Navigation of multiple humanoid robots using hybrid adaptive swarm‐adaptive ant colony optimisation technique

Abstract This paper is aimed at designing a navigation strategy for humanoid robots using a hybridised technique consisting of adaptive particle swarm optimisation and adaptive ant colony optimisation. The inputs to the navigational controller are the front obstacle distance, left obstacle distance, and right obstacle distance, and the output is the required final turning angle to reach the target position. Here, the governing parameters of the adaptive ant colony optimisation technique are optimised by using adaptive particle swarm optimisation method. These optimised parameters are subsequently used by the adaptive ant colony optimisation technique to get the final turning angle by which the humanoid navigates in a cluttered environment. Here, navigation is performed in both static and dynamic environments. To avoid the intercollision among the humanoids, a Petri‐net controller has been designed and implemented along with the proposed hybridised method. Humanoid navigation is performed in both simulated and experimental environments, and a comparison is done between them. Finally, the proposed controller is compared with the developed method by other researchers.