Mobile robot path planning based on shuffled frog leaping optimization algorithm

Mobile robot path planning is a nondeterministic polynomial time (NP) problem, traditional optimization methods are not very effective to it, which are easy to plunge into local minimum. In this paper, we devise an evolutionary algorithm to solve the robot path planning problem. In the present work, we propose a method of robot path planning in partially unknown environments based on shuffled frog leaping (SFL) optimization algorithm. The proposed algorithm allows a mobile robot to navigate through static obstacles and finds its path in order to reach from its initial position to the target without collision. We firstly transform the problem of robot path planning into a minimization one, and then define the fitness of a frog based on the positions of the target and the obstacles in the environment. The position of the globally best frog in each iterative is selected, and reached by the robot in sequence. In addition, the environment is partially unknown for the robot due to the limit detection range of its sensors. The robot processor updates its information during the motion. We perform some simulations in different static environments, and the results show that the robot reaches its target with colliding free obstacles. The optimal path is generated with this method when the robot reaches its target. We implement our simulations on khepera II mobile robot in webots™ simulator environment.