Cloud Computing Fuzzy Adaptive Predictive Control for Mobile Robots

Abstract

The purpose of this paper is on the use of cloud computing for efficiently planning autonomous real-time prespecified trajectory tracking and obstacle avoidance control for an omnidirectional wheeled robot using fuzzy adaptive predictive control algorithm. The autonomous trajectory tracking control includes dynamic simulation, omnidirectional wheeled robot control, and the feedback signal mainly provided by the sensor object surface and depth measurement. The robot is equipped with three independent-driven omnidirectional wheels and six ultrasonic sensors. The Jacobian between Cartesian space corresponding to the joint space of the robot is setup for ellipse motion planning so that it can autonomously follow the prespecified trajectory tracking, obstacle avoidance, and other sports. An architecture is setup to split computation between the remote cloud and the robot so that a robot can interact with a computing cloud. Given this robot/cloud architecture, the stability of the closed-loop control system from the Lyapunov theorem for the fuzzy adaptive predictive control algorithm and trajectory planning is guaranteed with satisfactory performance on the cloud during a periodically updated preprocessing phase efficiently, and manipulation queries on the robots given changes in the workspace can achieve real-time trajectory tracking and obstacle avoidance with ellipse motion planning control. Finally, tradeoffs arising between path quality and computational efficiency are evaluated through simulation, and experiments are given for analyzing the control performance.