SAHA: 選択的間伐のための監督付き自律ハーベスタ
SAHA: Supervised Autonomous HArvester for selective forest thinning
4.5トンのハーベスタを改造し、学習とモデルベースの制御を組み合わせて、森林内を自律走行して選択的に木を伐採するシステムを開発し、北欧の森林で数キロメートルに及ぶ実証実験を行った。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Fang Nan, Meher Malladi, Qingqing Li, Fan Yang, Joonas Juola, Tiziano Guadagnino, Jens Behley, Cesar Cadena, Cyrill Stachniss, Marco Hutter
分類: cs.RO
原文アブストラクト
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.
