robot control interaction with cloud-assisted analysis control

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%.