Vazquez Flores, Gricel, Evangelidis, Alexandros, Shahbeigi Roudposhti, Sepeedeh et al. (1 more author) (2025) Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning. In: 20th International Conference on Software Engineering for Adaptive and Self-Managing Systems. 20th International Conference on Software Engineering for Adaptive and Self-Managing Systems, 28-29 Apr 2025 , CAN (In Press)
Abstract
Producing robust task plans in human-robot collaborative missions is a critical activity in order to increase the likelihood of these missions completing successfully. Despite the broad research body in the area, which considers different classes of constraints and uncertainties, its applicability is confined to relatively simple problems that can be comfortably addressed by the underpinning mathematically-based or heuristic-driven solver engines. In this paper, we introduce a hybrid approach that effectively solves the task planning problem by decomposing it into two intertwined parts, starting with the identification of a feasible plan and followed by its uncertainty augmentation and verification yielding a set of Pareto optimal plans. To enhance its robustness, adaptation tactics are devised for the evolving system requirements and agents’ capabilities. We demonstrate our approach through an industrial case study involving workers and robots undertaking activities within a vineyard, showcasing the benefits of our hybrid approach both in the generation of feasible solutions and scalability compared to native planners.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 13 Feb 2025 05:22 |
Last Modified: | 19 Feb 2025 17:10 |
Status: | In Press |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223260 |
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