Optimal Path Planning for an Autonomous Mobile Robot Using Dragonfly Algorithm

Abstract

Navigation, path generation and obstacle avoidance are considered as the key challenges in the area of autonomous mobile robots. In this article, a new meta-heuristic optimization technique called Dragonfly Algorithm (DA) is employed for the navigation of autonomous mobile robot in an unknown cluttered environment filled with several static obstacles. This new meta-heuristic Dragonfly algorithm is inspired from the static and dynamic swarming behaviours of dragonflies in nature. Two objective functions, target seeking and obstacle avoidance are formulated based on the distance between the robot, target and the obstacles and is optimized using the proposed DA for obtaining optimal path. After every iteration, based on the objective function values the robot proceeds towards the globally best agent in the swarm in a sequence of permutation which finally leads to the target. A variety of static environment is modelled and the algorithm is tested both through simulation and experimentally. The proposed algorithm shows that the robot reaches the target without colliding any obstacles while generating a smooth optimal trajectory.