A collaborative task allocation and path planning algorithm for heterogeneous UAV cluster based on intelligent emotional mode

This article proposes a collaborative task allocation and path planning algorithm for heterogeneous unmanned aerial vehicle (UAV) cluster based on intelligent emotional mode in a multi-mission scenario. The basis of this algorithm is to establish constraint and environment models of heterogeneous UAV cluster to maximize net profit under the constraints of range, task requirement, track safety and payload. We integrate intelligent emotional of heterogeneous UAV cluster into net profit function, construct a collaborative task allocation and path planning model, and jointly optimize the range, value gain, and threat level of heterogeneous UAV cluster. By introducing the roulette wheel selection (RWS) principle and elite strategy (ES) into ant colony optimization (ACO) algorithm to address this NP-hard problem. The simulation results demonstrate that the proposed algorithm is superior and more efficient compared to other approaches.