An Evolutionary Learning Approach for Robot Path Planning with Fuzzy Obstacle Detection and Avoidance in a Multi-agent Environment

This paper describes a Fuzzy-Genetic Algorithm Approach for path planning of mobile robots with obstacle detection and avoidance in static and dynamic scenarios. Through the software Net logo, used in simulations of multiagent applications, a seminal model was developed for the given problem. The model, which contains a robot and scenarios with or without obstacles, is responsible for determining the best path used by a robot to achieve the goal state in a shorter number of steps and avoiding collisions. Additionally, a performance evaluation of this model in comparison with A* algorithm is presented.