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Conference Paper: A Closed-Loop Bid Adjustment Method of Dynamic Task Allocation of Robots
Title | A Closed-Loop Bid Adjustment Method of Dynamic Task Allocation of Robots |
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Authors | |
Issue Date | 2013 |
Publisher | Springer Netherlands. The Journal's web site is located at http://www.springer.com/series/7818 |
Citation | International Conference in Electrical Engineering and Intelligent Systems of World Congress on Engineering (WCE 2011), London, United Kingdom, 6-8 July 2011. In Lecture Notes in Electrical Engineering, 2013, v. 130, p. 81-94 How to Cite? |
Abstract | Task allocation in dynamic environments is one of the most challenging problems in multi-robot coordination, and it is imperative for a wide range of applications. Auction-based approaches are popular methods that aim to assemble robot team information at a single location to achieve practicable task allocation. However, a main deficiency of auction-based methods is that robots generally do not have sufficient information to estimate accurate bids to perform tasks, particularly in dynamic environments with operational uncertainties. While some techniques have been developed to improve bidding, they are mostly open-looped without feedback adjustments to tune the bid prices for subsequent tasks of the same type. Robots’ bids, if not adjusted accordingly, may not be trustworthy and would indeed impede team performance. To tackle this issue, we propose a closed-loop bid adjustment mechanism to improve robots’ bids, and hence enhance the overall team performance. Each robot in a team maintains its own track record as closed-loop feedback information to adjust its bid prices. After a robot has completed a task, it assesses its performance to reflect the discrepancy between the bid price and the actual cost of the task. A series of such performance records, with time-discounting factors, are taken into account to damp out fluctuations of bid adjustments. Adopting this adjustment mechanism, a task would be more likely allocated to a competent robot that submits a more accurate bid price, and hence improve the overall team performance. Simulation of task allocation of automated guided vehicles serving at a container terminal is presented to demonstrate the effectiveness of the bid adjustment mechanism. |
Description | Lecture Notes in Electrical Engineering, vol. 130 has title: Electrical engineering and intelligent systems |
Persistent Identifier | http://hdl.handle.net/10722/201905 |
ISBN | |
ISSN | 2023 SCImago Journal Rankings: 0.147 |
DC Field | Value | Language |
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dc.contributor.author | Choi, SH | en_US |
dc.contributor.author | Zhu, W | en_US |
dc.date.accessioned | 2014-08-21T07:48:37Z | - |
dc.date.available | 2014-08-21T07:48:37Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | International Conference in Electrical Engineering and Intelligent Systems of World Congress on Engineering (WCE 2011), London, United Kingdom, 6-8 July 2011. In Lecture Notes in Electrical Engineering, 2013, v. 130, p. 81-94 | en_US |
dc.identifier.isbn | 9781461423164 | - |
dc.identifier.issn | 1876-1100 | - |
dc.identifier.uri | http://hdl.handle.net/10722/201905 | - |
dc.description | Lecture Notes in Electrical Engineering, vol. 130 has title: Electrical engineering and intelligent systems | - |
dc.description.abstract | Task allocation in dynamic environments is one of the most challenging problems in multi-robot coordination, and it is imperative for a wide range of applications. Auction-based approaches are popular methods that aim to assemble robot team information at a single location to achieve practicable task allocation. However, a main deficiency of auction-based methods is that robots generally do not have sufficient information to estimate accurate bids to perform tasks, particularly in dynamic environments with operational uncertainties. While some techniques have been developed to improve bidding, they are mostly open-looped without feedback adjustments to tune the bid prices for subsequent tasks of the same type. Robots’ bids, if not adjusted accordingly, may not be trustworthy and would indeed impede team performance. To tackle this issue, we propose a closed-loop bid adjustment mechanism to improve robots’ bids, and hence enhance the overall team performance. Each robot in a team maintains its own track record as closed-loop feedback information to adjust its bid prices. After a robot has completed a task, it assesses its performance to reflect the discrepancy between the bid price and the actual cost of the task. A series of such performance records, with time-discounting factors, are taken into account to damp out fluctuations of bid adjustments. Adopting this adjustment mechanism, a task would be more likely allocated to a competent robot that submits a more accurate bid price, and hence improve the overall team performance. Simulation of task allocation of automated guided vehicles serving at a container terminal is presented to demonstrate the effectiveness of the bid adjustment mechanism. | - |
dc.language | eng | en_US |
dc.publisher | Springer Netherlands. The Journal's web site is located at http://www.springer.com/series/7818 | en_US |
dc.relation.ispartof | Lecture Notes in Electrical Engineering | en_US |
dc.rights | The original publication is available at www.springerlink.com | - |
dc.title | A Closed-Loop Bid Adjustment Method of Dynamic Task Allocation of Robots | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Choi, SH: shchoi@hkucc.hku.hk | en_US |
dc.identifier.authority | Choi, SH=rp00109 | en_US |
dc.identifier.doi | 10.1007/978-1-4614-2317-1_7 | en_US |
dc.identifier.scopus | eid_2-s2.0-84865479022 | - |
dc.identifier.hkuros | 232421 | en_US |
dc.identifier.volume | 130 | en_US |
dc.identifier.spage | 81 | - |
dc.identifier.epage | 94 | - |
dc.publisher.place | Netherlands | - |
dc.identifier.issnl | 1876-1100 | - |