首个统一基准评估机器人跟人任务中的安全与舒适性平衡。
Follow-Bench: A Unified Motion Planning Benchmark for Socially-Aware Robot Person Following
- 构建涵盖多种场景的统一仿真基准Follow-Bench。
- 对比8种跟人规划器，量化分析其安全与舒适性权衡。
- 在真实机器人上验证效果，适合研究服务机器人导航的团队。
机器人跟人（RPF）——即移动机器人跟随并协助特定人员——在个人辅助、安保巡逻、老年人照护和物流等领域有广泛应用。为有效实现，机器人需在保证目标及周围人群安全与舒适的前提下进行跟随。本文首次系统研究了RPF，(i) 梳理典型场景、运动规划方法与评估指标，重点关注安全与舒适性；(ii) 提出Follow-Bench，一个统一的仿真基准，涵盖多样的目标轨迹模式、人群动态与环境布局；(iii) 重实现8种代表性RPF规划器，确保安全与舒适性被系统考虑。此外，我们在差速驱动机器人上评估表现最佳的两种规划器，提供实际部署洞察。大量仿真与真实实验定量分析现有规划器的安全-舒适性权衡，揭示开放挑战与未来方向。
原文摘要 · Abstract (English)
Robot person following (RPF) -- mobile robots that follow and assist a specific person -- has emerging applications in personal assistance, security patrols, eldercare, and logistics. To be effective, such robots must follow the target while ensuring safety and comfort for both the target and surrounding people. In this work, we present the first comprehensive study of RPF, which (i) surveys representative scenarios, motion-planning methods, and evaluation metrics with a focus on safety and comfort; (ii) introduces Follow-Bench, a unified benchmark simulating diverse scenarios, including various target trajectory patterns, crowd dynamics, and environmental layouts; and (iii) re-implements eight representative RPF planners, ensuring that both safety and comfort are systematically considered. Moreover, we evaluate the two best-performing planners from our benchmark on a differential-drive robot to provide insights into real-world deployment of RPF planners. Extensive simulation and real-world experiments provide quantitative study of the safety-comfort trade-offs of existing planners, while revealing open challenges and future research directions.
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