论文标题
从低级到高级运动计划者：使用运动预测和参考调速器安全机器人导航
From Low to High Order Motion Planners: Safe Robot Navigation using Motion Prediction and Reference Governor
论文作者
论文摘要
围绕障碍的安全导航是高度动态机器人的基本挑战。使用轨迹优化和跟踪控制将简单参考路径计划调整为复杂机器人动力学的最新方法是脆弱的，需要大量的重新层次周期。在本文中，我们介绍了一个新颖的反馈运动计划框架，该框架扩展了使用运动预测和参考州长的低阶（例如位置/速度控制）参考运动计划者的参考运动计划者的适用性。我们使用预测的机器人运动范围进行安全评估，并通过参考调查员在高级计划和低级控制之间建立双向接口。我们描述了反馈运动计划框架的通用基本构建块，并为运动控制，预测和参考计划提供了具体的示例构造。我们证明了我们的计划框架的正确性，并在数值模拟中证明了其性能。我们得出的结论是，准确的运动预测对于缩小高级计划和低级控制之间的差距至关重要。
Safe navigation around obstacles is a fundamental challenge for highly dynamic robots. The state-of-the-art approach for adapting simple reference path planners to complex robot dynamics using trajectory optimization and tracking control is brittle and requires significant replanning cycles. In this paper, we introduce a novel feedback motion planning framework that extends the applicability of low-order (e.g. position-/velocity-controlled) reference motion planners to high-order (e.g., acceleration-/jerk-controlled) robot models using motion prediction and reference governors. We use predicted robot motion range for safety assessment and establish a bidirectional interface between high-level planning and low-level control via a reference governor. We describe the generic fundamental building blocks of our feedback motion planning framework and give specific example constructions for motion control, prediction, and reference planning. We prove the correctness of our planning framework and demonstrate its performance in numerical simulations. We conclude that accurate motion prediction is crucial for closing the gap between high-level planning and low-level control.
