首个兼顾避障与吊物摆动抑制的实时控制方法，提升林用起重机安全性。
A Collision-Free Sway Damping Model Predictive Controller for Safe and Reactive Forestry Crane Navigation
- 将激光雷达环境映射融入模型预测控制，实时生成避障路径。
- 在动态环境中实现摆动抑制与碰撞规避双重目标，稳定性能优异。
- 适合需要高安全性的户外机械导航场景，如林业作业机器人。
林用起重机在动态非结构化户外环境中运行，同时实现避障与吊载摆动抑制对安全导航至关重要。现有方法多分别处理这两项挑战：或专注于摆动抑制并预设无碰撞路径，或仅在全局规划层面进行避障。本文提出首个统一避障与摆动抑制目标的模型预测控制器（MPC），首次将基于激光雷达的环境地图直接集成至MPC中，利用在线欧氏距离场（EDF）实现环境实时适应。该控制器可同步施加碰撞约束并抑制吊载摆动，具备三项能力：(i) 对准静态环境变化进行重规划；(ii) 在扰动下保持无碰撞运行；(iii) 当无绕行路径时实现安全停止。在真实林用起重机上的实验验证了其优秀的摆动抑制效果和可靠的障碍物规避能力。视频演示见 https://youtu.be/tEXDoeLLTxA。
原文摘要 · Abstract (English)
Forestry cranes operate in dynamic, unstructured outdoor environments where simultaneous collision avoidance and payload sway control are critical for safe navigation. Existing approaches address these challenges separately, either focusing on sway damping with predefined collision-free paths or performing collision avoidance only at the global planning level. We present the first collision-free, sway-damping model predictive controller (MPC) for a forestry crane that unifies both objectives in a single control framework. Our approach integrates LiDAR-based environment mapping directly into the MPC using online Euclidean distance fields (EDF), enabling real-time environmental adaptation. The controller simultaneously enforces collision constraints while damping payload sway, allowing it to (i) replan upon quasi-static environmental changes, (ii) maintain collision-free operation under disturbances, and (iii) provide safe stopping when no bypass exists. Experimental validation on a real forestry crane demonstrates effective sway damping and successful obstacle avoidance. A video can be found at https://youtu.be/tEXDoeLLTxA.
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