Overcoming Local Minima Through Viscoelastic Fluid-Inspired Swarm Behavior

Abstract

My paper discusses a novel swarm robotic algorithm inspired by the open channel siphon phenomena displayed in certain viscoelastic fluids. This siphoning ability enables the algorithm to mitigate the trapping effects of local minima, which are known to affect physicomimetics-based potential field control methods. Once a robot senses the goal, local communication between robots is used to propagate path-to-goal gradient information through the swarm's communication graph. This information is used to augment each agent's local potential field, reducing the local minima trap and often eliminating it. In this paper real world experiments using the Georgia Tech Miniature Autonomous Blimp (GT-MAB) aerial robotic platforms as well as mass Monte Carlo test simulations conducted in the Simulating Collaborative Robots in Massive Multi-Agent Game Execution (SCRIMMAGE) simulator are presented. Comparisons between the resultant behaviors and potential field based swarm behaviors that both do, and do not incorporate local minima fixes were assessed. These experiments and simulations demonstrate that this method is an effective solution to susceptibility to local minima for potential field approaches for controlling swarms.