一种无碰撞的摇摆阻尼模型预测控制器用于安全且反应性的林业起重机导航
A Collision-Free Sway Damping Model Predictive Controller for Safe and Reactive Forestry Crane Navigation
AI总结：
本文提出了一种无碰撞的摇摆阻尼模型预测控制器，用于林业起重机的安全导航和实时环境适应。
摘要：
林业起重机在动态、无结构的户外环境中运行，同时避免碰撞和控制负载摇摆对于安全导航至关重要。现有方法分别解决这些挑战，要么专注于预定义无碰撞路径的摇摆阻尼，要么仅在全局规划层面进行碰撞避免。我们提出了第一个无碰撞、摇摆阻尼的模型预测控制器（MPC）用于林业起重机，将这两个目标统一在一个控制框架中。我们的方法将基于LiDAR的环境映射直接整合到MPC中，使用在线欧几里得距离场（EDF）实现实时环境适应。控制器同时强制碰撞约束并阻尼负载摇摆，使其能够（i）在准静态环境变化时重新规划，（ii）在干扰下保持无碰撞操作，（iii）在无绕行路径时提供安全停止。在真实的林业起重机上进行的实验验证了有效的摇摆阻尼和成功的障碍物避免。一个视频可在https://youtu.be/tEXDoeLLTxA中找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
