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A novel distributed scheduling algorithm for time-critical multi-agent systems
conference contribution
posted on 2015-09-23, 14:52 authored by Amanda Whitbrook, Qinggang MengQinggang Meng, Paul ChungThis paper describes enhancements made to the
distributed performance impact (PI) algorithm and presents
the results of trials that show how the work advances the stateof-
the-art in single-task, single-robot, time-extended, multiagent
task assignment for time-critical missions. The
improvement boosts performance by integrating the
architecture with additional action selection methods that
increase the exploratory properties of the algorithm (either soft
max or ε-greedy task selection). It is demonstrated empirically
that the average time taken to perform rescue tasks can reduce
by up to 8% and solution of some problems that baseline PI
cannot handle is enabled. Comparison with the consensusbased
bundle algorithm (CBBA) also shows that both the
baseline PI algorithm and the enhanced versions are superior.
All test problems center around a team of heterogeneous,
autonomous vehicles conducting rescue missions in a 3-
dimensional environment, where a number of different tasks
must be carried out in order to rescue a known number of
victims that is always more than the number of available
vehicles.
Funding
This work was supported by EPSRC (grant number EP/J011525/1) with BAE Systems as the leading industrial partner.
History
School
- Science
Department
- Computer Science
Published in
IROS 2015Citation
WHITBROOK, A., MENG, Q. and CHUNG, P.W.H., 2015. A novel distributed scheduling algorithm for time-critical multi-agent systems. Presented at: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 28th Sept. to 2nd Oct. pp.6451-6488.Publisher
IEEEVersion
- AM (Accepted Manuscript)
Publisher statement
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2015Notes
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Language
- en