Adaptive Dragonfly Algorithm for AI-enabled path planning of humanoid robots in obstacle-constrained environments

Abstract Reliable path planning is a significant requirement for humanoid robots to move around in an environment filled with obstacles. In this paper, a novel method for humanoid robot navigation is proposed based on the Adaptive Dragonfly Algorithm (aDA), which is an improvement over the traditional Dragonfly Algorithm. The aDA method provides a better trade-off between exploration and exploitation of the search space, thereby avoiding local optima and increasing diversity in the search process. The aDA approach has been implemented and evaluated in a range of Webots-based simulation and experimental environments. Its performance is assessed using both single and multiple humanoid robots to analyze navigation capabilities under static as well as dynamic conditions. In multi-robot scenarios, conflict situations arise since each robot can act as a moving obstacle for the others, to overcome such situation, the dining philosopher controller is integrated with the proposed technique. The results of the aDA method are compared with traditional heuristics and metaheuristics for different scenarios of obstacle densities. The results obtained from the virtual and real environments show a variation within 5%, which proves that the aDA method is a robust and intelligent solution for humanoid robot navigation in a real-time environment. Furthermore, the proposed controller is benchmarked against an existing navigation method, demonstrating its improved performance and effectiveness.