A 3D Path Planning Algorithm Based on PSO for Autonomous UAVs Navigation

AbstractIn this paper, a new three-dimensional path planning approach with obstacle avoidance for UAVs is proposed. The aim is to provide a computationally-fast on-board sub-optimal solution for collision-free path planning in static environments. The optimal 3D path is an NP (non-deterministic polynomial-time) hard problem which may be solved numerically by global optimization algorithms such as the Particle Swarm Optimization (PSO). Application of PSO to the 3D path planning class of problems faces typical challenges such slow convergence rate. It is shown that the performance may be improved markedly by implementing a novel parallel approach and incorporation of new termination conditions. Moreover, the exploration and exploitation parameters are optimized to find a reasonably short, smooth, and safe path connecting the way-points. As an additional precaution to avoid collisions, obstacle dimensions are artificially slightly enlarged. To verify the robustness of the algorithm, several simulations are carried out by varying the number of obstacles, their volume and location in space. A certain number of simulations exploiting the random nature of PSO are performed to highlight the computational efficiency, and the robustness of this new approach. KeywordsParticle swarm optimization (PSO)3D path planning algorithmUnmanned aerial vehicle (UAV)Autonomous navigation