Hierarchical path generation for distributed mission planning of UAVs

Abstract

In the dynamical environment with forbidden areas, the achievement of multiple unmanned aerial vehicles (UAVs) executing cooperative missions strongly depends on the validity of mission planning results, which may be affected by the error of flight time estimation. In this paper, a hierarchical path generation scheme for improving the adaptability and performance of the distributed mission planning (DMP) system of the UAVs is proposed. The effectiveness of the market-based task allocation is enhanced by conducting a rough path planning beforehand, where only the neighboring forbidden areas of the vehicles are considered for a tradeoff between the planning accuracy and the computation. The subsequent refined path planning, which provides the guidance information to the control system, is also presented to assess the precision level of the rough path planning and the mission execution reward of the UAV fleet. The simulation results demonstrate the advantages and effectiveness of the proposed method for multiple UAVs executing missions in stochastic scenarios.