Fast Hybrid PSO-APF Algorithm for Path Planning in Obstacle Rich Environment

The present study aims to evolve a new hybrid algorithm for fast path planning in an obstacle-rich environment using the particle swarm optimization (PSO) and artificial potential field (APF). The proposed methodology combines the PSO with APF to improve the speed of determining a feasible and minimum cost path in an environment which has a high obstacle density. The proposed hybridization generates the way-points to be followed, by employing a combination of PSO and APF and it is found that in the environment with different obstacle densities, the hybrid algorithm arrives at the minimum cost feasible path in significantly lesser time in comparison to the basic PSO. It is also shown that the proposed hybrid algorithm is significantly faster than PSO even when there are moving obstacles and hence has the potential to be employed in real-time applications.