通过预测人类意图动态调整机器人路径，实现更安全高效的人机协同操作。
A Shared Control Framework for Mobile Robots with Planning-Level Intention Prediction
- 基于意图域约束的路径重规划，实时响应人类运动意图。
- 仿真训练无需真人数据，降低部署成本且提升泛化能力。
- 实测显示工作负荷显著降低，安全性和任务效率优于传统方法。
在移动机器人共控中，准确理解人类运动意图对实现无缝人机协作至关重要。本文提出一种新型共控框架，引入规划级意图预测机制。设计路径重规划算法，根据推断出的人类意图动态调整机器人的期望轨迹。为表征未来运动意图，提出“意图域”概念，作为路径重规划的约束条件。将意图域预测与路径重规划联合建模为马尔可夫决策过程，通过深度强化学习求解。此外，开发了一种基于Voronoi的人类轨迹生成算法，使模型可在纯仿真环境下训练，无需真实人类参与或示范数据。大量仿真与真实用户实验表明，该方法显著降低操作员工作负荷，提升安全性，同时不牺牲任务效率，优于现有辅助遥操作方案。
原文摘要 · Abstract (English)
In mobile robot shared control, effectively understanding human motion intention is critical for seamless human-robot collaboration. This paper presents a novel shared control framework featuring planning-level intention prediction. A path replanning algorithm is designed to adjust the robot's desired trajectory according to inferred human intentions. To represent future motion intentions, we introduce the concept of an intention domain, which serves as a constraint for path replanning. The intention-domain prediction and path replanning problems are jointly formulated as a Markov Decision Process and solved through deep reinforcement learning. In addition, a Voronoi-based human trajectory generation algorithm is developed, allowing the model to be trained entirely in simulation without human participation or demonstration data. Extensive simulations and real-world user studies demonstrate that the proposed method significantly reduces operator workload and enhances safety, without compromising task efficiency compared with existing assistive teleoperation approaches.
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