Zehfroosh Ashkan, Tanner Herbert G
Department of Mechanical Engineering, University of Delaware, Newark, DE, United States.
Front Robot AI. 2022 Apr 13;9:782783. doi: 10.3389/frobt.2022.782783. eCollection 2022.
This paper reports on a new approach to Signal Temporal Logic (STL) control synthesis, that 1) utilizes a navigation function as the basis to construct a Control Barrier Function (CBF), and 2) composes navigation function-based barrier functions using nonsmooth mappings to encode Boolean operations between the predicates that those barrier functions encode. Because of these two key features, the reported approach 1) covers a larger fragment of STL compared to existing approaches, 2) alleviates the computational cost associated with evaluation of the control law for the system in existing STL control barrier function methodologies, and 3) simultaneously relaxes some of the conservativeness of smooth combinations of barrier functions as a means of implementing Boolean operators. The paper demonstrates the efficacy of this new approach with three simulation case studies, one aiming at illustrating how complex STL motion planning specification can be realized, the second highlights the less-conservativeness of the approach in comparison to the existing methods, and another that shows how this technology can be brought to bear to push the envelope in the context of human-robot social interaction.
本文报道了一种信号时序逻辑（STL）控制综合的新方法，该方法：1）利用导航函数作为基础来构建控制障碍函数（CBF）；2）使用非光滑映射来组合基于导航函数的障碍函数，以对这些障碍函数所编码的谓词之间的布尔运算进行编码。由于这两个关键特性，所报道的方法：1）与现有方法相比，涵盖了更大的STL片段；2）减轻了现有STL控制障碍函数方法中与系统控制律评估相关的计算成本；3）同时放宽了作为实现布尔运算符手段的障碍函数平滑组合的一些保守性。本文通过三个仿真案例研究证明了这种新方法的有效性，一个旨在说明如何实现复杂的STL运动规划规范，第二个突出了该方法与现有方法相比保守性较低，另一个展示了如何在人机社会交互的背景下运用这项技术来突破极限。
