机器人可实时调整路径，安全高效应对人类行为变化。
Reactive and Safety-Aware Path Replanning for Collaborative Applications
- 结合实时重规划与安全成本函数，动态响应人类状态变化。
- 相比传统方法效率提升最高达60%，且无需减速。
- 适合需要高安全性和实时响应的协作场景。
本文针对人机协作场景中的运动重规划问题，强调反应性与安全合规的高效性。现有以人为本的运动规划器在结构化环境中表现良好，但面对不可预测的人类行为时，常需采取保守安全措施，限制了机器人的性能与吞吐量。本研究结合反应式路径重规划与安全感知代价函数，使机器人能根据人类状态变化动态调整路径。该方法显著降低执行时间，减少轨迹减速需求，同时保障安全性。仿真与真实实验表明，相较于标准人机协作方法，本方案效率提升最高达60%。
原文摘要 · Abstract (English)
This paper addresses motion replanning in human-robot collaborative scenarios, emphasizing reactivity and safety-compliant efficiency. While existing human-aware motion planners are effective in structured environments, they often struggle with unpredictable human behavior, leading to safety measures that limit robot performance and throughput. In this study, we combine reactive path replanning and a safety-aware cost function, allowing the robot to adjust its path to changes in the human state. This solution reduces the execution time and the need for trajectory slowdowns without sacrificing safety. Simulations and real-world experiments show the method's effectiveness compared to standard human-robot cooperation approaches, with efficiency enhancements of up to 60\%.
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