predict future positions and states;

[0105] Step 2: By iterating the state transition model multiple times, the probability trajectory of the obstacle is obtained and the possible location of the obstacle in the future is predicted;

[0106] Step 3: Collision risk assessment: Based on the probability distribution of the obstacle's motion trajectory, the drone assesses the risk of collision with the dynamic obstacle.

[0107] Assume that the current position of the drone is , calculate the future time according to the speed and planned path of the drone Drone location , the collision risk is expressed as the probability that the obstacle and the UAV are at the same spatial position:

[0108]

[0109] Quantify the collision risk by calculating the probability of overlap between the drone and the obstacle in the future time period;

[0110] Safety distance detection: set a minimum safety distance When the distance between the drone and the obstacle is less than this value, it is considered that there is a collision risk. The collision judgment formula is: ;

[0111] Step 4: Local path adjustment. When the system detects a potential collision risk, the drone will dynamically adjust the current local path to avoid obstacles. The adjustment steps are as follows:

[0112] Path deviation calculation: Based on the probability trajectory of the obstacle and the collision risk assessment, the direction and distance that the drone needs to deviate are calculated; the direction of the path deviation is the direction away from the obstacle, and the deviation is To meet the minimum distance for safety distance;

[0113] Path update: The new position of the drone is:

[0114] Step 5: Real-time dynamic adjustment and feedback: During flight, the drone continuously monitors environmental changes and the trajectory of obstacles, updates the collision risk assessment model in real time, and recalculates the local path when a significant change in the obstacle trajectory is detected.

[0115] Each time step Repeat steps 3 and 4 to ensure the drone can fly safely in a dynamic environment.

[0116] The advantages and benefits of this invention lie in: This technical solution integrates advanced methods from multiple fields, including allocation mechanisms, path planning, and energy management, to propose a multi-task collaborative path planning method for UAVs in complex environments. This highly innovative solution effectively addresses the challenge of efficient UAV task collaboration under energy and time constraints.

[0117] As used herein, the word "preferred" is intended to serve as an example, instance, or illustration. Any aspect or design described herein as "preferred" is not necessarily to be construed as advantageous over other aspects or designs. Rather, the use of the word "preferred" is intended to present concepts in a concrete manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X employs A or B" is intended to mean any of the naturally inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then "X employs A or B" is satisfied in any of the foregoing examples.

[0118] Moreover, although the present disclosure has been illustrated and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based on the foregoing description and accompanying drawings. The present disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, blocks, etc.) the terms (including a reference to a "means") used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the present disclosure. In addition, while a particular feature of the disclosure can have been disclosed with respect to only one of several implementations, such feature can be combined with one or other features of the other implementations as can be desired and advantageous for any given or particular application. Furthermore, structures and functionality presented as discrete components in the exemplary configurations can be implemented as a combined structure or component. Such modifications, changes, variations, alternatives, combinations, sub-combinations, and equivalents are therefore contemplated as being within the scope of the present disclosure. It will be appreciated that the specified systems or devices referred to herein can be embodied in multiple arrangements and aspects. Furthermore, although the term "comprise" or variations such as "comprises an", "comprises", "comprising", "includes", "including" or "has" or variations such as "has an", "has" or "having" are used herein in order to provide an open-ended definition (i.e., a definition which does not preclude

[0119] The various functional units in the embodiments of the present application can be integrated in one processing module, or each unit can exist physically, or a plurality of or more units can be integrated in one module. The integrated module can be realized in the form of hardware, or in the form of a software functional module. If the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium. The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. The above-mentioned devices or systems can execute the storage method in the corresponding method embodiments.

[0120] In summary, the above embodiments are one embodiment of the present application, but the embodiments of the present application are not limited by the above embodiments, and any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principle of the present application are equivalent replacement methods, and are included in the protection scope of the present application.

Claims

1. A multi-task collaborative UAV path planning method, characterized in that: The steps include: Step 1: Group multiple tasks according to their attributes and divide them into task groups and ungrouped tasks; Step 2: Assign drones to task groups and ungrouped tasks based on their attributes; Step 3: Perform global path planning for ungrouped tasks; Step 4: Path sharing and collaborative path optimization are performed for the same task group; Step 5: Use local dynamic path to adjust the drone path during flight; The allocation of drones to task groups and ungrouped tasks is performed using an auction mechanism. Based on the capabilities of the drones and the attributes of the task or task group, suitable drones are matched to perform specific tasks or task groups. Drone allocation is based on the following factors: Energy limitation: Based on energy consumption estimation, ensure that the UAV has enough energy to complete all tasks within the task group; Mission load: Consider the payload capacity of the drone. If the mission load in the mission group is heavy, choose a drone with a strong payload capacity. Task allocation formula: Use auction mechanism to allocate tasks and select the most suitable drone to perform the task group. The task allocation formula is: in, It's a drone , It is a task force The tasks in It's a drone Execute the task The total cost includes energy consumption and time cost.

2. A multi-task collaborative UAV path planning method according to claim 1, characterized in that: Multiple tasks are grouped according to task attributes, including grouping tasks according to their geographical location and time window standards. Tasks with similar locations and overlapping time windows are divided into the same group so that drones can complete tasks efficiently through collaborative optimization. The task division standards include spatial proximity and time window overlap. Spatial proximity means that the geographical distance between tasks is close, which is conducive to drones completing multiple tasks in one flight. Time window overlap means that the time requirements of tasks are similar, ensuring that drones can perform multiple tasks in the same period. Tasks are grouped by clustering algorithms, and each task group Represents a set of tasks with geographical and temporal proximity.

3. The multi-task collaborative UAV path planning method according to claim 2, characterized in that: The energy consumption estimation method includes the following steps: The energy consumption includes flight energy consumption and mission execution energy consumption; The flight energy consumption calculation formula is: ,in, Indicates flight power in watts, estimated by the flight speed, payload, and altitude of the drone; Indicates flight time in seconds, calculated by flight distance and flight speed; The energy consumption of the mission execution depends on the type of mission, mission duration and the actions that the UAV needs to perform during the mission: , It is the power when the task is executed, which is related to the task type; It is the time for task execution, which is determined by the complexity and execution time of the task; The energy consumption is: ; is the drag coefficient of the drone; is the cross-sectional area of ​​the drone; is the air density; is the flight speed, is the distance the drone flew; is the total mass of the UAV and its payload; is the acceleration due to gravity; is the propeller efficiency of the drone.

4. The multi-task collaborative UAV path planning method according to claim 3, characterized in that: The global path planning for the ungrouped tasks includes the following steps: Get the starting point and end point ; Set the obstacles in the environment as polygon collections , each obstacle By its vertex set express; Initial path search, assuming there are no obstacles, the drone will follow the path from the starting point To the end A straight line is defined as: ; Obstacle Detection: Checking the Path Whether to gather with obstacles If there is no intersection, the path This is the optimal path, no further planning is required; if there is an intersection, proceed to the next step; Find guidance points and identify intersecting obstacles: For all paths The set of intersecting obstacles; Calculate the feasible vertices of the obstacle: For each intersecting obstacle , extract the vertex set of obstacles , and define the feasible vertex set : , that is, the vertex Is the starting point The points where the line does not intersect any obstacles; Select the optimal guidance point, for each feasible vertex of the obstacle , select an optimal guidance point , so that from the starting point arrive The path and To the end The paths do not intersect with obstacles. The selection criteria for the optimal guiding point is the point with the shortest distance to the target point: ,in, is the vertex To the end The Euclidean distance of Path update, find the optimal guidance point Finally, the global path is divided into two segments: and ,path is the path from the starting point to the guidance point, the path It is the path from the guiding point to the destination; Check if the updated path intersects with other obstacles and check the path and Whether it intersects with other obstacles. If so, continue to select new guidance points until the path does not intersect with any obstacles. When the final generated path When it does not intersect with any obstacles, the path is output as the optimal path, and m represents the number of obstacles.

5. The multi-task collaborative UAV path planning method according to claim 4, characterized in that: The path sharing and collaborative path optimization for the same task group includes the following steps: For tasks in the same task group, a path sharing mechanism is adopted. The principle of path sharing is that the UAV can maximize the coverage of the task points in the task group during the flight process to form a coherent path; The goal of path sharing is to minimize the total path length: in, is the total path length between task points within the task group, It's a mission point location, It's a mission point and The distance between is the number of task points in the task group; Based on the path sharing in the task group, the optimization algorithm of the traveling salesman problem is used to plan the optimal path of the UAV; When the UAVs perform a task group, they use local dynamic path adjustment to ensure that they avoid collisions with other UAVs or dynamic obstacles during the collaborative flight.

6. The multi-task collaborative UAV path planning method according to claim 5, characterized in that: The local dynamic path is used to adjust the drone path during the flight process, including the following: Step 501, using a Markov decision process to predict the future motion trajectory of the obstacle; State definition: Define the current state of the obstacle as , that is, the obstacle at time Location ,speed and acceleration ; State transition model: The future state of the obstacle is represented by a probability model, assuming that the obstacle is The status is , its state transition probability Predictions are made using the following model: in is the state transfer function, based on the current velocity, acceleration and time step , predict future positions and states; Step 502: Obtain the probabilistic trajectory of the obstacle by multiple iterations of the state transition model, and predict the possible location of the obstacle in the future. Step 503: Collision risk assessment: Based on the probability distribution of the obstacle's motion trajectory, the drone assesses the risk of collision with the dynamic obstacle. Assume that the current position of the drone is , calculate the future time according to the speed and planned path of the drone Drone location , the collision risk is expressed as the probability that the obstacle and the UAV are at the same spatial position: Quantify the collision risk by calculating the probability of overlap between the drone and the obstacle in the future time period; Safety distance detection: set a minimum safety distance When the distance between the drone and the obstacle is less than the minimum safety distance, it is considered that there is a collision risk. The collision judgment formula is: ; Step 504: Local path adjustment. When the system detects a potential collision risk, the drone will dynamically adjust the current local path to avoid obstacles. The adjustment steps are as follows: Path deviation calculation: Based on the probability trajectory of the obstacle and the collision risk assessment, the direction and distance that the drone needs to deviate are calculated; the direction of the path deviation is the direction away from the obstacle, and the deviation is To meet the minimum distance for safety distance; Path update: The new position of the drone is: Step 505: Real-time dynamic adjustment and feedback. During flight, the UAV continuously monitors environmental changes and the trajectory of obstacles, updates the collision risk assessment model in real time, and recalculates the local path when a significant change in the trajectory of an obstacle is detected. Each time step Repeat steps 503 and 504 to ensure that the drone can fly safely in a dynamic environment.

Images