|Multi-layer Feed-forward Neural Network Deep Learning Control with Hybrid Position and Virtual-force Algorithm for Mobile Robot Obstacle Avoidance
Wei Zheng*, Hong-BinWang, Zhi-Ming Zhang, Ning Li, and Peng-Heng Yin
International Journal of Control, Automation, and Systems, vol. 17, no. 4, pp.1007-1018, 2019
Abstract : "This paper addresses the trajectory tracking and obstacle avoidance control problems for a class of
mobile robot systems. Two classes of controllers are designed for the mobile robot system in the free motion,
respectively. A new hybrid position virtual-force controller is designed to adjust the distance between the mobile
robot and the obstacles. Since the uncertainties between the mobile robot dynamics model and obstacles degrade
the performance of the obstacle avoidance system, a multi-layer feed-forward neural networks (NNs) deep learning
method with hybrid position and virtual-force is proposed, such that the distance between the mobile robot and
the obstacles converges to an adjustable bounded region. It is shown that the proposed controller in this paper
is smooth, effective, and only uses the system output. The control design conditions are relaxed because of the
developed multi-layer feed-forward NNs deep learning compensator. The simulation results and obstacle avoidance
cases are performed to show the effectiveness of the proposed method."
"Avoidance obstacles, mobile robot, multi-layer feed-forward neural networks, position control, virtualforce control."
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