Multi-unmanned aerial vehicle cooperative dynamic path planning method

Abstract

The invention discloses an unmanned aerial vehicle multi-aircraft cooperative dynamic path planning method, which comprises the steps of 1, establishing a multi-aircraft cooperative flight system model comprising an unmanned aerial vehicle individual model and a flight environment model, 2, collecting real-time environment information, 3, carrying out distributed cooperative decision making and path planning, 4, carrying out dynamic adjustment and anti-collision control, and 5, carrying out unmanned aerial vehicle multi-aircraft cooperative dynamic path planning. 5, multi-unmanned aerial vehicle cooperative flight control is carried out, and a flight control instruction of each unmanned aerial vehicle is generated according to the adjusted path; the multi-unmanned aerial vehicle cooperative flight system has the beneficial effects that the multi-unmanned aerial vehicle cooperative flight system model is based on a distributed control architecture, model integration is realized through three steps of data acquisition, information fusion and strategy generation, real-time data sharing among unmanned aerial vehicles is realized, when a certain unmanned aerial vehicle detects a dynamic obstacle, collective path re-planning can be triggered, and the dynamic obstacle can be detected by the unmanned aerial vehicle. And the formation stability is kept while multiple machines avoid obstacles.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a method for collaborative dynamic path planning of multiple UAVs. Background Art

[0002] With the continuous development of drone technology, drones are being used more and more widely in military, civilian and other fields. In many tasks, multiple drones need to work together to complete them, such as regional reconnaissance, target tracking, cargo transportation, etc. When multiple drones fly together, path planning is one of the key technologies, which directly affects the efficiency of mission completion and the flight safety of drones. Existing drone path planning methods can achieve multi-drone collaborative flight to a certain extent, but there are still some shortcomings. For example, for complex and changeable flight environments, such as dynamic obstacles and strong airflows, the adaptability of existing methods is weak, which can easily lead to unreasonable path planning and even drone collisions. In addition, the existing methods still need to be improved in terms of the collaborative stability and coordination reliability between drones, and it is difficult to ensure that multiple drones maintain a good collaborative state during long-term flight. Summary of the Invention

[0003] The purpose of the present invention is to provide a multi-drone collaborative dynamic path planning method in order to solve the problems that existing drones are unable to cope with complex and changeable flight environments, have weak adaptability, and have low collaborative stability and cooperation reliability.

[0004] The purpose of the present invention can be achieved by the following technical solution: A method for multi-drone collaborative dynamic path planning, comprising the following steps:

[0005] Step 1: Establish a multi-machine collaborative flight system model that includes UAV individual models and flight environment models;

[0006] Step 2: Real-time environmental information collection. Each drone collects real-time flight environment information through its onboard sensors, including the location, shape, and motion status of obstacles, as well as environmental meteorological data. The collected environmental information is sent to the ground control center via a wireless communication module or distributedly transmitted between drones to form global environmental information. This global environmental information is then fused and processed to remove noise and redundant information, resulting in accurate and complete environmental status data.

[0007] Step 3: Distributed collaborative decision-making and path planning. Using a distributed collaborative algorithm, each drone interacts with other drones and makes collaborative decisions based on its own location, status, and global environment information.

[0008] Step 4: Dynamic Adjustment and Collision Avoidance Control: During flight, the system monitors environmental changes and the flight status of drones in real time. When a new obstacle is detected or the distance between drones falls below a safety threshold, the dynamic adjustment mechanism is triggered. Each drone uses a local obstacle avoidance algorithm to adjust its current flight path in real time based on the latest environmental information and the position and speed of neighboring drones.

[0009] Step 5: Multi-drone collaborative flight control. Based on the adjusted path, flight control instructions are generated for each drone, including flight speed, direction, and altitude. The drone's attitude and movement are precisely controlled through the control system. During the flight, the actual flight status of the drone is continuously fed back and compared with the expected status. Closed-loop control is performed to ensure the accuracy and stability of multi-drone collaborative flight.

[0010] Furthermore, the individual model of the drone is:

[0011]

[0012] Represents the position of the drone at time t, p(t) is equal to the initial position p0 plus the integral of the velocity v(τ) from time 0 to time t over time. That is, the position is the sum of the initial position and the cumulative change of the velocity over time, reflecting the integral relationship between velocity and position;

[0013] The velocity v(t) of the UAV at time t is equal to the initial velocity v0 plus the integral of the acceleration a(τ) from time 0 to time t over time. That is, the velocity is the sum of the initial velocity and the cumulative change of the acceleration over time, reflecting the integral relationship between acceleration and velocity.

[0014] a(t)≤a max , which means that during the movement of the drone, the acceleration a(t) at any time t cannot exceed the set maximum acceleration a max , which is the constraint condition for the acceleration of the UAV during movement. At the same time, according to the maximum angular velocity ω of the UAV max and linear velocity v, through the formula Calculate the minimum turning radius to constrain the trajectory changes of the drone.

[0015] Furthermore, the flight environment model is: a grid method or a Voronoi diagram method is used to digitally model the flight area, and for dynamic obstacles, a position prediction model is established through Kalman filtering:

[0016]

[0017] in, is the state estimate at time k, F kis the state transfer matrix, B k is the control matrix, u k is the control vector, K k is the Kalman gain, z k is the measured value, H k is the measurement matrix;

[0018] Introducing the environmental parameter layer, the following formula is used to calculate the actual speed of the drone:

[0019]

[0020] Among them, v drone represents the flight speed set by the drone itself, θ is the flight direction angle of the drone itself, and v wind is the airflow velocity in the current environment, θ wind It represents the wind direction, that is, the direction angle of the airflow. By superimposing the UAV's own velocity vector with the ambient airflow velocity vector, the actual movement speed of the UAV in a complex meteorological environment is calculated.

[0021] Furthermore, the multi-machine cooperative flight system model is based on the integration of the UAV individual model and the flight environment model, and the formula is:

[0022] Cost = α·Individual energy consumption + β·Obstacle avoidance distance + γ·Task timeliness

[0023] Among them, α, β, and γ are weight coefficients, which respectively measure the relative importance of individual energy consumption, obstacle avoidance distance, and mission timeliness in the total cost. This formula quantifies the above-mentioned individual energy consumption, obstacle avoidance distance, and mission timeliness into a single cost indicator through weighted summation, providing a decision-making basis for the dynamic path planning of multi-UAV collaborative operation, helping to optimize the overall path plan and balance energy consumption, safety, and mission efficiency.

[0024] Furthermore, the formula for the individual energy consumption is expressed as It represents the integration operation of the motor power P from the starting time 0 to the current time τ. By integrating the power over time, the total work done by the drone motor during the flight time from 0 to τ is obtained. It quantifies the total amount of energy consumed by the motor to propel the drone during this time period.

[0025] Furthermore, the obstacle avoidance distance is calculated as the current position p(t) of the drone and the predicted position of the obstacle The shortest distance.

[0026] Furthermore, the task timeliness is calculated based on the task's start and end time, as well as the current time. The remaining available time is then estimated based on the drone's current position p(t), velocity v(t), and the task's target location. The difference between the remaining available time and the estimated time required to complete the task is used as a measure of task timeliness. A smaller difference indicates a more urgent task.

[0027] Furthermore, the local obstacle avoidance algorithm adopts a method that combines artificial potential field method with fuzzy logic to construct gravitational potential field and repulsive potential field. The gravitational potential field guides the drone to fly to the target point, and the repulsive potential field enables the drone to avoid obstacles and other drones. Combined with the fuzzy logic controller, the weights of gravity and repulsion are adjusted in real time to adapt to different flight environments and drone states, ensuring that there will be no collision between drones while maintaining the stability of multi-drone collaborative flight.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The multi-drone collaborative flight system model is based on a distributed control architecture. It integrates models through three steps: data collection, information fusion, and strategy generation, enabling real-time data sharing between drones. When a drone detects a dynamic obstacle, it triggers collective path replanning to ensure that multiple drones maintain a stable formation while avoiding obstacles.

[0030] 2. Combining the artificial potential field method with fuzzy logic, the fuzzy logic controller is used to adjust the weights of attraction and repulsion in real time, overcoming the shortcomings of the traditional artificial potential field method, which has fixed weights and is difficult to adapt to complex environments. This ensures that drones can flexibly and reliably avoid obstacles in dynamic environments and maintain a safe distance and collaborative stability among multiple drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0032] Figure 1 This is a flow chart of a method for collaborative dynamic path planning of multiple unmanned aerial vehicles (UAVs) according to the present invention. DETAILED DESCRIPTION

[0033] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described 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 without making any creative efforts shall fall within the scope of protection of the present invention.

[0034] See also Figure 1 As shown, a method for multi-UAV collaborative dynamic path planning includes the following steps:

[0035] A method for multi-UAV collaborative dynamic path planning includes the following steps:

[0036] Step 1: Establish a multi-machine collaborative flight system model that includes UAV individual models and flight environment models;

[0037] Step 2: Real-time environmental information collection. Each drone collects real-time flight environment information through its onboard sensors, including the location, shape, and motion status of obstacles, as well as environmental meteorological data. The collected environmental information is sent to the ground control center via a wireless communication module or distributedly transmitted between drones to form global environmental information. This global environmental information is then fused and processed to remove noise and redundant information, resulting in accurate and complete environmental status data.

[0038] Step 3: Distributed collaborative decision-making and path planning. Using a distributed collaborative algorithm, each drone interacts with other drones and makes collaborative decisions based on its own location, status, and global environment information.

[0039] Step 4: Dynamic Adjustment and Collision Avoidance Control: During flight, the system monitors environmental changes and the flight status of drones in real time. When a new obstacle is detected or the distance between drones falls below a safety threshold, the dynamic adjustment mechanism is triggered. Each drone uses a local obstacle avoidance algorithm to adjust its current flight path in real time based on the latest environmental information and the position and speed of neighboring drones.

[0040] Step 5: Multi-drone collaborative flight control: Based on the adjusted path, flight control instructions are generated for each drone, including flight speed, direction, and altitude. The control system precisely controls the drone's attitude and movement. During flight, the drone's actual flight status is continuously fed back and compared with the expected status, performing closed-loop control to ensure the accuracy and stability of multi-drone collaborative flight.

[0041] By constructing a multi-machine collaborative flight system model that includes individual UAV models, flight environment models, and collaborative interaction models, we can achieve comprehensive modeling of UAV dynamic characteristics, complex environmental factors, and multi-machine collaborative mechanisms. On this basis, combined with real-time environmental information collection and processing, distributed collaborative decision-making, dynamic adjustment, and anti-collision control, we can ultimately complete multi-machine collaborative flight control, which has the characteristics of strong environmental adaptability, high collaborative efficiency, excellent optimization effect, and outstanding robustness.

[0042] In this solution, the individual drone model is:

[0043]

[0044] Represents the position of the drone at time t, p(t) is equal to the initial position p0 plus the integral of the velocity v(τ) from time 0 to time t over time. That is, the position is the sum of the initial position and the cumulative change of the velocity over time, reflecting the integral relationship between velocity and position;

[0045] The velocity v(t) of the UAV at time t is equal to the initial velocity v0 plus the integral of the acceleration a(τ) from time 0 to time t over time. That is, the velocity is the sum of the initial velocity and the cumulative change of the acceleration over time, reflecting the integral relationship between acceleration and velocity.

[0046] a(t)≤a max , which means that during the movement of the drone, the acceleration a(t) at any time t cannot exceed the set maximum acceleration a max , which is the constraint condition for the acceleration of the UAV during movement. At the same time, according to the maximum angular velocity ω of the UAV max and linear velocity v, through the formula Calculate the minimum turning radius to constrain the trajectory changes of the drone.

[0047] The flight environment model is to digitally model the flight area using a grid method or a Voronoi diagram method, and to establish a position prediction model for dynamic obstacles using a Kalman filter:

[0048]

[0049] in, is the state estimate at time k, F k is the state transfer matrix, B k is the control matrix, u k is the control vector, K k is the Kalman gain, z k is the measured value, H k is the measurement matrix;

[0050] Introducing the environmental parameter layer, the following formula is used to calculate the actual speed of the drone:

[0051]

[0052] Among them, v drone represents the flight speed set by the drone itself, θ is the flight direction angle of the drone itself, and v wind is the airflow velocity in the current environment, θ wind It represents the wind direction, that is, the direction angle of the airflow. By superimposing the UAV's own velocity vector with the ambient airflow velocity vector, the actual movement speed of the UAV in a complex meteorological environment is calculated.

[0053] In this solution, the multi-machine cooperative flight system model is based on the integration of UAV individual models and flight environment models, and the formula is:

[0054] Cost = α·Individual energy consumption + β·Obstacle avoidance distance + γ·Task timeliness

[0055] Among them, α, β, and γ are weight coefficients, which respectively measure the relative importance of individual energy consumption, obstacle avoidance distance, and mission timeliness in the total cost. This formula quantifies the above individual energy consumption, obstacle avoidance distance, and mission timeliness into a single cost indicator through weighted summation. This provides a decision-making basis for multi-UAV collaborative dynamic path planning, helps optimize the overall path plan, and balances energy consumption, safety, and mission efficiency.

[0056] The formula for the individual energy consumption is expressed as It represents the integration operation of the motor power P in the time period from the starting time 0 to the current time τ. By integrating the power over time, the total work done by the drone motor during the flight time from 0 to τ is obtained. It quantifies the total energy consumed by the motor to propel the drone to fly during this time period. The obstacle avoidance distance is calculated as the current position of the drone p(t) and the predicted position of the obstacle. The task timeliness is calculated by calculating the remaining available time based on the task start time, deadline, and current time. The time required to complete the task is predicted by combining the current position p(t), speed v(t), and the task target position of the UAV. The difference between the remaining available time and the predicted time required to complete the task is used as an indicator to measure the task timeliness. The smaller the difference, the more urgent the task timeliness.

[0057] The two sub-models, the drone individual model and the flight environment model, realize data interaction and strategy linkage through a collaborative interaction model. The drone individual model provides motion constraints, the flight environment model provides perception data, and the collaborative interaction model serves as an information hub, integrating individual capabilities and environmental information into a global optimization goal, and ultimately constructing a complete multi-drone collaborative flight system model.

[0058] In this solution, the local obstacle avoidance algorithm uses a method that combines artificial potential field method with fuzzy logic to construct gravitational potential field and repulsive potential field. The gravitational potential field guides the drone to fly towards the target point, and the repulsive potential field enables the drone to avoid obstacles and other drones. Combined with the fuzzy logic controller, the weights of gravitational and repulsive forces are adjusted in real time to adapt to different flight environments and drone states, ensuring that there will be no collisions between drones while maintaining the stability of multi-drone coordinated flight.

[0059] During flight, when a dynamic obstacle is detected approaching, a dynamic adjustment mechanism is triggered. Each drone adjusts its flight path based on the latest environmental information and the status of neighboring drones using a local obstacle avoidance algorithm that combines artificial potential field method with fuzzy logic to avoid obstacles and other drones and maintain a safe distance.

[0060] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for multi-drone collaborative dynamic path planning, characterized in that: The steps include: Step 1: Establish a multi-machine collaborative flight system model that includes UAV individual models and flight environment models; Step 2: Real-time environmental information collection. Each drone collects real-time flight environment information through its onboard sensors, including the location, shape, and motion status of obstacles, as well as environmental meteorological data. The collected environmental information is sent to the ground control center via a wireless communication module or distributedly transmitted between drones to form global environmental information. This global environmental information is then fused and processed to remove noise and redundant information, resulting in accurate and complete environmental status data. Step 3: Distributed collaborative decision-making and path planning. Using a distributed collaborative algorithm, each drone interacts with other drones and makes collaborative decisions based on its own location, status, and global environment information. Step 4: Dynamic Adjustment and Collision Avoidance Control: During flight, the system monitors environmental changes and the flight status of drones in real time. When a new obstacle is detected or the distance between drones falls below a safety threshold, the dynamic adjustment mechanism is triggered. Each drone uses a local obstacle avoidance algorithm to adjust its current flight path in real time based on the latest environmental information and the position and speed of neighboring drones. Step 5: Multi-drone collaborative flight control. Based on the adjusted path, flight control instructions are generated for each drone, including flight speed, direction, and altitude. The drone's attitude and movement are precisely controlled through the control system. During the flight, the actual flight status of the drone is continuously fed back and compared with the expected status. Closed-loop control is performed to ensure the accuracy and stability of multi-drone collaborative flight.

2. The method for multi-UAV collaborative dynamic path planning according to claim 1, characterized in that: The individual model of the drone is: a(t)≤a max Represents the position of the drone at time t, p(t) is equal to the initial position p0 plus the integral of the velocity v(τ) from time 0 to time t over time. That is, the position is the sum of the initial position and the cumulative change of the velocity over time, reflecting the integral relationship between velocity and position; The velocity v(t) of the UAV at time t is equal to the initial velocity v0 plus the integral of the acceleration a(τ) from time 0 to time t over time. That is, the velocity is the sum of the initial velocity and the cumulative change of the acceleration over time, reflecting the integral relationship between acceleration and velocity. a(t)≤a max , which means that during the movement of the drone, the acceleration a(t) at any time t cannot exceed the set maximum acceleration a max , which is the constraint condition for the acceleration of the UAV during movement. At the same time, according to the maximum angular velocity ω of the UAV max and linear velocity v, through the formula Calculate the minimum turning radius to constrain the trajectory changes of the drone.

3. The method for multi-UAV collaborative dynamic path planning according to claim 1, characterized in that: The flight environment model is to digitally model the flight area using a grid method or a Voronoi diagram method, and to establish a position prediction model for dynamic obstacles using a Kalman filter: in, is the state estimate at time k, F k is the state transfer matrix, B k is the control matrix, u k is the control vector, K k is the Kalman gain, z k is the measured value, H k is the measurement matrix; Introducing the environmental parameter layer, the following formula is used to calculate the actual speed of the drone: Among them, v drone represents the flight speed set by the drone itself, θ is the flight direction angle of the drone itself, and v wind is the airflow velocity in the current environment, θ wind It represents the wind direction, that is, the direction angle of the airflow. By superimposing the UAV's own velocity vector with the ambient airflow velocity vector, the actual movement speed of the UAV in a complex meteorological environment is calculated.

4. The method for multi-UAV collaborative dynamic path planning according to claim 1, characterized in that: The multi-machine cooperative flight system model is based on the integration of UAV individual models and flight environment models, and the formula is: Cost = α·Individual energy consumption + β·Obstacle avoidance distance + γ·Task timeliness Among them, α, β, and γ are weight coefficients, which respectively measure the relative importance of individual energy consumption, obstacle avoidance distance, and mission timeliness in the total cost. This formula quantifies the above-mentioned individual energy consumption, obstacle avoidance distance, and mission timeliness into a single cost indicator through weighted summation, providing a decision-making basis for the dynamic path planning of multi-UAV collaborative operation, helping to optimize the overall path plan and balance energy consumption, safety, and mission efficiency.

5. The method for multi-UAV collaborative dynamic path planning according to claim 4, characterized in that: The formula for the individual energy consumption is expressed as It represents the integration operation of the motor power P from the starting time 0 to the current time τ. By integrating the power over time, the total work done by the drone motor during the flight time from 0 to τ is obtained. It quantifies the total amount of energy consumed by the motor to propel the drone during this time period.

6. The method for multi-UAV collaborative dynamic path planning according to claim 4, characterized in that: The obstacle avoidance distance is calculated as the distance between the current position of the drone p(t) and the predicted position of the obstacle The shortest distance.

7. The method for multi-UAV collaborative dynamic path planning according to claim 4, characterized in that: Mission timeliness is calculated based on the mission's start and end times, as well as the current time. The remaining available time is then estimated based on the drone's current position p(t), velocity v(t), and the mission's target location. The difference between the remaining available time and the predicted time is used as a measure of mission timeliness. A smaller difference indicates a more urgent mission.

8. The method for multi-UAV collaborative dynamic path planning according to claim 1, characterized in that: The local obstacle avoidance algorithm adopts a method that combines the artificial potential field method with fuzzy logic to construct a gravitational potential field and a repulsive potential field. The gravitational potential field guides the drone to fly towards the target point, and the repulsive potential field enables the drone to avoid obstacles and other drones. Combined with the fuzzy logic controller, the weights of gravity and repulsion are adjusted in real time to adapt to different flight environments and drone states, ensuring that there will be no collisions between drones while maintaining the stability of multi-drone collaborative flight.

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