Dynamic walking of multi-humanoid robots using BFGS Quasi-Newton method aided artificial potential field approach for uneven terrain

The humanoid robot garners paramount interest because of its ability to mimic human-like behavior in real-time environments. In this paper, a hybridized Artificial Potential Field (APF)-Broyden Fletcher Goldfarb Shanno (BFGS) Quasi-Newton technique is being proposed for the trajectory planning robot. methodology combines faster local search BFGS method with global APF an efficient and faster-hybridized technique. herein tested multi-humanoid working space having uneven surfaces. data obtained from sensors concerning location obstacles target are at first imparted method, which supplies intermediate turning angle dependent on predesigned training model method. then fed into quasi-newton produce optimum angle, inevitably guides robots by keeping minimum safe distance obstacles. Multi-humanoid employed environment random static obstacles, unique targets them reach. proposal using system arises chance inter-collision among humanoids. To get rid situation, dining philosophers controller implemented. simulations experiments carried ratify efficacy experimental simulation results yield suitable acceptance rate under 5%. comparison previously used navigational technique, it proves be comfortably skillful robust