TY - GEN
T1 - Stochastic strategies for a swarm robotic assembly system
AU - Matthey, Löic
AU - Berman, Spring
AU - Kumar, Vijay
PY - 2009
Y1 - 2009
N2 - We present a decentralized, scalable approach to assembling a group of heterogeneous parts into different products using a swarm of robots. While the assembly plans are predetermined, the exact sequence of assembly of parts and the allocation of subassembly tasks to robots are determined by the interactions between robots in a decentralized fashion in real time. Our approach is based on developing a continuous abstraction of the system derived from models of chemical reactions and formulating the strategy as a problem of selecting rates of assembly and disassembly. These rates are mapped onto probabilities that determine stochastic control policies for individual robots, which then produce the desired aggregate behavior. This top-down approach to determining robot controllers also allows us to optimize the rates at the abstract level to achieve fast convergence to the specified target numbers of products. Because the method incorporates programs for assembly and disassembly, changes in demand can lead to reconfiguration in a seamless fashion. We illustrate the methodology using a physics-based simulator with examples involving 15 robots and two types of final products.
AB - We present a decentralized, scalable approach to assembling a group of heterogeneous parts into different products using a swarm of robots. While the assembly plans are predetermined, the exact sequence of assembly of parts and the allocation of subassembly tasks to robots are determined by the interactions between robots in a decentralized fashion in real time. Our approach is based on developing a continuous abstraction of the system derived from models of chemical reactions and formulating the strategy as a problem of selecting rates of assembly and disassembly. These rates are mapped onto probabilities that determine stochastic control policies for individual robots, which then produce the desired aggregate behavior. This top-down approach to determining robot controllers also allows us to optimize the rates at the abstract level to achieve fast convergence to the specified target numbers of products. Because the method incorporates programs for assembly and disassembly, changes in demand can lead to reconfiguration in a seamless fashion. We illustrate the methodology using a physics-based simulator with examples involving 15 robots and two types of final products.
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U2 - 10.1109/ROBOT.2009.5152457
DO - 10.1109/ROBOT.2009.5152457
M3 - Conference contribution
AN - SCOPUS:70350367577
SN - 9781424427895
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1953
EP - 1958
BT - 2009 IEEE International Conference on Robotics and Automation, ICRA '09
T2 - 2009 IEEE International Conference on Robotics and Automation, ICRA '09
Y2 - 12 May 2009 through 17 May 2009
ER -