Title | ||
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Design of ant-inspired stochastic control policies for collective transport by robotic swarms. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2014 | 10.1007/s11721-014-0100-8 | Swarm intelligence |
Keywords | Field | DocType |
Collective transport,Bio-inspired robotics,Stochastic robotics,Stochastic hybrid system,Distributed robotic system | Robot control,Population,Mathematical optimization,Swarm behaviour,Computer science,Team composition,Bio-inspired robotics,Artificial intelligence,Robot,Hybrid system,Machine learning,Stochastic control | Journal |
Volume | Issue | ISSN |
8 | 4 | 1935-3812 |
Citations | PageRank | References |
15 | 0.77 | 22 |
Authors | ||
6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Sean Wilson | 1 | 24 | 3.49 |
Theodore P. Pavlic | 2 | 42 | 10.50 |
Ganesh P. Kumar | 3 | 28 | 3.32 |
Aurélie Buffin | 4 | 17 | 1.17 |
Stephen C. Pratt | 5 | 100 | 7.21 |
Spring Berman | 6 | 317 | 30.90 |