A Robotic Complex Control Method Based on Deep Reinforcement Learning of Recurrent Neural Networks for Automatic Harvesting of Greenhouse Crops
- DOI
- 10.2991/aisr.k.201029.064How to use a DOI?
- Keywords
- deep reinforcement learning, recurrent neural networks, recurrent Q-networks, automation and robotics, decision-making
- Abstract
The modern development of technology determines the feasibility of the transition in agriculture from manual labor to automatic production. One of the promising areas is the automation of growing vegetable crops in greenhouse complexes. Necessary factors for intensive plant growth and unfavorable for human health, such as high temperature and humidity, as well as an atmosphere saturated with chemicals, make the task of robotizing agricultural operations urgent in this area. The method for controlling a robotic complex for automatic fruit collection in greenhouse complexes is proposed. Work in greenhouse complexes is characterized as non-deterministic and with partial observability of the environment; therefore, the deep recurrent neural network DRQN was used as the basis for the method of controlling the robotic complex. Deep learning with reinforcement was used for optimizing its weights. The presented simulation results demonstrate the efficiency of the proposed method and the need for its further development.
- Copyright
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Vyacheslav Petrenko AU - Fariza Tebueva AU - Vladimir Antonov AU - Mikhail Gurchinsky PY - 2020 DA - 2020/11/10 TI - A Robotic Complex Control Method Based on Deep Reinforcement Learning of Recurrent Neural Networks for Automatic Harvesting of Greenhouse Crops BT - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020) PB - Atlantis Press SP - 340 EP - 346 SN - 1951-6851 UR - https://doi.org/10.2991/aisr.k.201029.064 DO - 10.2991/aisr.k.201029.064 ID - Petrenko2020 ER -