Fruit Picking Robot Arm Training Solution Based on Reinforcement Learning in Digital Twin

被引：0

作者：

Tian X. ^{[1
]}

Pan B. ^{[2
]}

Bai L. ^{[1
]}

Wang G. ^{[3
]}

Mo D. ^{[1
,3
]}

机构：

[1] Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau

[2] Sichuan Digital Transportation Tech Co. Ltd., Sichuan, Chengdu

[3] School of Mechanical and Electrical Engineering, Lingnan Normal University, Guangdong, Zhanjiang

来源：

Journal of ICT Standardization | 2023年 / 11卷 / 03期

关键词：

digital twin; ML-agent; reinforcement learning; Robot arm; unity;

D O I：

10.13052/jicts2245-800X.1133

中图分类号：

学科分类号：

摘要：

In the era of Industry 4.0, digital agriculture is developing very rapidly and has achieved considerable results. Nowadays, digital agriculture-based research is more focused on the use of robotic fruit picking technology, and the main research direction of such topics is algorithms for computer vision. However, when computer vision algorithms successfully locate the target object, it is still necessary to use robotic arm movement to reach the object at the physical level, but such path planning has received minimal attention. Based on this research deficiency, we propose to use Unity software as a digital twin platform to plan the robotic arm path and use ML-Agent plug-in as a reinforcement learning means to train the robotic arm path, to improve the accuracy of the robotic arm to reach the fruit, and happily the effect of this method is much improved than the traditional method. © 2023 River Publishers.

引用

页码：261 / 282

页数：21

共 26 条

[1]

Rose D. C., Chilvers J., Agriculture 4.0: Broadening responsible innovation in an era of smart farming, Frontiers in Sustainable Food Systems, 2, (2018)

[2]

Smith M. J., Getting value from artificial intelligence in agriculture, Animal Production Science, 60, 1, pp. 46-54, (2018)

[3]

Rotz S., Duncan E., Small M., Botschner J., Dara R., Mosby I., Fraser E. D., The politics of digital agricultural technologies: a preliminary review, Sociologia Ruralis, 59, 2, pp. 203-229, (2019)

[4]

Inshakova A. O., Frolova E. E., Rusakova E. P., Kovalev S. I., The model of distribution of human and machine labor at intellectual production in industry 4.0, Journal of Intellectual Capital, 21, 4, pp. 601-622, (2020)

[5]

Daudelin J., Jing G., Tosun T., Yim M., Kress-Gazit H., Campbell M., An integrated system for perception-driven autonomy with modular robots, Science Robotics, 3, 23, (2018)

[6]

Zhang Y., Li M., Qiao J., Liu G., A segmentation algorithm for apple fruit recognition using artificial neural network, Aktualni zadaci mehanizacije poljoprivrede. Zbornik radova, 35. med̄unarodnog simpozija iz podruèja mehanizacije poljoprivrede, pp. 359-367, (2008)

[7]

Wang Z., Robot obstacle avoidance and navigation control algorithm research based on multi-sensor information fusion, 2018 11th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 351-354, (2018)

[8]

Au W., Chung H., Chen C., Path planning and assembly mode-changes of 6-DOF Stewart-Gough-type parallel manipulators, Mechanism and Machine Theory, 106, pp. 30-49, (2016)

[9]

Gomez-Bravo F., Carbone G., Fortes J. C., Collision free trajectory planning for hybrid manipulators, Mechatronics, 22, 6, pp. 836-851, (2012)

[10]

Yang H., Li L., Gao Z., Obstacle avoidance path planning of hybrid harvesting manipulator based on joint configuration space, Transactions of the Chinese Society of Agricultural Engineering, 33, 4, pp. 55-62, (2017)

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