用时序逻辑规划无人机与人协作轨迹，兼顾安全与舒适
STL-Based Motion Planning and Uncertainty-Aware Risk Analysis for Human-Robot Collaboration with a Multi-Rotor Aerial Vehicle
- 用信号时序逻辑编码安全、时间与人体工学需求
- 优化生成满足非线性动力学的可行轨迹
- 考虑人体姿态不确定性，支持在线动态重规划
本文提出一种用于多旋翼飞行器人机协作的运动规划与风险分析框架。采用信号时序逻辑（STL）编码关键任务目标，包括安全性、时间约束及人类偏好，尤其关注操作舒适性与人体工效。基于优化的规划器生成满足飞行器非线性动力学和执行器约束的动态可行轨迹。针对由此产生的非凸、非光滑优化问题，采用平滑鲁棒性近似与梯度优化技术求解。同时引入不确定性感知的风险分析方法，量化在人体姿态不确定下的规范违反概率。设计鲁棒性感知的事件触发重规划策略，在执行中保留安全裕度，实现对扰动和意外事件的在线恢复。通过MATLAB与Gazebo仿真，在模拟电力线路维护场景的物体交接任务中验证了该框架的有效性。结果表明，所提方法能在真实操作条件下实现安全、高效且具备鲁棒性的人机协作。
原文摘要 · Abstract (English)
This paper presents a motion planning and risk analysis framework for enhancing human-robot collaboration with a Multi-Rotor Aerial Vehicle. The proposed method employs Signal Temporal Logic to encode key mission objectives, including safety, temporal requirements, and human preferences, with particular emphasis on ergonomics and comfort. An optimization-based planner generates dynamically feasible trajectories while explicitly accounting for the vehicle's nonlinear dynamics and actuation constraints. To address the resulting non-convex and non-smooth optimization problem, smooth robustness approximations and gradient-based techniques are adopted. In addition, an uncertainty-aware risk analysis is introduced to quantify the likelihood of specification violations under human-pose uncertainty. A robustness-aware event-triggered replanning strategy further enables online recovery from disturbances and unforeseen events by preserving safety margins during execution. The framework is validated through MATLAB and Gazebo simulations on an object handover task inspired by power line maintenance scenarios. Results demonstrate the ability of the proposed method to achieve safe, efficient, and resilient human-robot collaboration under realistic operating conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。