A Novel Method for Multi-UAV Cooperative Reconnaissance Mission Planning in Denied Environment

Abstract

The traditional swarm intelligence algorithm to solve the path planning in single combat style of unmanned aerial vehicle (UAV) can no longer meet the requirements of multi-UAV cooperative reconnaissance mission planning (MUCRMP) problem in denied environment for its slow convergence rate, ignorance of complex constraints and guidance to local optimization. A novel method for multi-UAV cooperative reconnaissance mission planning in denied environment (MUCRMP-DE) based on an improved synthetic heuristic algorithm is proposed to tackle these. In this paper, a hierarchical model is established with the global optimization goal of UAV's minimum radar detection time at first, including the planning of reconnaissance sequence between and within target groups, as well as relative position to targets. Then an improved synthetic heuristic algorithm is proposed to solve the model, which obtains valuable reconnaissance mission plan. For an application example of reconnaissance mission involving 68 targets, the simulation results show that the improved synthetic heuristic algorithm can suit the needs of the mission, particularly in effectively evading the detection of multiple radars. While it can also give better anti-radar attributes to the UA V and efficiently improved the convergence speed in the specific reconnaissance mission.