基于自适应卡尔曼滤波的侧滑移动机器人运动模型估计

吴耀; 王田苗; 王晓刚; 刘淼

doi:10.11999/JEIT150289

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

基金项目:

国家863计划(2011AA040202)

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- 被引次数: 0
出版历程
- 收稿日期: 2015-03-09
- 修回日期: 2015-09-09
- 刊出日期: 2015-12-19

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

Funds:

The National 863 Program of China (2011AA 040202)

摘要

摘要: 精确实时在线的运动模型对于侧滑移动机器人的运动控制和轨迹规划至关重要，相比于离线模型估计，该文在基于速度瞬心(ICRs)的侧滑移动机器人运动学模型基础上，采用扩展卡尔曼滤波(EKF)，在同一特定地形下在线准确得到ICRs的参数值；并针对不同的地形情况，采用k-近邻法对地形进行分类，实时判别机器人当前运行的路面，采用自适应的卡尔曼滤波器(AKF)调整滤波器参数。仿真和实验对比表明，该方法在同一地形和变化地形下均能快速估计出侧滑移动机器人的运动学模型，收敛时间均为3 s以内，可以满足实际使用的需要。
- 移动机器人 /
- 侧滑移动 /
- 自适应卡尔曼滤波 /
- 速度瞬心 /
- k-近邻法
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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参考文献(29)

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