Kinematics Model Prediction of Skid-steering Robot Using Adaptive Kalman Filter Estimation

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

doi:10.11999/JEIT150289

Volume 37 Issue 12

Jan. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(12): 3016-3024

Wu Yao, Wang Tian-miao, Wang Xiao-gang, Liu Miao. Kinematics Model Prediction of Skid-steering Robot Using Adaptive Kalman Filter Estimation[J]. Journal of Electronics & Information Technology, 2015, 37(12): 3016-3024. doi: 10.11999/JEIT150289

Citation:

Wu Yao, Wang Tian-miao, Wang Xiao-gang, Liu Miao. Kinematics Model Prediction of Skid-steering Robot Using Adaptive Kalman Filter Estimation[J]. Journal of Electronics & Information Technology, 2015, 37(12): 3016-3024. doi: 10.11999/JEIT150289

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Kinematics Model Prediction of Skid-steering Robot Using Adaptive Kalman Filter Estimation

doi: 10.11999/JEIT150289

Funds:

The National 863 Program of China (2011AA 040202)

Received Date: 2015-03-09
Rev Recd Date: 2015-09-09
Publish Date: 2015-12-19

Abstract

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

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References(29)

References

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