提出混合任务规划方法，让人机协作更鲁棒且可适应变化。
Adaptive Human-Robot Collaborative Missions using Hybrid Task Planning
- 分两步求解：先找可行方案，再增不确定性验证并生成最优解集
- 在葡萄园案例中实现高效可行解，性能优于传统规划器
- 适合需要动态调整的工业人机协作场景
在人机协作任务中，生成稳健的任务计划是提高任务成功率的关键。尽管该领域已有大量研究，涵盖各类约束与不确定性，但其应用仍局限于可通过数学或启发式求解器处理的简单问题。本文提出一种混合方法，将任务规划分解为两个相互关联的部分：首先识别可行计划，随后通过不确定性增强与验证，生成一组帕累托最优计划。为提升鲁棒性，还设计了应对系统需求与代理能力变化的自适应策略。我们在一个涉及工人与机器人在葡萄园内协作的工业案例中验证了该方法，结果表明，相比原生规划器，该方法在生成可行解和可扩展性方面均具显著优势。
原文摘要 · Abstract (English)
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.
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