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arXiv · 2409.17116

Hierarchical Tri-manual Planning for Vision-assisted Fruit Harvesting with Quadrupedal Robots

Zhichao Liu·Jingzong Zhou·Konstantinos Karydis
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Abstract

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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Zhichao Liu, Jingzong Zhou, Konstantinos Karydis. 2024-09-25. Hierarchical Tri-manual Planning for Vision-assisted Fruit Harvesting with Quadrupedal Robots. https://arxiv.org/abs/2409.17116

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