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A Comparative Study of Artificial Potential Fields and Reciprocal Control Barrier Function-based Safety Filters

arXiv 2403.15743 ↗First posted 23 Mar 2024eess.SY cs.AI cs.SYcited by 0

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Abstract (arXiv)

In this paper, we demonstrate that controllers designed by artificial potential fields (APFs) can be derived from reciprocal control barrier function quadratic program (RCBF-QP) safety filters. By integrating APFs within the RCBF-QP framework, we explicitly establish the relationship between these two approaches. Specifically, we first introduce the concepts of tightened control Lyapunov functions (T-CLFs) and tightened reciprocal control barrier functions (T-RCBFs), each of which incorporates a flexible auxiliary function. We then utilize an attractive potential field as a T-CLF to guide the nominal controller design, and a repulsive potential field as a T-RCBF to formulate an RCBF-QP safety filter. With appropriately chosen auxiliary functions, we show that controllers designed by APFs and those derived by RCBF-QP safety filters are equivalent. Based on this insight, we further generalize the APF-based controllers (equivalently, RCBF-QP safety filter-based controllers) to more general scenarios without restricting the choice of auxiliary functions. Finally, we present a collision avoidance example to clearly illustrate the connection and equivalence between the two methods.

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References · 6

  1. 2019Control Barrier Functions: Theory and Applicationscited by 372
  2. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 287
  3. 2020Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidancecited by 9
  4. 2024Safety-Critical Control for Autonomous Systems: Control Barrier Functions via Reduced-Order Modelscited by 8
  5. 2022Multi-agent systems with CBF-based controllers -- collision avoidance and liveness from instabilitycited by 2
  6. 2024A Tunable Universal Formula for Safety-Critical Controlcited by 1

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