A double-layer coding model with a rotation-based particle swarm algorithm for unmanned combat aerial vehicle path planning

Unmanned combat aerial vehicle (UCAV) technology has to address many challenges in complex battlefield environments. To produce a safe and low-energy flying path, a UCAV requires many points to build a path that avoids threats, which increases the problem dimension, consumes more computational resources and makes the results unstable. To address this issue, this paper employs a new model known as double-layer coding (DLC) for path planning with unevenly distributed points to decrease the number of superfluous points on the path. Meanwhile, the RPSO algorithm, which introduces a novel strategy of rotating particles in high-dimensional space to search for targets, is proposed as an enhanced particle swarm optimization (PSO) algorithm. The proposed method effectively improves PSO ' s exploration capacity. Furthermore, RPSO is employed to implement the double-layer coding model for path planning (DLCRPSO). The experimental results show that the proposed DLCRPSO method for path planning always produces feasible flight paths in complex environments.

Automated summary

This paper proposes a path planning method for unmanned combat aerial vehicles (UCAVs) in complex battlefield environments, which combines a double-layer coding model with the RPSO algorithm. The method reduces the number of superfluous points on the path and improves the exploration capacity of the PSO algorithm.