Path planning of unmanned aerial systems in a static 3D environment using firefly algorithm F1-3 with B-spline

This paper proposes an improved path planning solution for an unmanned aerial system (UAS). The proposed solution optimizes a cost function that takes path length, obstacle avoidance, turning angles, climb angles, flight height, and penalty constraints into account. The fluctuation of height in the obstacles is considered using a controlled stochastic function to make the path planning problem a realistic one. An improved version of the firefly algorithm (FA), namely firefly algorithm F1–3 with B-spline (FF1-3B), is used for optimization. A comparative analysis of the performance among five algorithms, namely spherical Vector-based particle swarm optimization (SPSO), FA, multi-verse optimizer (MVO), chaotic Cauchy opposition-based African vulture optimization algorithm (CCOAVOA), and FF1-3B, has been presented. The results show that the FF1-3B algorithm outperforms SPSO, MVO, CCOAVOA, and FA in terms of fitness value, smoothness, and path length. In particular, the FF1-3B algorithm achieves the best smoothness of the path, leading to less fuel consumption and better battery life of a UAS, allowing for more extended coverage and prolonged flight durations. The proposed solution promises to provide a feasible and optimized navigation for real-world UAS route planning tasks.