基于自适应卡尔曼滤波的侧滑移动机器人运动模型估计

doi:10.11999/JEIT150289

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Abstract

Exact and real-time kinematics model plays a very important role in the mobile robot motion control and path planning. Compared to the off-line model estimation, based on an Instantaneous Centers of Rotation (ICRs) based kinematic model of skid-steering, an Extend Kalman Filter (EKF) method is used to estimate ICRs values on specific terrain on line. Terrains are identified by introducing k-Nearest Neighbors (kNN) algorithm when the robot moves on different terrains. Based on terrain classification, an Adaptive Kalman Filter (AKF) is used to adjust the filter parameters. The simulation and experiment results show that this method can converge very fast and estimate the ICRs value accurately with 3 seconds.

Key words： Mobile robot Skid-steering Adaptive Kalman Filter (AKF) Instantaneous Centers of Rotation (ICRs) k-Nearest Neighbors (kNN)

Received: 09 March 2015 Published: 13 October 2015

PACS:

TP242

Fund:

The National 863 Program of China (2011AA 040202)

Corresponding Authors: Wu Yao E-mail: wuyaongyao@126.com

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	Wu Yao
	Wang Tian-miao
	Wang Xiao-gang
	Liu Miao

Cite this article:

Wu Yao,Wang Tian-miao,Wang Xiao-gang等. Kinematics Model Prediction of Skid-steering Robot Using Adaptive Kalman Filter Estimation[J]. JEIT, 2015, 37(12): 3016-3024.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT150289 OR http://jeit.ie.ac.cn/EN/Y2015/V37/I12/3016

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