Non-Smooth Control Barrier Navigation Functions for STL Motion Planning
Date
2022-04-13
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers in Robotics and AI
Abstract
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.
Description
This article was originally published in Frontiers in Robotics and AI. The version of record is available at: https://doi.org/10.3389/frobt.2022.782783.
Keywords
signal temporal logic, robot motion planning, control barrier function, navigation function, autonomous systems
Citation
Zehfroosh, Ashkan, and Herbert G. Tanner. 2022. “Non-Smooth Control Barrier Navigation Functions for STL Motion Planning.” Frontiers in Robotics and AI 9 (April): 782783. https://doi.org/10.3389/frobt.2022.782783.