Peduncle collision-free grasping based on deep reinforcement learning for tomato harvesting robot

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成果类型：

期刊论文

作者：

Li, Yajun;Feng, Qingchun;Zhang, Yifan;Peng, Chuanlang;Ma, Yuhang;...

通讯作者：

Feng, QC

作者机构：

[Ru, Mengfei; Peng, Chuanlang; Feng, Qingchun; Zhao, Chunjiang; Zhang, Yifan; Li, Yajun; Ma, Yuhang; Liu, Cheng; Sun, Jiahui] Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China.

[Li, Yajun] Hunan Agr Univ, Coll Mech & Elect Engn, Changsha 410128, Peoples R China.

[Feng, Qingchun; Zhao, Chunjiang] Beijing Key Lab Intelligent Equipment Technol Agr, Beijing 100097, Peoples R China.

[Feng, Qingchun; Feng, QC] Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, 11 Shuguang Garden Middle Rd, Beijing 100097, Peoples R China.

通讯机构：

[Feng, QC ] B

Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, 11 Shuguang Garden Middle Rd, Beijing 100097, Peoples R China.

语种：

英文

关键词：

Collision-free;Deep reinforcement learning;Opimal operating posture;Tomato-harvesting robot

期刊：

Computers and Electronics in Agriculture

ISSN：

0168-1699

年：

2024

卷：

216

页码：

108488

DOI：

10.1016/j.compag.2023.108488

基金类别：

CRediT authorship contribution statement Yajun Li: Conceptualization, Data curation, Investigation, Software, Visualization, Writing – original draft. Qingchun Feng: Conceptualization, acquisition, Investigation, Project administration, Supervision, Writing – review & editing. Yifan Zhang: Investigation, Methodology, Software. Chuanlang Peng: Data curation, Formal analysis. Yuhang Ma: Supervision, Validation. Cheng Liu: Conceptualization, Investigation, Software. Mengfei Ru: Methodology. Jiahui Sun: Investigation. Chunjiang

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本校为其他机构

摘要：

Collision-free grasping of the thin, brief peduncles connecting cherry tomato clusters to the main stem was crucial for tomato harvesting robots. Recognizing that the optimal operating posture for each individual peduncle was various, this study proposed a novel peduncle grasping posture decision model using deep reinforcement learning (DRL) for tomato harvesting manipulators, to overcome the collision issue caused by fixed-posture grasping. This model could dynamically generated action sequences for the harvesting manipulator, ensuring that the end-effector approach to the peduncle along the ...

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Peduncle collision-free grasping based on deep reinforcement learning for tomato harvesting robot

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