Path Planning for an Autonomous Mobile Robot Using Red Fox Optimization Algorithm

Path planning is a key challenge in the study of autonomous mobile robots. This paper describes a new bio-inspired optimization method known as the Red Fox Optimization algorithm, which generates a path free of collision for mobile autonomous robots in a known environment with static barriers. The actions of the red fox population in nature served as inspiration for this algorithm. Foxes have created a very successful hunting system that comprises two distinct phases. The first is carrying out a worldwide search, and the second is a local search. Global search behavior was implemented to determine the optimum collision-free route between starting position and the goal position. The algorithm was tested through a simulation in a range of static environments. The suggested algorithm demonstrates that the robot can create an ideal semi-smooth path by reducing the number of nodes and successfully navigating to its objective by avoiding obstacles.