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

2026-02-10

AI Summary

This work addresses the challenge of simultaneously achieving collision avoidance and payload swing suppression for forestry cranes operating in dynamic, unstructured environments—a task typically handled by decoupled approaches in existing methods. The paper presents the first unified model predictive control (MPC) framework that integrates obstacle avoidance and swing damping. By online constructing a Euclidean Distance Field (EDF), the method directly embeds real-time LiDAR-based environmental perception into the control optimization, enabling joint handling of collision constraints and swing dissipation. The proposed approach supports dynamic replanning and safe emergency stopping, and its effectiveness is validated on a real forestry crane, demonstrating robust safety under disturbances, strong environmental adaptability, and coordinated control of both payload swing and obstacle avoidance.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

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.

Problem

• collision avoidance
• payload sway damping
• forestry crane
• safe navigation
• model predictive control

Innovation

• model predictive control
• collision avoidance
• sway damping
• Euclidean distance field
• forestry crane