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


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.