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

机译:通过预测势场解决输入过高和底盘运动受限的运动学约束

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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.
机译:当前,具有独立转向和驱动轮的伪全向轮式移动机器人似乎在复杂性,灵活性和耐用性之间提供了坚实的折衷方案。然而,这种起落架被施加于致动器战斗的风险,并且在它们的配置空间内遭受奇异区域的困扰。为了解决这些问题,我们通过在预测范围内进行扩展来扩展以前开发的基于势场(PF)的方法。所提出的方法基于模型预测控制(MPC)方法,并通过Pontryagin最小原理结合了梯度下降优化步骤。为了在优化过程中强制遵守约束条件,我们在肋骨的反向传播内修改了拉格朗日乘数。拟议的方法进行了模拟的w.r.t. Care-O-bot ® 3移动机器人的底盘系统,并与基于势场的模型预测控制方法进行了比较。

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