### Abstract

We consider the problem of dynamic reconfiguration by modular self-reconfigurable robots (MSRs) in the presence of uncertainty in their motion and the environment. Specifically, we consider the situation where the MSR is unable to continue its motion in its current configuration and needs to identify a new configuration among the existing modules, which would be the most configuration suitable for performing the robot's assigned task under the current circumstances. To address this problem, we propose a new data structure called an uncertain coalition structure graph (UCSG) that accommodates uncertainty in the MSR's motion and the environment, using a framework from cooperative game theory called the coalition structure graph. We then propose a new search algorithm called searchUCSG that intelligently prunes nodes from the UCSG using a modified branch-and-bound technique. We have shown analytically that our algorithm is anytime, that is, if it terminates arbitrarily, it returns the best solution found thus far, which is guaranteed to be within a constant bound from the optimal solution. We have verified the performance of our algorithm experimentally in simulation and shown that it is able to find a solution that is within the worst bound of 80% of the optimal solution while exploring only half of the nodes in the UCSG. Our algorithm also takes lesser computation time than the existing algorithms (that do not model uncertainty) for solving similar problems. Finally, to verify the operation of our algorithm, we have implemented it to partition a set of mobile e-puck robots into clusters and shown how different number of robots and different robot motion uncertainty parameters affect the formed clusters.

Original language | English (US) |
---|---|

Pages (from-to) | 225-244 |

Number of pages | 20 |

Journal | Robotica |

Volume | 32 |

Issue number | 2 |

DOIs | |

State | Published - Jan 1 2014 |

### Fingerprint

### Keywords

- Automation
- DARS2012
- Modular robots
- Multi-robot systems
- Space robotics

### ASJC Scopus subject areas

- Control and Systems Engineering
- Software
- Mathematics(all)
- Computer Science Applications

### Cite this

*Robotica*,

*32*(2), 225-244. https://doi.org/10.1017/S0263574714000095

**SearchUCSG : A fast coalition structure search algorithm for modular robot reconfiguration under uncertainty.** / Dutta, Ayan; Dasgupta, Prithviraj; Baca, José; Nelson, Carl A.

Research output: Contribution to journal › Article

*Robotica*, vol. 32, no. 2, pp. 225-244. https://doi.org/10.1017/S0263574714000095

}

TY - JOUR

T1 - SearchUCSG

T2 - A fast coalition structure search algorithm for modular robot reconfiguration under uncertainty

AU - Dutta, Ayan

AU - Dasgupta, Prithviraj

AU - Baca, José

AU - Nelson, Carl A

PY - 2014/1/1

Y1 - 2014/1/1

N2 - We consider the problem of dynamic reconfiguration by modular self-reconfigurable robots (MSRs) in the presence of uncertainty in their motion and the environment. Specifically, we consider the situation where the MSR is unable to continue its motion in its current configuration and needs to identify a new configuration among the existing modules, which would be the most configuration suitable for performing the robot's assigned task under the current circumstances. To address this problem, we propose a new data structure called an uncertain coalition structure graph (UCSG) that accommodates uncertainty in the MSR's motion and the environment, using a framework from cooperative game theory called the coalition structure graph. We then propose a new search algorithm called searchUCSG that intelligently prunes nodes from the UCSG using a modified branch-and-bound technique. We have shown analytically that our algorithm is anytime, that is, if it terminates arbitrarily, it returns the best solution found thus far, which is guaranteed to be within a constant bound from the optimal solution. We have verified the performance of our algorithm experimentally in simulation and shown that it is able to find a solution that is within the worst bound of 80% of the optimal solution while exploring only half of the nodes in the UCSG. Our algorithm also takes lesser computation time than the existing algorithms (that do not model uncertainty) for solving similar problems. Finally, to verify the operation of our algorithm, we have implemented it to partition a set of mobile e-puck robots into clusters and shown how different number of robots and different robot motion uncertainty parameters affect the formed clusters.

AB - We consider the problem of dynamic reconfiguration by modular self-reconfigurable robots (MSRs) in the presence of uncertainty in their motion and the environment. Specifically, we consider the situation where the MSR is unable to continue its motion in its current configuration and needs to identify a new configuration among the existing modules, which would be the most configuration suitable for performing the robot's assigned task under the current circumstances. To address this problem, we propose a new data structure called an uncertain coalition structure graph (UCSG) that accommodates uncertainty in the MSR's motion and the environment, using a framework from cooperative game theory called the coalition structure graph. We then propose a new search algorithm called searchUCSG that intelligently prunes nodes from the UCSG using a modified branch-and-bound technique. We have shown analytically that our algorithm is anytime, that is, if it terminates arbitrarily, it returns the best solution found thus far, which is guaranteed to be within a constant bound from the optimal solution. We have verified the performance of our algorithm experimentally in simulation and shown that it is able to find a solution that is within the worst bound of 80% of the optimal solution while exploring only half of the nodes in the UCSG. Our algorithm also takes lesser computation time than the existing algorithms (that do not model uncertainty) for solving similar problems. Finally, to verify the operation of our algorithm, we have implemented it to partition a set of mobile e-puck robots into clusters and shown how different number of robots and different robot motion uncertainty parameters affect the formed clusters.

KW - Automation

KW - DARS2012

KW - Modular robots

KW - Multi-robot systems

KW - Space robotics

UR - http://www.scopus.com/inward/record.url?scp=84898051493&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898051493&partnerID=8YFLogxK

U2 - 10.1017/S0263574714000095

DO - 10.1017/S0263574714000095

M3 - Article

AN - SCOPUS:84898051493

VL - 32

SP - 225

EP - 244

JO - Robotica

JF - Robotica

SN - 0263-5747

IS - 2

ER -