A dynamic obstacle avoidance path planning method for a UAV

Abstract

The application discloses a dynamic obstacle avoidance path planning method for an unmanned aerial vehicle. The method firstly defines a flight turning angle and a flight height cost function, and establishes a three-dimensional flight path planning target function of the unmanned aerial vehicle. Secondly, the pheromone concentration is non-uniformly initialized, and a fast exploration random tree algorithm is introduced to improve the individual state transition rule. Thirdly, a nonlinear dynamic adjustment pheromone evaporation factor is designed to improve the search efficiency of the algorithm. Finally, a quasi-uniform B-spline curve is used to optimize the path so that the path is more consistent with the flight trajectory of the unmanned aerial vehicle. The experimental results show that the average convergence accuracy of the proposed algorithm is the smallest among all the compared algorithms, which indicates that the proposed algorithm has stronger optimization ability and higher search efficiency, and can effectively avoid static and dynamic obstacles in a complex urban environment and obtain a required flight path.

Description

Technical Field

[0001] This invention relates to signal processing technology, applicable to the field of UAV path planning, and specifically to a dynamic obstacle avoidance path planning method for UAVs. Background Technology

[0002] Currently, UAV trajectory planning is often considered a complex optimization problem and solved using various optimization algorithms. Traditional algorithms include A* algorithm, fast exploratory random tree algorithm, and artificial potential field method. However, as the requirements for UAV trajectory planning continue to increase, these methods face problems such as low efficiency and low optimization accuracy, making it difficult to achieve the expected goals. It is worth noting that intelligent optimization algorithms have advantages in handling highly complex optimization problems and are gradually becoming the mainstream approach to solving such problems. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by providing a dynamic obstacle avoidance path planning method for unmanned aerial vehicles (UAVs). This method has stronger optimization capabilities and higher search efficiency, and can effectively avoid both static and dynamic obstacles in complex urban environments to obtain a suitable flight path.

[0004] The technical solution to achieve the objective of this invention is: A method for dynamic obstacle avoidance path planning for unmanned aerial vehicles (UAVs) includes the following steps: 1) City Model Construction: Design city terrain, buildings, movable obstacles, and no-fly zones to construct a complex city model, in which buildings are modeled as cuboids and no-fly zones as spheres; 2) Population creation: Create a population of a specific number of individuals, where each individual represents a feasible UAV path solution containing multiple waypoints; 3) Non-uniform pheromone concentration initialization: Non-uniform pheromone concentration is assigned to each feasible path node to avoid blind searching by individuals in the next step, thereby improving the search efficiency of the population. 4) Node search: Each individual in the population transitions its state towards the target point until it reaches the target point or the maximum number of searches is reached, and records the feasible paths; 5) Pheromone update: A non-linearly dynamically adjusted pheromone evaporation factor is introduced, which makes the pheromone decay faster in the early stage of the algorithm, thus enhancing the algorithm's search ability, and the pheromone decays more slowly in the later stage of the algorithm, thus enhancing the algorithm's accurate optimization ability. 6) Obtain the optimal path: Update the optimal path solution during the algorithm iteration process to obtain the optimal path solution.

[0005] To obtain a path that better meets the actual needs of the UAV, the objective function for trajectory planning shown is established by considering factors such as the UAV's path length, flight altitude, and flight turning angle: (1), in, The overall objective function for trajectory planning is denoted as . The function is the track length. This is a function of flight angle; It is a function of flight altitude; , , These are weighting coefficients, and they satisfy... , The track length function ensures that the UAV obtains a shorter track, which is a necessary condition for the UAV to perform its mission efficiently. A shorter track also reduces the risk of encountering attacks and colliding with obstacles during flight. The specific function expression is as follows: (2), in, For the first The coordinates of each path node. Design the flight altitude function, described as follows: (3), (4), Where HD is the sign function value of the height difference between the endpoint and the starting point. The endpoint height coordinates; The starting point's height coordinates; Design a non-uniform pheromone concentration initialization function, the expression of which is: (5), in, The starting coordinates; The coordinates of the endpoint; The four nodes with the highest heuristic values ​​are selected as feasible nodes to ensure that node replacement can be performed sequentially when there are obstacles ahead. When there are fewer than four feasible nodes, the node with the highest heuristic value is selected as the next transfer direction. The heuristic value is calculated as follows: (6), in, This represents the straight-line distance between the current node and the destination. The state transition rule after pruning is as follows: (7), in, These are the exploration locations that can be selected after pruning; To select a position for updating based on probability.

[0006] To further enhance the algorithm's optimization capability, the RRT algorithm is used to improve the state transition rules based on pruning. RRT is a traditional path planning algorithm suitable for path planning in complex environments. Its core idea is to explore paths in the solution space through random sampling. The improved state transition rules after RRT are as follows: (8), in, To explore step length, The selectable exploration location for a specific exploration step size; To update, a position is randomly selected based on probability.

[0007] Pheromon concentration is an important factor in determining an individual's update path. In the early stages of the algorithm, a higher concentration of pheromones is crucial. Values ​​that are beneficial for global search become less effective in the later stages of the algorithm. The value is beneficial for accurate optimization. To balance the global search and accurate optimization capabilities in the early and later stages of the algorithm, a nonlinear dynamically adjusted pheromone evaporation factor is designed: (9), in, A nonlinearly dynamically adjusted pheromone evaporation factor; As a regulating factor; This represents the current iteration number; The maximum number of iterations, This is the current optimal fitness value.

[0008] This technical solution provides a UAV 3D trajectory planning method for dynamic obstacle avoidance trajectory planning in complex urban environments. By defining the cost functions of flight angle and flight altitude, a UAV 3D trajectory planning objective function is further established, enabling the UAV to obtain a globally optimal path. Non-uniform initialization of pheromone concentration effectively avoids blind search in the early stages of the algorithm. A path optimization mechanism is introduced, using the RRT algorithm to improve the individual state transition rules, thereby enhancing the search efficiency and improving path quality. A non-linearly dynamically adjusted pheromone evaporation factor is designed, enabling the algorithm to escape local optima. Quasi-uniform B-spline curves are used to smooth the path, enhancing the flightability of the trajectory.

[0009] This method has stronger optimization capabilities and higher search efficiency. In complex urban environments, it can effectively avoid static and dynamic obstacles and obtain a suitable flight path. Attached Figure Description

[0010] Figure 1 This is a flowchart of the method in the embodiment; Figure 2Experimental results on the effectiveness of the strategy in the examples; Figure 3 Flight trajectory diagram in the embodiment. Detailed Implementation

[0011] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this is not intended to limit the scope of the invention.

[0012] Example: Reference Figure 1 A dynamic obstacle avoidance path planning method for unmanned aerial vehicles (UAVs) includes the following steps: 1) City Model Construction: Design city terrain, buildings, movable obstacles, and no-fly zones to construct a complex city model, in which buildings are modeled as cuboids and no-fly zones as spheres; 2) Population creation: Create a population of a specific number of individuals, where each individual represents a feasible UAV path solution containing multiple waypoints; 3) Non-uniform pheromone concentration initialization: Non-uniform pheromone concentration is assigned to each feasible path node to avoid blind searching by individuals in the next step, thereby improving the search efficiency of the population. 4) Node search: Each individual in the population transitions its state towards the target point until it reaches the target point or the maximum number of searches is reached, and records the feasible paths; 5) Pheromone update: A non-linearly dynamically adjusted pheromone evaporation factor is introduced, which makes the pheromone decay faster in the early stage of the algorithm, thus enhancing the algorithm's search ability, and the pheromone decays more slowly in the later stage of the algorithm, thus enhancing the algorithm's accurate optimization ability. 6) Obtain the optimal path: Update the optimal path solution during the algorithm iteration process to obtain the optimal path solution.

[0013] To obtain a path that better meets the actual needs of the UAV, the objective function for trajectory planning shown is established by considering factors such as the UAV's path length, flight altitude, and flight turning angle: (1), in, The overall objective function for trajectory planning is denoted as . The function is the track length. This is a function of flight angle; It is a function of flight altitude; , , These are weighting coefficients, and they satisfy... , The track length function ensures that the UAV obtains a shorter track, which is a necessary condition for the UAV to perform its mission efficiently. A shorter track also reduces the risk of encountering attacks and colliding with obstacles during flight. The specific function expression is as follows: (2), in, For the first The coordinates of each path node. Design the flight altitude function, described as follows: (3), (4), Where HD is the sign function value of the height difference between the endpoint and the starting point. The endpoint height coordinates; The starting point's height coordinates; Design a non-uniform pheromone concentration initialization function, the expression of which is: (5), in, The starting coordinates; The coordinates of the endpoint; The four nodes with the highest heuristic values ​​are selected as feasible nodes to ensure that node replacement can be performed sequentially when there are obstacles ahead. When there are fewer than four feasible nodes, the node with the highest heuristic value is selected as the next transfer direction. The heuristic value is calculated as follows: (6), in, This represents the straight-line distance between the current node and the destination. The state transition rule after pruning is as follows: (7), in, These are the exploration locations that can be selected after pruning; To select a position for updating based on probability.

[0014] To further enhance the algorithm's optimization capability, the RRT algorithm is used to improve the state transition rules based on pruning. RRT is a traditional path planning algorithm suitable for path planning in complex environments. Its core idea is to explore paths in the solution space through random sampling. The improved state transition rules after RRT are as follows: (8), in, To explore step length, The selectable exploration location for a specific exploration step size; To update, a position is randomly selected based on probability.

[0015] Pheromon concentration is an important factor in determining an individual's update path. In the early stages of the algorithm, a higher concentration of pheromones is crucial. Values ​​that are beneficial for global search become less effective in the later stages of the algorithm. The value is beneficial for accurate optimization. To balance the global search and accurate optimization capabilities in the early and later stages of the algorithm, a nonlinear dynamically adjusted pheromone evaporation factor is designed: (9), in, A nonlinearly dynamically adjusted pheromone evaporation factor; As a regulating factor; This represents the current iteration number; The maximum number of iterations, This is the current optimal fitness value.

[0016] Reference Figure 2 , Figure 3 In this example, we conducted strategy effectiveness testing, algorithm performance testing, and comparative experiments with other intelligent algorithms under different environmental models. It can be seen that the method in this example has excellent performance.

Claims

1. A dynamic obstacle avoidance path planning method for unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: 1) City Model Construction: Design city terrain, buildings, movable obstacles, and no-fly zones to construct a complex city model, in which buildings are modeled as cuboids and no-fly zones as spheres; 2) Population creation: Create a population of a specific number of individuals, where each individual represents a feasible UAV path solution containing multiple waypoints; 3) Non-uniform pheromone concentration initialization: Assign non-uniform pheromone concentration values ​​to each feasible path node; 4) Node search: Each individual in the population transitions its state towards the target point until it reaches the target point or the maximum number of searches is reached, and records the feasible paths; 5) Pheromone update: A non-linearly dynamically adjusted pheromone evaporation factor is introduced, which makes the pheromone decay faster in the early stage of the algorithm, thus enhancing the algorithm's search ability, and the pheromone decays more slowly in the later stage of the algorithm, thus enhancing the algorithm's accurate optimization ability. 6) Obtain the optimal path: Update the optimal path solution during the algorithm iteration process to obtain the optimal path solution.

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