An End-to-End Task Allocation Framework for Autonomous Mobile Systems
Title: An End-to-End Task Allocation Framework for Autonomous Mobile Systems
Authors: Song Ma (University College London); Jingqing Ruan (Chinese Academy of Sciences); Yali Du (King’s College London); Richard Bucknall (University College London); Yuanchang Liu (University College London);
Year: 2022
Citation: Ma, S., Ruan, J., Du, Y., Bucknall, R., Liu, Y., (2022). An End-to-End Task Allocation Framework for Autonomous Mobile Systems. UKRAS22 Conference “Robotics for Unconstrained Environments” Proceedings, 22-23. doi: 10.31256/Sc6Do6C
Abstract:
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.