Adaptive path planning method for UAVs in complex environments

Path planning is an important problem in the field of unmanned aerial vehicles (UAVs), particularly in complex environments; however, existing path planning methods have certain limitations and yield poor results. For a better solution to the path planning problem, we propose an adaptive path planning method for UAVs in complex environments. This method is based on discrete global grid systems for conflict detection between the airspace and UAV path. A multi-scale discrete layered grid model that provides a new management framework for the discrete global grid and accelerates conflict detection is proposed for complex environments. Thereafter, the particle swarm optimization (PSO) was exploited to develop an adaptive path planning PSO (APP-PSO) method, which was improved in terms of dimension, initialization, and iteration update strategy to plan an optimal path. Finally, the proposed method was validated by comparison with other related PSO algorithms and several simulation-based experiments illustrating its optimality.

Automated summary

An adaptive path planning method for UAVs in complex environments is proposed, which utilizes discrete global grid systems and a multi-scale discrete layered grid model, combined with particle swarm optimization algorithm, to address the path planning problem.