Viscoelastic Fluid-Inspired Swarm Behavior to Reduce Susceptibility to Local Minima: The Chain Siphon Algorithm

We present a novel distributed robotic swarm algorithm inspired by the open channel siphon phenomenon displayed in certain viscoelastic fluids. Self-siphoning viscoelastic fluids are often able to mitigate the trapping effects of local minima in the environment. Using a similar strategy, our algorithm enables a robot swarm to mitigate the trapping effects of local minima in potential fields. 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 traps and often eliminating them. We perform hardware experiments using the Georgia Tech Miniature Autonomous Blimp (GT-MAB) aerial robotic platforms as well as Monte Carlo simulations conducted in the Simulating Collaborative Robots in Massive Multi-Agent Game Execution (SCRIMMAGE) simulator. We compare the new method to other potential field based swarm behaviors that both do and do not incorporate local minima fixes. The distributed algorithm generates self-siphoning behavior within the robotic swarm, and this reduces its susceptibility to local minima.

Automated summary

We present a novel distributed robotic swarm algorithm inspired by the open channel siphon phenomenon in certain viscoelastic fluids. The algorithm mitigates the trapping effects of local minima in potential fields by propagating gradient information through local communication in the robot swarm. Experimental results show that the algorithm reduces the susceptibility of the robot swarm to local minima.