Task Allocation and Path Planning Algorithms for Multi-Fixed-Wing UAV

[Purposes] The task allocation and path planning of multi-fixed-wing Unmanned Aerial Vehicle (UAV) simply regard it as a particle, thus the problems of ignoring its own heading angle, initial and terminal velocity vectors exist. Aiming at the problem of multi-fixed-wing UAV mission planning, improved Ant Colony Optimization (ACO) algorithm and Self-Organizing Map (SOM) algorithm are combined to propose ACO-SOM algorithm for multi-UAV system. [Methods] The SOM was used for task allocation, and the ant colony state transition probability method was introduced to adjust the weights of SOM winning neurons, so that the fixed-wing UAV reached each task point in turn according to the optimal dubins route. According to the cruise ability and kinematic constraints, the tasks were reasonably allocated, and the multi-objective optimization function was constructed to realize the cooperative task execution ability of multiple fixed-wing UAVs. The ACO-SOM algorithm was simulated and verified in the scenarios of obstacles and different landing points. [Findings] Experimental results show that, compared with SOM, the proposed method can make the multi-fixed-wing UAV reasonably assign tasks and plan the optimal path, and has higher obstacle avoidance and planning abilities.