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.

翻译：林业起重机在动态、非结构化的户外环境中作业，同时实现碰撞避免与负载摆振控制对于安全导航至关重要。现有方法分别应对这些挑战，要么专注于沿预定义无碰撞路径进行摆振抑制，要么仅在全局规划层面执行碰撞避免。我们提出了首个用于林业起重机的无碰撞摆振抑制模型预测控制器（MPC），将两个目标统一在单一控制框架中。我们的方法通过在线欧几里得距离场（EDF）将基于LiDAR的环境建图直接集成到MPC中，实现了实时环境适应。该控制器在抑制负载摆振的同时强制执行碰撞约束，使其能够：（i）在准静态环境变化时重新规划，（ii）在扰动下保持无碰撞运行，以及（iii）当不存在绕行路径时提供安全停止。在真实林业起重机上的实验验证证明了有效的摆振抑制和成功的障碍物避让。相关视频可在 https://youtu.be/tEXDoeLLTxA 查看。