论文标题
通过控制屏障功能为动态机器人技术的安全和强大的运动计划
Safe and Robust Motion Planning for Dynamic Robotics via Control Barrier Functions
论文作者
论文摘要
控制屏障功能（CBF）广泛用于对非线性系统的安全关键约束。最近，这些功能已被整合到设计安全关键路径计划者的路径规划框架中。但是，考虑到算法的运行时复杂性和安全性关键约束的执行，这些方法尚未提供一条现实的道路。本文提出了一种新型的运动计划方法，该方法使用众所周知的快速探索随机树（RRT）算法，该算法强制执行CBF和机器人运动动力学约束，以产生安全关键的路径。所提出的算法还输出导致无障碍路径的相应控制信号。该方法还允许通过将强大的CBF约束结合到建议的框架中来考虑模型不确定性。因此，所产生的路径没有任何障碍，并解释了机器人动态和感知的模型不确定性。结果分析表明，所提出的方法的表现优于各种基于RRT的路径计划者，从而确保了用最小的计算开销的安全关键路径。我们介绍了仓鼠V7机器人汽车上算法的数值验证，该机器人汽车是一种微型自动驾驶的无人接地车，在障碍物的路径上执行动态导航，并在感知噪声和机器人动力学中具有各种不确定性。
Control Barrier Functions (CBF) are widely used to enforce the safety-critical constraints on nonlinear systems. Recently, these functions are being incorporated into a path planning framework to design safety-critical path planners. However, these methods fall short of providing a realistic path considering both the algorithm's run-time complexity and enforcement of the safety-critical constraints. This paper proposes a novel motion planning approach using the well-known Rapidly Exploring Random Trees (RRT) algorithm that enforces both CBF and the robot Kinodynamic constraints to generate a safety-critical path. The proposed algorithm also outputs the corresponding control signals that resulted in the obstacle-free path. The approach also allows considering model uncertainties by incorporating the robust CBF constraints into the proposed framework. Thus, the resulting path is free of any obstacles and accounts for the model uncertainty from robot dynamics and perception. Result analysis indicates that the proposed method outperforms various conventional RRT-based path planners, guaranteeing a safety-critical path with minimal computational overhead. We present numerical validation of the algorithm on the Hamster V7 robot car, a micro autonomous Unmanned Ground Vehicle that performs dynamic navigation on an obstacle-ridden path with various uncertainties in perception noises and robot dynamics.
