A fuzzy set-based methodology for autonomous navigation

This paper presents a fuzzy set-based methodology to achieve simultaneous path adherence and local collision avoidance in autonomous navigation. It targets scenarios where a global path to the vehicle's destination is already planned but with imperfect knowledge of the obstacles in the environment and their dynamics. Assuming that the vehicle can localize itself and the obstacles around it, the proposed methodology incorporates the global path and the acquired real-time knowledge of the local obstacles by the vehicle to create a comprehensive fuzzy representation of the environment. This fuzzy representation is then utilized to assess the desirability of the vehicle states within an optimization framework that balances global path adherence and obstacle avoidance objectives. The proposed gradient-based solution to this optimization problem navigates the vehicle such that it maintains its global course toward the designated destination while avoiding collision with local obstacles. Extensive simulations on a mobile robot validate the method's efficacy in guiding vehicles along desired paths, maneuvering around obstacles with minimal deviation from the route, and negotiating local minima without oscillations. Additionally, simulation results highlight the proposed fuzzy set-based methodology's low computational complexity, real-time operation capability, and adaptability in handling complex geometries of paths and obstacles.