Robot control interaction with cloud-assisted analysis control | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 143، دوره 13، شماره 2، مهر 2022، صفحه 1789-1794 اصل مقاله (918.57 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.27409.3590 | ||
| نویسندگان | ||
| Alaa Adeb Abdulraheem* ؛ Aqeel Abdulazeez Mohammed | ||
| Department of Electronics and Communications, College of Engineering, University of Baghdad, Baghdad, Iraq | ||
| چکیده | ||
| Path planning with avoiding obstacles autonomously with a large of computing capabilities in an unknown dynamic environment is a difficult challenge for a mobile robot to solve. This research solves this challenge by combining deep Q-network (DQN) with cloud computing. To begin, a DQN is created and trained to predict the state-action value function of a mobile robot. The information collected from the original RGB image (pixels in the image) taken from the surrounding is fed into the DQN using a cloud computing platform, which reduces the algorithms high computation complexity; Finally, the action chosen policy picks the current optimal mobile robot action. To validate the DQN algorithm, we trained the robot in a dynamic environment with a simple and complex case. The simulation results show that, in a simple case of the environment, the DQN technique can converge to explore a path with fewer steps and higher average reward than in a complicated case and find a collision-free path with an accuracy rate of 89\% in the simple case and when the environment becomes more complex, the accuracy rate is 70 %. | ||
| کلیدواژهها | ||
| cloud services؛ deep Q- learning؛ Autonomous Navigation of the robot؛ Obstacle avoidance | ||
| مراجع | ||
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