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Mission Energy Prediction for Unmanned Ground Vehicles Using Real-time Measurements and Prior Knowledge

机译：基于实时测量和先验知识的无人地面车辆任务能量预测

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摘要

A typical unmanned ground vehicle (UGV) mission can be composed of various tasks and several alternative paths. Small UGVs are typically teleoperated and rely on electric rechargeable batteries for their operations. Since each battery has limited energy storage capacity, it is essential to predict the expected mission energy requirement during the mission execution and update this prediction adaptively via real-time performance measurements (e.g., vehicle power consumption and velocity). We propose and compare two methods in this paper. One is based on recursive least-squares estimation built upon a UGV longitudinal dynamics model. The other is based on Bayesian estimation when prior knowledge (e.g., road average grade and operator driving style) is available. The proposed Bayesian prediction can effectively combine prior knowledge with real-time performance measurements for adaptively updating the prediction of the mission energy requirement. Our experimental and simulation studies show that the Bayesian approach can yield more accurate predictions even with moderately imprecise prior knowledge.
机译：典型的无人地面飞行器（UGV）任务可由各种任务和几种替代路径组成。小型UGV通常是远程操作的，并且依靠可充电电池进行操作。由于每个电池的能量存储容量有限，因此必须在执行任务期间预测预期的任务能量需求，并通过实时性能测量（例如，车辆功耗和速度）自适应地更新此预测。我们提出并比较了本文中的两种方法。一种是基于基于UGV纵向动力学模型的递归最小二乘估计。另一个是基于现有知识（例如，道路平均坡度和驾驶员驾驶风格）可用的贝叶斯估计。提出的贝叶斯预测可以有效地将先验知识与实时性能测量相结合，以自适应地更新任务能量需求的预测。我们的实验和模拟研究表明，即使具有适度不精确的先验知识，贝叶斯方法也可以产生更准确的预测。

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  • 来源
    《Journal of Field Robotics》 |2013年第3期|399-414|共16页
  • 作者

    Amir Sadrpour; Jionshua (Judy) Jin; A. Galip Ulsoy;

  • 作者单位

    Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109;

    Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109;

    Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109;

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