| With the development of technologies such as artificial intelligence,microelectronics,and aviation control,drone technology has become increasingly mature.They have the advantages of low cost,high flexibility,and high efficiency,and are widely used in various fields.To solve increasingly complex task environments,it is critical to develop multi-drone task planning.This paper mainly studies multi-drone task planning issues in complex environments,focusing on task allocation and path planning.A complex environment refers to one that involves obstacles,threat sources,and emergent threats.Firstly,combining the three-dimensional environment model with the information of multiple drones and task points,we use task assignment algorithms to distribute tasks to multiple drones and obtain the optimal execution plan.Then,based on this plan,we plan the safe path for drones to the task points and study the local path planning problem for drones in the face of sudden threats.The main research work and achievements of this paper include:(1)Establishing a multi-UAV task allocation model,constructing a mathematical model with a reasonable cost function and constraints from the perspective of task allocation,and then using a task allocation algorithm to solve the allocation model.An adaptive hybrid particle swarm algorithm is proposed as the task allocation algorithm based on the characteristics of task allocation and particle swarm optimization algorithm.To address the problem of the traditional particle swarm optimization algorithm easily falling into a local optimum,the improved algorithm introduces an adaptive inertia weight and combines the fast simulated annealing algorithm with the particle swarm optimization algorithm.Experimental results demonstrate that the improved algorithm can effectively solve the multi-UAV task allocation problem and improve search capability.(2)A problem model based on path planning evaluation indicators is established for the global path planning problem of multi-UAVs in a known environment,and an improved Harris hawk algorithm is proposed.To improve the balance between global and local searches and the global search capability of the Harris hawk algorithm,a nonlinear energy factor and chaotic mapping are introduced.Experimental results demonstrate the feasibility and efficiency of the improved algorithm in solving the multi-UAV global path planning problem.(3)Using an improved artificial potential field method to solve the local path planning problem for unmanned aerial vehicles(UAVs)when encountering sudden threats.Compared to traditional artificial potential fields,the virtual force guidance method is employed,along with the introduction of a distance factor to address the issues of local minima and unreachable targets,respectively.Simulation results demonstrate that this algorithm is capable of resolving unexpected threats encountered by UAVs,enabling them to safely reach their designated waypoints. |
