Addressing input saturation and kinematic constraints of overactuated undercarriages by predictive potential fields

C. Connette, A. Pott, M. Hägele, A. Verl
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引用次数: 20

Abstract

Currently, pseudo-omnidirectional, wheeled mobile robots with independently steered and driven wheels seem to provide a solid compromise between complexity, flexibility and robustness. Yet, such undercarriages are imposed to the risk of actuator fighting and suffer from singular regions within their configuration space. To address these problems we expand a previously developed potential field (PF) based approach by expanding it with a predictive horizon. The proposed method is based on a model predictive control (MPC) approach, incorporating a gradient descent optimization step via the Pontryagin minimum principle. To enforce adherence to the constraints during optimization, we modify the Lagrange-multipliers within the backpropagation of the costates. The proposed approach is evaluated simulatively w.r.t. the undercarriage of the Care-O-bot ® 3 mobile robot and is compared to the potential field based and a model predictive control approach.
用预测势场求解过驱动起落架的输入饱和和运动约束
目前,具有独立导向和驱动轮的伪全向轮式移动机器人似乎在复杂性、灵活性和鲁棒性之间提供了一个坚实的折衷方案。然而,这样的起落架存在致动器战斗的风险,并且在其配置空间中存在奇异区域。为了解决这些问题,我们扩展了先前开发的基于势场(PF)的方法,通过扩展预测视界来扩展它。该方法基于模型预测控制(MPC)方法,结合了一个基于庞特里亚金最小原理的梯度下降优化步骤。为了在优化过程中加强对约束的遵守,我们修改了状态反向传播中的拉格朗日乘子。该方法在Care-O-bot®3移动机器人底盘上进行了仿真评估,并与基于势场和模型预测的控制方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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