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Motion Predicting of Autonomous Tracked Vehicles with Online Slip Model Identification

机译：在线滑行模型辨识的无轨履带车辆运动预测

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摘要

Precise understanding of the mobility is essential for high performance autonomous tracked vehicles in challenging circumstances, though the complex track/terrain interaction is difficult to model. A slip model based on the instantaneous centers of rotation (ICRs) of treads is presented and identified to predict the motion of the vehicle in a short term. Unlike many research studies estimating current ICRs locations using velocity measurements for feedback controllers, we focus on predicting the forward trajectories by estimating ICRs locations using position measurements. ICRs locations are parameterized over both tracks rolling speeds and the kinematic parameters are estimated in real time using an extended Kalman filter (EKF) without requiring prior knowledge of terrain parameters. Simulation results verify that the proposed algorithm performs better than the traditional method when the pose measuring frequencies are low. Experiments are conducted on a tracked vehicle with a weight of 13.6 tons. Results demonstrate that the predicted position and heading errors are reduced by about 75% and the reduction of pose errors is over 24% in the absence of the real-time kinematic global positioning system (RTK GPS).
机译：尽管很难对复杂的履带/地形交互进行建模，但对具有挑战性的情况下的高性能自动履带车辆而言，对机动性的准确理解至关重要。提出并识别了基于胎面瞬时旋转中心（ICR）的滑移模型，以预测车辆的短期运动。与许多研究使用反馈控制器的速度测量来估计当前ICR位置的研究不同，我们专注于通过使用位置测量来估计ICR位置来预测正向轨迹。在两个履带的滚动速度上对ICR位置进行参数化，并使用扩展卡尔曼滤波器（EKF）实时估算运动学参数，而无需事先了解地形参数。仿真结果验证了该算法在姿态测量频率较低时性能优于传统方法。实验是在重量为13.6吨的履带车上进行的。结果表明，在没有实时运动学全球定位系统（RTK GPS）的情况下，预测的位置和航向误差减少了约75％，姿态误差的减少超过了24％。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第9期|6375652.1-6375652.13|共13页
  • 作者

    Lu Hao; Xiong Guangming; Guo Konghui;

  • 作者单位

    Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China;

    Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China;

    Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China;

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