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：本文针对具有单轮车运动学的机器人（其中一类典型代表为轮式移动机器人（WMR））的路径追踪应用，提出了一种可证明稳定的传感器驱动控制器。传感器测量值通过某种映射（即评分函数）转换为标量值（即评分），该映射可人为设计或通过学习获得。随后，利用一个包含三个参数的简单规则将评分映射为前向速度和角速度。本文的关键贡献在于：该控制器的正确性仅依赖于评分函数相对于底层状态（即局部路径坐标）满足单调性条件，而非在所有状态下达到特定值。单调性条件可通过以下方式在线验证：无需状态估计仅通过移动WMR，或离线利用测量生成模型（如仿真器）。本文方法兼具纯基于测量控制的实用性以及基于状态保证的正确性。通过在仿真平台和实际WMR上使用基于真实深度图像训练的二分类器导出的学习评分函数，本文验证了该路径追踪方法的有效性。