Ma, Song;
Ruan, Jingqing;
Du, Yali;
Bucknall, Richard;
Liu, Yuanchang;
(2022)
An End-to-End Task Allocation Framework for Autonomous Mobile Systems.
In:
UKRAS22 Conference "Robotics for Unconstrained Environments" Proceedings.
EPSRC UK-RAS Network: Aberystwyth, UK.
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Abstract
This work aims to unravel the problem of task allocation and planning for multi-agent systems with a particular interest in promoting adaptability. We proposed a novel end-to-end task allocation framework employing reinforcement learning methods to replace the handcrafted heuristics used in previous works. The proposed framework achieves high adaptability and also explores more competitive results. Learning experiences from the feedback help to reach the advantages. The systematic objectives are adjustable and responsive to the reward design intuitively. The framework is validated in a set of tests with various parameter settings, where adaptability and performance are demonstrated.
| Type: | Proceedings paper |
|---|---|
| Title: | An End-to-End Task Allocation Framework for Autonomous Mobile Systems |
| Event: | UKRAS22 Conference "Robotics for Unconstrained Environments" |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.31256/Sc6Do6C |
| Publisher version: | http://doi.org/10.31256/Sc6Do6C |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | task allocation, autonomous system, reinforcement learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159398 |
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