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Observer-Based Environment Robust Control Barrier Functions for Safety-critical Control with Dynamic Obstacles

Ying Shuai Quan, Jian Zhou, Erik Frisk, Chung Choo Chung

Abstract

This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with moving obstacles. The approach reduces conservatism, compared with a worst-case uncertainty approach, by incorporating a state observer for obstacles into the ECBF design. The controller, which guarantees safety, is achieved through solving a quadratic programming problem. The proposed method's effectiveness is demonstrated via a dynamic obstacle-avoidance problem for an autonomous vehicle, including comparisons with established baseline approaches.

Observer-Based Environment Robust Control Barrier Functions for Safety-critical Control with Dynamic Obstacles

Abstract

This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with moving obstacles. The approach reduces conservatism, compared with a worst-case uncertainty approach, by incorporating a state observer for obstacles into the ECBF design. The controller, which guarantees safety, is achieved through solving a quadratic programming problem. The proposed method's effectiveness is demonstrated via a dynamic obstacle-avoidance problem for an autonomous vehicle, including comparisons with established baseline approaches.
Paper Structure (13 sections, 34 equations, 4 figures)

This paper contains 13 sections, 34 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Control Barrier Function (CBF)
  4. Environmental Control Barrier Function (ECBF)
  5. Observer-Based Robust ECBF
  6. Bounded-Error Observer for Surrounding Obstacles
  7. Estimation Errors
  8. Robust ECBF
  9. Case Study
  10. Scenario Description and System Modeling
  11. Bounded-Error Observer for Robust ECBF
  12. Results and Discussion
  13. Conclusion

Figures (4)

  • Figure 1: Trajectories generated by different methods in a dynamic environment with measurement uncertainties. The solid black line represents the road boundary, and the dotted grey line represents the road lane. The orange dotted-line circle is the unsafe set centered by the obstacle's center of geometry.
  • Figure 2: Value of CBF with different approaches.
  • Figure 3: Control inputs generated by different approaches.
  • Figure 4: Computation time of different approaches

Theorems & Definitions (4)

  • proof
  • proof
  • proof
  • proof