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.

翻译：林业在社会中发挥着至关重要的作用，创造了巨大的生态、经济和娱乐价值。高效的森林管理涉及劳动密集且复杂的作业。为维持森林健康和生产力的关键任务之一是选择性疏伐，这需要熟练的操作员移除特定树木，为剩余树木创造最佳生长条件。在本研究中，我们提出了一种基于小型机器人采伐机（SAHA）的解决方案，旨在通过监督式自主执行此任务。我们在一个4.5吨的采伐机平台上进行构建，并实施了用于感知和自动控制的关键硬件改造。我们采用基于学习和基于模型的方法，以实现液压执行器的精确控制、在杂乱环境中的准确导航、鲁棒的状态估计以及对地形可通行性的可靠语义估计。通过集成感知、规划与控制领域的最新技术，我们的机器人采伐机能够自主导航森林环境，并抵达目标树木进行选择性疏伐。我们展示了在北欧森林中进行长达数公里的自主任务广泛实地试验的实验结果，证明了该采伐机在真实森林中的作业能力。我们分析了其性能，并总结了推进机器人森林管理的经验教训。