This paper develops a provably stable sensor-driven controller for path-following applications of robots with unicycle kinematics, one specific class of which is the wheeled mobile robot (WMR). The sensor measurement is converted to a scalar value (the score) through some mapping (the score function); the latter may be designed or learned. The score is then mapped to forward and angular velocities using a simple rule with three parameters. The key contribution is that the correctness of this controller only relies on the score function satisfying monotonicity conditions with respect to the underlying state -- local path coordinates -- instead of achieving specific values at all states. The monotonicity conditions may be checked online by moving the WMR, without state estimation, or offline using a generative model of measurements such as in a simulator. Our approach provides both the practicality of a purely measurement-based control and the correctness of state-based guarantees. We demonstrate the effectiveness of this path-following approach on both a simulated and a physical WMR that use a learned score function derived from a binary classifier trained on real depth images.
翻译:证明正确性的单轮车辆传感器驱动轨迹跟踪:使用单调分数函数
本文针对具有单轮车辆运动特性的机器人,特别是轮式移动机器人(WMR)的轨迹跟踪应用开发了一种证明稳定性的传感器驱动控制器。通过某些映射(分数函数),将传感器测量转换为标量值(分数)。然后使用三个参数的简单规则将分数映射到前向和角速度。关键贡献在于,该控制器的正确性仅依赖于分数函数关于底层状态(本地路径坐标)满足单调性条件,而不是在所有状态下实现具体值。单调性条件可以通过移动WMR在线检查,无需状态估计,也可以使用测量的生成模型进行离线检查。我们的方法既提供了纯基于测量的控制的可行性,又具备基于状态的保证的正确性。我们在使用从二进制分类器训练的真实深度图像导出的学习分数函数的仿真和物理WMR上展示了这种轨迹跟踪方法的有效性。