We introduce a novel method for safe mobile robot navigation in dynamic, unknown environments, utilizing onboard sensing to impose safety constraints without the need for accurate map reconstruction. Traditional methods typically rely on detailed map information to synthesize safe stabilizing controls for mobile robots, which can be computationally demanding and less effective, particularly in dynamic operational conditions. By leveraging recent advances in distributionally robust optimization, we develop a distributionally robust control barrier function (DR-CBF) constraint that directly processes range sensor data to impose safety constraints. Coupling this with a control Lyapunov function (CLF) for path tracking, we demonstrate that our CLF-DR-CBF control synthesis method achieves safe, efficient, and robust navigation in uncertain dynamic environments. We demonstrate the effectiveness of our approach in simulated and real autonomous robot navigation experiments, marking a substantial advancement in real-time safety guarantees for mobile robots.

翻译：本文提出了一种在动态未知环境中实现移动机器人安全导航的新方法，该方法利用机载传感器施加安全约束，无需精确的地图重建。传统方法通常依赖详细的地图信息来合成移动机器人的安全稳定控制，这种方法计算量大且效果欠佳，尤其在动态运行条件下更为明显。借助分布鲁棒优化领域的最新进展，我们开发了一种分布鲁棒控制屏障函数（DR-CBF）约束，可直接处理距离传感器数据以施加安全约束。结合用于路径跟踪的控制李雅普诺夫函数（CLF），我们证明了所提出的CLF-DR-CBF控制合成方法能够在不确定动态环境中实现安全、高效且鲁棒的导航。通过仿真和真实自主机器人导航实验，我们验证了该方法的有效性，标志着移动机器人实时安全保证领域取得了实质性进展。