Bee foraging inspired multi-agent optimal motion planning analysis in a simulated-mobile environment

Abstract

Motion-planning has been the arena of attraction for researchers in Multi-agent systems. This paper puts forward a pioneering approach to path-planning of mobile agents using the stochastic artificial bee colony (ABC) optimization algorithm amidst dynamic obstacles thereby highlighting a comparative analysis of the different bio-inspired computing schemes. The problem formulates the determination of the trajectory of motion of the robots from predefined starting positions to fixed destinations in the world map with an ultimate objective to minimize the path length of all the robots. A local trajectory planning scheme has been devised with bee-colony optimization algorithm to optimally obtain the next positions of all the robots in the world map from their current positions, so that the paths to be developed locally for n-robots are sufficiently small with minimum spacing with the static and dynamic obstacles, present, in the world map. Experimental results are indicative of the fact that the proposed optimization scheme outperforms prevalent algorithms of literature, with respect to standard metrics, like average total path deviation and average uncovered target distance.