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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

吴耀, 王田苗, 王晓刚, 刘淼. 基于自适应卡尔曼滤波的侧滑移动机器人运动模型估计[J]. 电子与信息学报, 2015, 37(12): 3016-3024. doi: 10.11999/JEIT150289
引用本文: 吴耀, 王田苗, 王晓刚, 刘淼. 基于自适应卡尔曼滤波的侧滑移动机器人运动模型估计[J]. 电子与信息学报, 2015, 37(12): 3016-3024. doi: 10.11999/JEIT150289
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

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)

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

Funds: 

The National 863 Program of China (2011AA 040202)

  • 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。
  • Kozlowski K and Pazderski D. Modeling and control of a 4-wheel skid-steering mobile robot[J]. International Journal of Applied Mathematics and Computer Science, 2004, 12(4): 477-496.
    Yu W, Chuy O, Collins E G, et al.. Dynamic modeling of a skid-steered wheeled vehicle with experimental verification[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA, 2009: 4211-4219.
    Yu W, Chuy O, Collins E G, et al.. Analysis and experimental verification for dynamic modeling of a skid-steered wheeled vehicle[J]. IEEE Transactions on Robotics, 2010, 26(2): 340-353.
    Yi J, Zhang J, Song D, et al.. IMU-based localization and slip estimation for skid-steered mobile robots[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA, 2007: 2845-2850.
    Wong J and Chiang C. A general theory for skid steering of tracked vehicles on firm ground[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2001, 215(3): 343-355.
    Le A, Rye D, and Durrant-Whyte H. Estimation of track-soil interactions for autonomous tracked vehicles[C]. IEEE International Conference on Robotics Automation, Albuquerque, NM, USA, 1997: 1388-1393.
    Ani O A, Xu He, Xue Kai, et al.. Analytical modeling and multi?objective optimization (MOO) of slippage for wheeled mobile robot (WMR) in rough terrain[J]. Journal of Central South University, 2012, 19(9): 2458-2467.
    Ani O A, Xu He, Shen Yi-ping, et al.. Modeling and multiobjective optimization of traction performance for autonomous wheeled mobile robot in rough terrain[J]. Journal of Zhejiang University, 2013, 14(1): 11-29.
    赵磊, 王鸿鹏, 董良, 等. 一种基于动力学模型的高速轮式移动机器人漂移运动控制方法[J]. 机器人, 2014, 36(2): 137-146.
    Zhao Lei, Wang Hong-peng, Dong Liang, et al.. A drift control method for high-speed wheeled mobile robot based on dynamic model[J]. ROBOT, 2014, 36(2): 137-146.
    Zhang Yu, Hu Ji-bin, Li Xue-yuan, et al.. A linear lateral dynamic model of skid steered wheeled vehicle[C]. IEEE Intelligent Vehicles Symposium, Gold Coast, Australia, 2013: 964-969.
    Ni Jun and Hu Ji-bin. The research of steady-state and transient-state response of skid steering wheeled vehicle[C]. IEEE Transportation Electrification Conference Expo, Beijing, China, 2014: 1-6.
    Wu Yao, Wang Tian-miao, Liang Jian-hong, et al.. Experimental kinematics modeling estimation for wheeled skid-steering mobile robots[C]. IEEE International Conference on Robotics and Biomimetics, Shenzhen, China, 2014: 268-273.
    Rogers-Marcovitz F, George M, Seegmiller N, et al.. Aiding off-road inertial navigation with high performance models of wheel slip[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Portugal, 2012: 215-222.
    Moosavian S A A and Kalantari A. Experimental slip estimation for exact kinematics modeling and control of a tracked mobile robot[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, 2008: 95-100.
    Martnez J, Mandow A, Morales J, et al.. Approximating kinematics for tracked mobile robots[J]. The International Journal of Robotics Research, 2005, 24(10): 867-878.
    杨云, 王鸿鹏, 刘景泰, 等. 四轮独立驱动式移动机器人的运动学分析与仿真[C]. 第30届中国控制会议, 烟台, 2011: 3958-3963.
    Yang Yun, Wang Hong-peng, Liu Jing-tai, et al.. The kinematic analysis and simulation for four-wheel independent drive mobile robot[C]. Proceedings of the 30th Chinese Control Conference, Yantai, China, 2011: 3958-3963.
    Pentzer J, Brennan S, and Reichard K. Model-based prediction of skid-steer robot kinematics using online estimation of track instantaneous centers of rotation[J]. Journal of Field Robotics, 2014, 31(3): 455-476.
    蒋恩松, 李孟超, 孙刘杰. 一种基于神经网络的卡尔曼滤波改进方法[J]. 电子与信息学报, 2007, 29(9): 2073-2076.
    Jiang En-song, Li Meng-chao, and Sun Liu-jie. An improved method of kalman filter based on neural network[J]. Journal of Electronics Information Technology, 2007, 29(9): 2073-2076.
    徐定杰, 沈忱, 沈锋. 时变有色观测噪声下基于变分贝叶斯学习的自适应卡尔曼滤波[J]. 电子与信息学报, 2013, 35(7): 1593-1598.
    Xu Ding-jie, Shen Chen, and Shen Feng. Adaptive kalman filtering with time-varying colored measurement noise by variational bayesian learning [J]. Journal of Electronics Information Technology, 2013, 35(7): 1593-1598.
    李强. 基于振动信号的轮式移动机器人地面分类方法研究[D]. [博士论文], 哈尔滨工程大学, 2013.
    Li Qiang. Research on terrain classification methods for wheeled robots based on vibration signals[D]. [Ph.D. dissertation], Harbin Engineering University, 2013.
    Reinstein M, Kubelka V, and Zimmermann K. Terrain adaptive odometry for mobile skid-steer robots[C]. IEEE International Conference on Robotics Automation, Karlsruhe, Germany, 2013: 4691-4696.
    Weiss C, Fechner N, Stark M, et al.. Comparison of different approaches to vibration-based terrain classification[C]. The European Conference on Mobile Robots, Freiburg, Germany, 2007.
    Tick D, Rahman T, Busso C, et al.. Indoor robotic terrain classification via angular velocity based hierarchical classifier selection[C]. IEEE International Conference on Robotics Automation, River Centre, Saint Paul, Minnesota, USA, 2012: 3594-3600.
    Dorf R C and Bishop R H. Modern Control System[M]. 12th Edition. Upper Saddle River, NJ, US, Prentice Hall, Inc., 2010: 234-235.
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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

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