[1]翟永杰,王家豪,张鑫,等.基于语义分割视觉伺服的种苗自动夹取系统设计[J].智能系统学报,2023,18(6):1259-1267.[doi:10.11992/tis.202212026]
ZHAI Yongjie,WANG Jiahao,ZHANG Xin,et al.Design of automatic picking system for seedlings based on semantic segmentation visual servo[J].CAAI Transactions on Intelligent Systems,2023,18(6):1259-1267.[doi:10.11992/tis.202212026]
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ZHAI Yongjie,WANG Jiahao,ZHANG Xin,et al.Design of automatic picking system for seedlings based on semantic segmentation visual servo[J].CAAI Transactions on Intelligent Systems,2023,18(6):1259-1267.[doi:10.11992/tis.202212026]
基于语义分割视觉伺服的种苗自动夹取系统设计
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第6期
页码:
1259-1267
栏目:
学术论文—机器人
出版日期:
2023-11-05
- Title:
- Design of automatic picking system for seedlings based on semantic segmentation visual servo
- Keywords:
- semantic segmentation; visual servo; seedling; grasping point localization; grasping system; robot arm; intelligent robotic grasping; deep learning
- 分类号:
- TP241.2；S126
- 摘要:
- 现代植物组织培养是一项耗时费力的工作，工作强度大，工作内容单调，为了减少劳动力成本并提高产量，设计了一种基于语义分割视觉伺服的种苗自动夹取系统并进行了测试。首先，提出了一种基于DP-BiSeNetV2语义分割算法的视觉定位方法，确定了根上合适的夹取点；之后，设计开发并测试了一种适合于实际工作环境的夹取装置；最后，将视觉定位算法与机器人夹取装置集成，构建了一个种苗自动夹取系统。在实验环节，使用蝴蝶兰种苗数据集进行了测试，在语义分割实验中，DP-BiSeNetV2模型的平均交并比为63.51%，像素准确度为98.25%；在夹取实验中，夹取成功率为81.7%。实验结果表明，该自动夹取系统具有很高的潜力，可以满足植物组织培养生产线的移植需求。
- Abstract:
- Modern plant tissue culture is a time-consuming and labor-intensive task with monotonous work. An automatic seedling clamping system based on semantic segmentation visual servo was designed and tested to reduce labor costs and increase production. First, a vision localization method was proposed based on the DP-BiSeNetV2 semantic segmentation algorithm to determine the appropriate clamping point on the root. Further, a clamping device suitable for the actual working environment was designed, developed, and tested. Finally, an automatic seedling clamping system was constructed by integrating the vision localization algorithm with the robot clamping device. In the experimental session, tests were conducted using the Phalaenopsis seedling dataset. In the semantic segmentation experiment, the mIoU and pixel accuracy of the DP-BiSeNetV2 model were 63.51% and 98.25%, respectively. Furthermore, the success rate was 81.7% in the clamping experiment. Experimental results show that the automatic clamping system has a large potential to meet the transplantation requirements of plant tissue culture production lines.
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备注/Memo
收稿日期:2022-12-26。
基金项目:国家自然科学基金联合基金重点支持项目（U21A20486）；中国科学院自动化研究所复杂系统管理与控制国家重点实验室开放课题（20220102）.
作者简介:翟永杰,教授,博士,主要研究方向为电力视觉。主持国家自然科学基金面上项目2项、河北省自然科学基金项目2项、横向科研项目20余项。编著1部,参编教材1部、著作3部。发表学术论文30余篇;王家豪,硕士研究生,主要研究方向为电力视觉与智能机器人;徐大伟,讲师,博士,主要研究方向为机器人。参与国家重点研发计划项目1项、国家自然科学基金面上项目1项,主持横向科研项目2项
通讯作者:徐大伟.E-mail:xudawei@ncepu.edu.cn
更新日期/Last Update:
1900-01-01