Ant-Inspired Control Strategies for Collective Transport by Dynamic Multi-Robot Teams with Temporary Leaders
In certain ant species, groups of ants work together to transport food and materials back to their nests. In some cases, the group exhibits a leader-follower behavior in which a single ant guides the entire group based on its knowledge of the destination. In some cases, the leader role is occupied temporarily by an ant, only to be replaced when an ant with new information arrives. This kind of behavior can be very useful in uncertain environments where robot teams work together to transport a heavy or bulky payload. The purpose of this research was to study ways to implement this behavior on robot teams.
In this work, I combined existing dynamical models of collective transport in ants to create a stochastic model that describes these behaviors and can be used to control multi-robot systems to perform collective transport. In this model, each agent transitions stochastically between roles based on the force that it senses the other agents are applying to the load. The agent’s motion is governed by a proportional controller that updates its applied force based on the load velocity. I developed agent-based simulations of this model in NetLogo and explored leader-follower scenarios in which agents receive information about the transport destination by a newly informed agent (leader) joining the team. From these simulations, I derived the mean allocations of agents between “puller” and “lifter” roles and the mean forces applied by the agents throughout the motion.
From the simulation results obtained, we show that the mean ratio of lifter to puller populations is approximately 1:1. We also show that agents using the role update procedure based on forces are required to exert less force than agents that select their role based on their position on the load, although both strategies achieve similar transport speeds.