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Open AccessArticle

Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network

by 1,2, 1,2 and 1,2,*
1
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
2
Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(18), 3806; https://doi.org/10.3390/app9183806
Received: 27 June 2019 / Revised: 17 August 2019 / Accepted: 6 September 2019 / Published: 11 September 2019
(This article belongs to the Special Issue Intelligent Robotics)
In order to efficiently and exactly in tracking the desired path points, autonomous cleaning vehicles have to adapt their own behavior according to the perceived environmental information. This paper proposes a target points tracking control algorithm based on the Long Short-Term Memory network, which can generate the speed and yaw rate to arrive at the target point in real time. The target point is obtained by a parameter named foresight distance that is deduced based on the fuzzy control, whose inputs are the speed and yaw rate of the vehicle at the current point. The effectiveness of the proposed algorithm is illustrated by the simulation and field experiments. Compared with other classical algorithms, this algorithm can track the point sequence on straight path and multiple curvature path more accurately. The field experiment indicates the proposed controller is efficient in following the pre-defined path points, furthermore, it can make the autonomous cleaning vehicle run smoothly in the path which is disturbed by bounded disturbances. The distance errors can meet the actual requirement of the cleaning vehicle during the tracking process. View Full-Text
Keywords: target points tracking; LSTM network; fuzzy control; automatic vehicle target points tracking; LSTM network; fuzzy control; automatic vehicle
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Wang, H.; Chen, X.; Miao, Z. Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network. Appl. Sci. 2019, 9, 3806.

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