Three Dimensional Path Planning for UAVs in Dynamic Environment using Glow-worm Swarm Optimization

We propose an efficient solution for finding a collision-free path in a Three-Dimensional environment with dynamic obstacles for Unmanned Aerial Vehicles (UAVs). Path Planning for Unmanned Aerial Vehicles (UAVs) in Three Dimensional Dynamic Environment is considered a challenging task in the field of robotics. During their mission, UAVs have to maneuver in an environment which can have obstacles of varying size and random motion. The aim of the proposed algorithm is to traverse an optimal flight route in real world environment with no collision with environmental elements. This paper proposes use of a Glow-worm Swarm Optimization (GSO) for Path-Planning of Unmanned Aerial Vehicles (UAVs). It provides improved convergence rate and accuracy than the other Meta Heuristic optimization algorithms. The simulation is modelled in a real world environment. A swarm of particles is made to co-ordinate with each other for optimal path planning. The simulation is run in Python and the viability of the algorithm according to path-cost, time and number of expanded nodes is measured.