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Boosting Cost-Efficiency in Robotics: A Distributed Computing Approach for Harvesting Robots

文献类型： 外文期刊

作者： Xie, Feng 1 ; Li, Tao 2 ; Feng, Qingchun 2 ; Zhao, Hui 4 ; Chen, Liping 3 ; Zhao, Chunjiang 3 ;

作者机构： 1.Jiangsu Univ, Sch Agr Engn, Zhenjiang, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing, Peoples R China

3.Beijing Key Lab Intelligent Equipment Technol Agr, Beijing, Peoples R China

4.Tianjin Univ Technol, Sch Elect Engn & Automat, Tianjin, Peoples R China

关键词： 5G network; computation allocation; edge computing; harvesting robot; visual system

期刊名称：JOURNAL OF FIELD ROBOTICS （ 影响因子：5.2； 五年影响因子：7.5 ）

ISSN： 1556-4959

年卷期： 2025 年 42 卷 5 期

页码：

收录情况： SCI

摘要： Multi-arm harvesting robots offer a promising solution to the labor shortage in fruit harvesting, due to their ability to improve harvesting efficiency. However, multi-arm harvesters necessitate additional visual sensors to acquire distribution information of fruits within larger working spaces. Greater demands are consequently imposed on graphics computation, leading to increased costs in computing hardware of robot system. To balance the graphics computing cost and reduce energy consumption, distributed graphics computation frameworks for multi-arm robot vision system are proposed in this study. First, a host-edge framework is proposed to assign the tasks of image inference and depth alignment to host computer and edge computing modules through a decentralized mode of local connection. Moreover, to increase the endurance time of robot in application, the edge computing modules are reduced and the fifth generation mobile communication is integrated into robot graphics computing system to transfer on-board image processing to a remote computing server with MQTT protocol. To verify the effectiveness of the proposed framework, comprehensive experiments were performed, demonstrating that, compared with traditional computing framework, the proposed local distributed framework reduced 35.6% average time consumption, and over 20 FPS average processing speed can be achieve. The remote distributed framework has reduced the computational power consumption of the on-board system by approximately 23.1% while ensuring the performance is not lower than the local distributed framework. Finally, by discussing the two frameworks in terms of stability and cost, we present the commercial viability for the application of multi-arm harvesting robot.

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