An Intelligent Path Planning Approach for Humanoid Robots Using Adaptive Particle Swarm Optimization

The current investigation is focused on the development of a novel navigational controller for the optimized path planning and navigation of humanoid robots. The proposed navigational controller works on the principle of adaptive particle swarm optimization. To improve the working pattern of a simple particle swarm optimization controller, some modifications are done to the controlling parameters of the algorithm. The input parameters to the controller are the sensory information in forms of obstacle distances, and the output from the controller is the required turning angle to safely reach the target position by avoiding the obstacles present in the path. By applying the logic of the adaptive particle swarm optimization, humanoid robots are tested in simulation environments. To validate the results, an experimental platform is also developed under laboratory conditions, and a comparison has been performed between the simulation and experimental results. To test the proposed controller in both static and dynamic environments, it is implemented in the navigation of single as well as multiple humanoid robots. Finally, to ensure the efficacy of the proposed controller, it is compared with some of the existing techniques available for navigational purpose.