Abstract
Safety–critical system is important in a human–robot collaborative environment. Control Barrier Functions (CBFs)-based methods have emerged as a practical tool for the safety–critical control of autonomous systems. The design of CBFs is difficult to tune. Also, once additional constraints are introduced, the quadratic programming may encounter infeasibility. This paper proposes a safety–critical controller based on a control barrier function using a quasi-saturation function. To avoid infeasibility, we propose to separate the tracking controller from the safety controller. To facilitate the design of the control barrier function, we also propose the control barrier function using the quasi-saturation function. Numerical simulations are presented to show the effectiveness of the proposed method.
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Brunke L et al (2022) Safe learning in robotics: from learning-based control to safe reinforcement learning. Annu Rev Control Robot Auton Syst 5:411–444
Wieland P, Allgöwer F (2007) Constructive safety using control barrier functions. IFAC Proc Vol 40(12):462–467
Aaron DA, Xiangru X, Jessy WG, Paulo T (2017) Control barrier function based quadratic programs for safety critical systems. IEEE Trans Autom Control 62(8):3861–3876
Aaron DA et al (2019) Control barrier functions: theory and applications. Eur Control Conf. https://doi.org/10.23919/ECC.2019.8796030
Jun Z, Bike Z, Zhongyu L, Koushil S (2021) Safety-critical control using optimal-decay control barrier function with guaranteed point-wise feasibility. Am Control Conf. https://doi.org/10.23919/ACC50511.2021.9482626
Jun Z, Bike Z, Koushil S (2021) Safety-critical model predictive control with discrete-time control barrier function. Am Control Conf. https://doi.org/10.23919/ACC50511.2021.9483029
Nakamura H, Yoshinaga T, Koyama Y, Etoh J (2019) Control barrier function based human assist control. T SICE 55(9):353–361
Tonkens S, Herbert S (2022) Refining control barrier functions through Hamilton-Jacobi reachability. IEEE/RSJ Int Conf Intell Robots Syst (IROS). https://doi.org/10.1109/IROS47612.2022.9982203
Ayush A, Koushil S (2017) Discrete control barrier functions for safety-critical control of discrete systems with application to bipedal robot navigation. In: The 2017 Robotics: Science and Systems Conference
Thirugnanam A, Zeng J, Sreenath K (2022) Safety-critical control and planning for obstacle avoidance between polytopes with control barrier functions. Int Conf Robot Autom (ICRA). https://doi.org/10.1109/ICRA46639.2022.9812334
Ahmadi M, Xiong X, Ames AD (2022) Risk-averse control via CVaR Barrier functions: application to bipedal robot locomotion. IEEE Control Syst Lett 6:878–883. https://doi.org/10.1109/LCSYS.2021.3086854
Ma H et al (2021) Model-based constrained reinforcement learning using generalized control barrier function. IEEE/RSJ Int Conf Intell Robots Syst (IROS). https://doi.org/10.1109/IROS51168.2021.9636468
Niloy MAK et al (2021) Critical design and control issues of indoor autonomous mobile robots: a review. IEEE Access 9:35338
Coulter R (1990) Implementation of the pure pursuit path tracking algorithm. Carnegie Mellon University, Pittsburgh
Acknowledgements
This study was partially supported by Human resource development and research project on production technology for the aerospace industry: Subsidy from Gifu Prefecture.
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Ueki, S., Ikeda, T. & Yamada, H. Safety controller based on control barrier functions using quasi-saturation function. Artif Life Robotics 28, 789–796 (2023). https://doi.org/10.1007/s10015-023-00899-3
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DOI: https://doi.org/10.1007/s10015-023-00899-3