This paper introduces a novel model-free, real-time unicycle-based source seeking design. This design autonomously steers the unicycle dynamic system towards the extremum point of an objective function or physical/scalar signal that is unknown expression-wise, but accessible via measurements. A key contribution of this paper is that the introduced design converges exponentially to the extremum point of objective functions (or scalar signals) that behave locally like a higher-degree power function (e.g., fourth-degree polynomial function) as opposed to locally quadratic objective functions, the usual case in literature. We provide theoretical results and design characterization, supported by a variety of simulation results that demonstrate the robustness of the proposed design, including cases with different initial conditions and measurement delays/noise. Also, for the first time in the literature, we provide experimental robotic results that demonstrate the effectiveness of the proposed design and its exponential convergence ability.

翻译：本文提出了一种新颖的无模型、实时独轮车源追踪设计。该设计能够自主引导独轮车动态系统朝向目标函数或物理/标量信号的极值点运动，这些目标函数或信号在表达式层面未知，但可通过测量获取。本文的一个关键贡献在于：所提出的设计能够指数收敛至局部表现为高次幂函数（例如四次多项式函数）的目标函数（或标量信号）的极值点，而非文献中常见的局部二次目标函数。我们提供了理论结果与设计特性分析，并通过多种仿真结果验证了所提设计的鲁棒性，包括不同初始条件及测量延迟/噪声的情况。此外，本文首次在文献中提供了机器人实验结果，证明了所提设计的有效性及其指数收敛能力。