Design of ant-inspired stochastic control policies for collective transport by robotic swarms
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In this paper, we present an approach to designing decentralized robot control policies that mimic certain microscopic and macroscopic behaviors of ants performing collective transport tasks. In prior work, we used a stochastic hybrid system model to characterize the observed team dynamics of ant group retrieval of a rigid load. We have also used macroscopic population dynamic models to design enzyme-inspired stochastic control policies that allocate a robotic swarm around multiple boundaries in a way that is robust to environmental variations. Here, we build on this prior work to synthesize stochastic robot attachment–detachment policies for tasks in which a robotic swarm must achieve non-uniform spatial distributions around multiple loads and transport them at a constant velocity. Three methods are presented for designing robot control policies that replicate the steady-state distributions, transient dynamics, and fluxes between states that we have observed in ant populations during group retrieval. 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. We validate our model predictions in an agent-based simulation, verify that each controller design method produces successful transport of a load at a regulated velocity, and compare the advantages and disadvantages of each method.
KeywordsCollective transport Bio-inspired robotics Stochastic robotics Stochastic hybrid system Distributed robotic system
Comments by Simon DeDeo regarding the philosophical implications of equilibria of irreversible processes have been helpful in framing our thoughts about this approach. Interaction with him was possible due to a workshop on Information, Complexity, and Life organized by the BEYOND Center for Fundamental Concepts in Science at Arizona State University. We are also grateful for the helpful comments of four anonymous referees. This work was supported in part by NSF Award no. CCF-1012029, NSF Award No. CMMI-1363499, and DARPA Young Faculty Award No. D14AP00054.
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