Hierarchical Tri-manual Planning for Vision-assisted Fruit Harvesting with Quadrupedal Robots
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909608194342912 |
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| author | Liu, Zhichao Zhou, Jingzong Karydis, Konstantinos |
| author_facet | Liu, Zhichao Zhou, Jingzong Karydis, Konstantinos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17116 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Tri-manual Planning for Vision-assisted Fruit Harvesting with Quadrupedal Robots Liu, Zhichao Zhou, Jingzong Karydis, Konstantinos Robotics 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. |
| title | Hierarchical Tri-manual Planning for Vision-assisted Fruit Harvesting with Quadrupedal Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.17116 |