Route Mapping of Multiple Humanoid Robots Using Firefly-Based Artificial Potential Field Algorithm in a Cluttered Terrain

The hybrid outcome of the Firefly Algorithm (FA) and Artificial Potential Field (APF) algorithm for humanoid control is preferred in the present study for navigational tasks. Initially, the APF algorithm is procured with sensory input concerning the barrier range in a different direction, source points, and respective targets that give an intermediate steering angle. It is then fed to FA to obtain the ultimate steering angle as an output. FA guides the robot close (safest distance) to the obstacle and optimizes the footsteps to provide a better path. Simulations and real-time experiments are carried out for the motion of multiple humanoids in a cluttered terrain. Humanoid robots have been shown to be able to maneuver comfortably from source point to target using the preferred algorithm. In the system containing multiple humanoid robots, one robot behaves like a dynamic obstacle to the other humanoids, leading to a conflicting situation during navigation. This situation is eliminated by the implementation of the dining philosopher controller in the base algorithm. The simulation and experiment result shows a good relationship with a deviation of under 6%. Finally, the established trajectory planning algorithm is also tested in contrast to the existing navigational model.