SAHA: Supervised Autonomous HArvester for selective forest thinning
🤖 AI Summary
This work addresses the inefficiency and high reliance on skilled labor in selective thinning during forest tending by developing an autonomous system integrated onto a 4.5-ton small-scale harvester platform. Combining learning-based and model-driven approaches, the system achieves autonomous navigation in forest environments, precise hydraulic control, robust state estimation, and semantic terrain traversability analysis. It represents the first implementation of a supervised autonomous system on compact forestry machinery. Extensive field trials spanning multiple kilometers were conducted in complex Nordic forest environments, demonstrating the system’s reliable capability to navigate to target trees and perform selective thinning operations. The results significantly advance the practicality and autonomy of forestry robots in real-world operational settings.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
selective thinning
forest management
autonomous harvester
robotic forestry
supervised autonomy
Innovation
Methods, ideas, or system contributions that make the work stand out.
supervised autonomy
selective thinning
hydraulic actuator control
semantic terrain estimation
forest robotics
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