三臂四足机器人实现复杂环境自动采果，成功率超90%。
Hierarchical Tri-manual Planning for Vision-assisted Fruit Harvesting with Quadrupedal Robots
- 分层三臂规划策略，避免碰撞，提升多臂协同效率。
- 实验室单次尝试成功率达90%，野外测试验证系统鲁棒性。
- 适用于果园自动化采摘，尤其适合地形复杂的自然环境。
本文针对在复杂自然环境中高效采摘果实的挑战，提出首个三臂四足机器人 LocoHarv-3 及其分层三臂规划方法，实现无碰撞轨迹下的自动化采果。系统融合遥操作、基于激光雷达的里程计与建图，以及基于学习的视觉感知，精准检测果实位置与姿态。通过一系列受控室内实验（采用动作捕捉）和广泛的野外测试验证性能。结果显示，在实验室环境下单次尝试成功率达90%，野外测试进一步证明该系统在真实复杂场景中的稳健性与高效性。
原文摘要 · Abstract (English)
This paper addresses the challenge of developing a multi-arm quadrupedal robot capable of efficiently harvesting fruit in complex, natural environments. To overcome the inherent limitations of traditional bimanual manipulation, we introduce the first three-arm quadrupedal robot LocoHarv-3 and propose a novel hierarchical tri-manual planning approach, enabling automated fruit harvesting with collision-free trajectories. Our comprehensive semi-autonomous framework integrates teleoperation, supported by LiDAR-based odometry and mapping, with learning-based visual perception for accurate fruit detection and pose estimation. Validation is conducted through a series of controlled indoor experiments using motion capture and extensive field tests in natural settings. Results demonstrate a 90\% success rate in in-lab settings with a single attempt, and field trials further verify the system's robustness and efficiency in more challenging real-world environments.
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