A Collision-Free Sway Damping Model Predictive Controller for Safe and Reactive Forestry Crane Navigation

TL;DR

This paper introduces a novel collision-free sway-damping model predictive controller for forestry cranes that unifies collision avoidance and payload sway control in real-time, enhancing safety and responsiveness in dynamic outdoor environments.

Contribution

It presents the first integrated MPC framework combining collision avoidance and sway damping, utilizing LiDAR-based environment mapping with online Euclidean distance fields for real-time adaptation.

Findings

Effective sway damping demonstrated in real-world tests.

Successful obstacle avoidance in dynamic environments.

Controller maintains safety under disturbances.

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 https://youtu.be/tEXDoeLLTxA.