Adaptive Mayfly Algorithm for UAV Path Planning and Obstacle Avoidance in Indoor Environment

This paper presents the Adaptive Mayfly Algorithm (AMA) as a solution for path planning and obstacle avoidance in unmanned systems. Path planning is a critical function for autonomous navigation in complex environments. The AMA combines the global search capabilities of the Mayfly algorithm with the local search capability of obstacle avoidance approaches to provide an effective and efficient solution. For obstacle avoidance, the AMA employs a gradient descent approach to find an alternative path that avoids the obstacle. This adaptive approach ensures that the algorithm can handle complex situations and dynamically adjust the paths based on the environment. Simulation and experimental validation demonstrate that the AMA gives optimal path length and navigational time it also outperforms existing algorithms in terms of finding the best pathways and effectively avoiding obstacles in complicated scenarios. The obtained percentage of deviation between simulation and experimental results are less than 5.5%.