Abstract
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 this https URL.
Abstract (translated)
林业起重机在动态、无结构的户外环境中运行，其中同时避免碰撞和负载摆动控制对于安全导航至关重要。现有的方法分别解决了这些挑战，要么集中在预定义的安全路径上的摆动减缓上，要么仅在全球规划层面上进行避碰操作。我们提出了一种专为林业起重机设计的第一款无需碰撞、摆动抑制的模型预测控制器（MPC），该控制器在一个控制框架内统一了两个目标。我们的方法直接将基于LiDAR的环境映射集成到MPC中，使用在线欧几里得距离场（EDF）进行实时环境适应。此控制器同时执行避碰约束和负载摆动抑制，使其能够(i)在准静态环境变化时重新规划路径，(ii)在受到干扰的情况下保持无碰撞运行，并且(iii)当没有绕行路径时提供安全停止功能。在实际林业起重机上的实验验证显示了有效的摆动减缓及成功的障碍物规避。一段展示该成果的视频可以在以下网址找到：[此URL]。
URL
https://arxiv.org/abs/2602.10035
