TY - GEN
T1 - IQ-ASyMTRe
T2 - 23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010
AU - Zhang, Yu
AU - Parker, Lynne E.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - This paper presents the IQ-ASyMTRe architecture, which is aimed to address both coalition formation and execution for tightly-coupled multirobot tasks in a single framework. Many task allocation algorithms have been previously proposed without explicitly enabling the sharing of robot capabilities. Inspired by information invariant theory, ASyMTRe was introduced which enables the sharing of sensory and computational capabilities by allowing information to flow among different robots via communication. However, ASyMTRe does not provide a solution for how a coalition should satisfy sensor constraints introduced by the sharing of capabilities while executing the assigned task. Furthermore, conversions among different information types1 are hardcoded, which limits the flexibility of ASyMTRe. Moreover, relationships between entities (e.g., robots) and information types are not explicitly captured, which may produce infeasible solutions from the start, as the defined information type may not correspond well to the current environment settings. The new architecture introduces a complete definition of information type to guarantee the feasibility of solutions; it also explicitly models information conversions. Inspired by our previous work, IQ-ASyMTRe uses measures of information quality to guide robot coalitions to satisfy sensor constraints (introduced by capability sharing) while executing tasks, thus providing a complete and general solution. We demonstrate the capability of the approach both in simulation and on physical robots to form and execute coalitions that share sensory information to achieve tightly-coupled tasks.
AB - This paper presents the IQ-ASyMTRe architecture, which is aimed to address both coalition formation and execution for tightly-coupled multirobot tasks in a single framework. Many task allocation algorithms have been previously proposed without explicitly enabling the sharing of robot capabilities. Inspired by information invariant theory, ASyMTRe was introduced which enables the sharing of sensory and computational capabilities by allowing information to flow among different robots via communication. However, ASyMTRe does not provide a solution for how a coalition should satisfy sensor constraints introduced by the sharing of capabilities while executing the assigned task. Furthermore, conversions among different information types1 are hardcoded, which limits the flexibility of ASyMTRe. Moreover, relationships between entities (e.g., robots) and information types are not explicitly captured, which may produce infeasible solutions from the start, as the defined information type may not correspond well to the current environment settings. The new architecture introduces a complete definition of information type to guarantee the feasibility of solutions; it also explicitly models information conversions. Inspired by our previous work, IQ-ASyMTRe uses measures of information quality to guide robot coalitions to satisfy sensor constraints (introduced by capability sharing) while executing tasks, thus providing a complete and general solution. We demonstrate the capability of the approach both in simulation and on physical robots to form and execute coalitions that share sensory information to achieve tightly-coupled tasks.
UR - http://www.scopus.com/inward/record.url?scp=78651515982&partnerID=8YFLogxK
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U2 - 10.1109/IROS.2010.5651186
DO - 10.1109/IROS.2010.5651186
M3 - Conference contribution
AN - SCOPUS:78651515982
SN - 9781424466757
T3 - IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
SP - 5595
EP - 5602
BT - IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
Y2 - 18 October 2010 through 22 October 2010
ER -