论文标题
安全反馈运动计划：收缩理论和$ \ MATHCAL {L} _1 $ - 基于适应控制的方法
Safe Feedback Motion Planning: A Contraction Theory and $\mathcal{L}_1$-Adaptive Control Based Approach
论文作者
论文摘要
能够在存在不完美的模型知识或外部干扰的情况下能够安全操作的自主机器人对于安全至关重要的应用至关重要。在本文中，我们提出了一个规划师 - 不合Snostic框架，以设计和认证围绕所需轨迹的安全管，该轨迹始终保证保留在内部。通过利用收缩分析的最新结果和$ \ Mathcal {l} _1 $ - 适应性控制，我们合成了一种架构，该体系结构可为具有状态和时变不确定性的非线性系统诱导安全管。我们用一些说明性示例演示了如何与传统运动计划算法结合使用基于收缩理论的$ \ Mathcal {l} _1 $适应性控制以获得可证明的安全轨迹。
Autonomous robots that are capable of operating safely in the presence of imperfect model knowledge or external disturbances are vital in safety-critical applications. In this paper, we present a planner-agnostic framework to design and certify safe tubes around desired trajectories that the robot is always guaranteed to remain inside of. By leveraging recent results in contraction analysis and $\mathcal{L}_1$-adaptive control we synthesize an architecture that induces safe tubes for nonlinear systems with state and time-varying uncertainties. We demonstrate with a few illustrative examples how contraction theory-based $\mathcal{L}_1$-adaptive control can be used in conjunction with traditional motion planning algorithms to obtain provably safe trajectories.
