Improved Rapidly-Exploring Random Tree using Firefly Algorithm for Robot Path Planning

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firefly Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for "Enhanced RRT with Firefly Algorithm", generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by the RRT algorithm and better paths in complex environments. The basic idea of the FA is to refine the resulting path by the RRT algorithm through optimizing the positions of Fireflies based on their intensity. Various tests show that ERRT-FA works better than the RRT algorithm in many robotic situations. It indicates a significant reduction in computation time, exploration efficiency, and route length, with statistical analysis showing a mean decrease. Such a result denotes that the proposed ERRT-FA is an alternative solution for optimizing ERRT-FA as a perfect path plan.