An Optimized Evolutionary Algorithm Applied to Mobile Robots For Finding The Shortest Path in A Known Environment

Mobile robots are used to accomplish various kinds of tasks nowadays. Path planning is one of the important processes that a mobile robot requires when there is a need for an automated navigation system. The path which is an output of the path planning should be a collision-free and optimized to increase the efficiency of the robot in various manners. Recently, meta-heuristic optimization techniques which have been inspired by the nature of the biosphere are used for finding the shortest path in path planning. This research mainly focused on developing a problem-specific evolutionary algorithm to generate the shortest path from a given initial position to a destination in a known environment. The improved algorithm consists of a combination of random mutation and windowed dynamic mutation operators that improve the intelligent path-searching process The MATLAB programming tool is used to develop the algorithm. Several case studies are conducted to find the shortest path between start and end points that are selected considering various distances. The Proposed EA can provide an optimized path with fewer iterations when compared to genetic algorithms. Thus, the proposed evolutionary algorithm is more computationally efficient than genetic algorithms for this specific application.