Bi-level intelligent dynamic path planning for an UAV in low-altitude complex urban environment

Abstract

An offline and online bi-level structure-based dynamic path planning algorithm is proposed for an unmanned aerial vehicle (UAV) in low-altitude complex urban environment. First, an improved Hunger Games Search (HGS) algorithm is developed to generate an offline optimized path under the UAV’s performance constraints and the known static obstacles’ constraints. The individuals of the proposed algorithm will be divided into multiple groups to increase the population diversity. And then, a dynamic grouping strategy and a quantum-behaved behavior are proposed to solve the premature convergence’s problem and the imbalance problem between exploration and exploitation ability in HGS. To improve the dynamic obstacle avoidance efficiency of the algorithm, the dynamic obstacles are classified into three categories: newly added no-fly zone, known and unknown dynamic obstacles. Then, utilizing the information of the offline optimized path and the airborne sensors, three kinds of online planning strategies—an improved rapid-exploring random tree (RRT), a changing speed strategy, and a novel three-dimensional rolling windows—are introduced to dynamically update the path or speed of the UAV. Simulation results indicated that the improved HGS can enhance the performances of the traditional HGS and outperform other compared algorithms on the benchmark functions. Meanwhile, the online planning strategies can effectively achieve dynamic obstacle avoidance within the constraints of offline path. More specially, the planning time and angles of the local path to avoid the no-fly-zone’s influence are improved by 11.3% and 56.8% through utilizing the improved RRT.