SAHA: Supervised Autonomous HArvester for selective forest thinning

TL;DR

SAHA is a robotic harvester designed for autonomous selective forest thinning, integrating perception, planning, and control to operate effectively in real forest environments, aiming to improve efficiency and safety in forest management.

Contribution

This work introduces a supervised autonomous robotic harvester with hardware modifications and advanced algorithms for perception and control, enabling effective forest thinning operations.

Findings

Successful autonomous navigation in real forests over kilometer-long missions

Effective perception and semantic terrain estimation in cluttered environments

Demonstrated robustness and reliability in field trials

Abstract

Forestry plays a vital role in our society, creating significant ecological, economic, and recreational value. Efficient forest management involves labor-intensive and complex operations. One essential task for maintaining forest health and productivity is selective thinning, which requires skilled operators to remove specific trees to create optimal growing conditions for the remaining ones. In this work, we present a solution based on a small-scale robotic harvester (SAHA) designed for executing this task with supervised autonomy. We build on a 4.5-ton harvester platform and implement key hardware modifications for perception and automatic control. We implement learning- and model-based approaches for precise control of hydraulic actuators, accurate navigation through cluttered environments, robust state estimation, and reliable semantic estimation of terrain traversability. Integrating state-of-the-art techniques in perception, planning, and control, our robotic harvester can autonomously navigate forest environments and reach targeted trees for selective thinning. We present experimental results from extensive field trials over kilometer-long autonomous missions in northern European forests, demonstrating the harvester's ability to operate in real forests. We analyze the performance and provide the lessons learned for advancing robotic forest management.