Modeling and Motion Planning for Autonomous Skid-Steered Vehicles

本研究的重点是开发和实验验证动态模型的3D运动的滑移轮式车辆，大型和重要的一类户外车辆，这往往是首选的全地形机动性，由于强大的机械结构。此外，研究将采用开发的3D模型在运动规划。建模是基于严格的动态分析，但需要实验来确定关键参数，特别是剪切变形模量，摩擦系数和滚动阻力。 该研究还强调包括电机速度控制器，以获得闭环模型，对模型的不确定性不太敏感，因此可以为运动规划者提供更好的车辆性能预测。 在这项研究中使用的主要运动规划方法被称为基于采样的模型预测控制，是一个变种的模型预测控制，利用采样方法的计算速度的优势。滑橇转向自主地面车辆（AGV）预计将被广泛用于非结构化，户外，起伏的环境中的任务，如搜索和救援，国土安全，消防，侦察和监视。这些任务将大大受益于有效的运动规划的AGV，这可以通过使用SBMPC结合在本研究中开发的车辆的动态模型来完成。最终结果是时间最优的轨迹、能量有效的运动规划、用于使命完成和加油的可靠能量预测、存在非致命机械故障（例如，漏气的轮胎），以及由于更准确地预测车辆运动而较少的重新规划。 详细的建模，控制和规划程序预计将适用于日益增长和越来越重要的一类滑移转向，独特的移动平台，如RHex和Wheg机器人，预计将发现越来越多的使用，由于更灵活的移动性，例如，攀爬复杂障碍的能力。

This research focuses on developing and experimentally verifying dynamic models for 3D motion of skid-steered wheeled vehicles, a large and important class of outdoor vehicles, which are often preferred for all-terrain mobility due to a robust mechanical structure. In addition, the research will employ the developed 3D models in motion planning. The modeling is based on rigorous dynamic analysis, but requires experimentation to determine key parameters, particularly the shear deformation modulus, the coefficients of friction, and the rolling resistance. The research also emphasizes the inclusion of the motor speed controllers to obtain closed-loop models that are less sensitive to model uncertainty and can hence provide better predictions of vehicle performance for motion planners. The primary motion planning methodology used in this research is called Sampling Based Model Predictive Control and is a variant of model predictive control that takes advantages of the computational speed of sampling methods.Skid-steered autonomous ground vehicles (AGVs) are expected to be used extensively in unstructured, outdoor, undulating environments for tasks such as search and rescue, homeland security, fire fighting, reconnaissance, and surveillance. These tasks will greatly benefit from efficient motion planning for the AGVs, which can be accomplished by using SBMPC in conjunction with the dynamic models of the vehicles developed in this research. The end result is time-optimal trajectories, energy-efficient motion planning, reliable energy predictions for mission completion and refueling, motion planning in the presence of non-fatal mechanical failures (e.g., flat tires), and less frequent replanning due to more accurate predictions of vehicle motion. The detailed modeling, control and planning procedures are expected to be applicable to the growing and increasingly important class of skid-steered, unique mobility platforms such as RHex and Wheg robots, which are expected to find increasing use due to more flexible mobility, e.g., the ability to climb complex obstacles.