Collective dynamics and model verification: Connecting kinetic modeling to data

Design of ant-inspired stochastic control strategies for boundary coverage and collective transport by robotic swarms

Sean Wilson

Arizona State University
[SLIDES]

This work focuses on designing decentralized robot control policies that mimic certain microscopic and macroscopic behaviors of ants performing collective transport tasks. A stochastic hybrid system model has been used to characterize the observed team dynamics of ant group retrieval of a rigid load. The macroscopic population dynamics of the ants during transport have been used to design enzyme-inspired stochastic control policies that allocate a robotic swarm around multiple boundaries in a way that is robust to environmental variations. Three methods are presented for designing robot control policies that replicate steady-state distributions, transient dynamics, and fluxes between states that have been observed from ant transport experiments. The equilibrium population matching method can be used to achieve a desired transport team composition as quickly as possible; the transient matching method can control the transient population dynamics of the team while driving it to the desired composition; and the rate matching method regulates the rates at which robots join and leave a load during transport. To validate these controllers, the predictions have been tested using agent-based simulation. To further validate these controllers, a custom differential drive platform, nicknamed "Pheeno", is currently being developed. Pheeno is designed to be a low-cost, modular mobile platform that is capable of sensing and manipulating its environment. This platform will make experimental validation of robotic swarm strategies more affordable and realizable.