Semnan University International Journal of Nonlinear Analysis and Applications 2008-6822 13 2 2022 07 01 Robot control interaction with cloud-assisted analysis control 1789 1794 6579 10.22075/ijnaa.2022.27409.3590 EN Alaa Adeb Abdulraheem Department of Electronics and Communications, College of Engineering, University of Baghdad, Baghdad, Iraq Aqeel Abdulazeez Mohammed Department of Electronics and Communications, College of Engineering, University of Baghdad, Baghdad, Iraq Journal Article 2022 02 17 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 %. https://ijnaa.semnan.ac.ir/article_6579_7156812a5d859b99899f21f684d81d3d.pdf