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.
Details
- Design of Ant-Inspired Stochastic Control Policies for Collective Transport by Robotic Swarms
- Wilson, Sean (Author)
- Pavlic, Theodore (Author)
- Kumar, Ganesh (Author)
- Buffin, Aurelie (Author)
- Pratt, Stephen (Author)
- Berman, Spring (Author)
- Ira A. Fulton Schools of Engineering (Contributor)
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Digital object identifier: 10.1007/s11721-014-0100-8
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Identifier TypeInternational standard serial numberIdentifier Value1935-3812
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Identifier TypeInternational standard serial numberIdentifier Value1935-3820
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This is the authors' final accepted manuscript. The final publication is available at http://dx.doi.org/10.1007/s11721-014-0100-8
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Wilson, Sean, Pavlic, Theodore P., Kumar, Ganesh P., Buffin, Aurelie, Pratt, Stephen C., & Berman, Spring (2014). Design of ant-inspired stochastic control policies for collective transport by robotic swarms. SWARM INTELLIGENCE, 8(4), 303-327. http://dx.doi.org/10.1007/s11721-014-0100-8