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Cited by 182Robotic Path Planning AlgorithmsFormal Methods in VerificationControl and Dynamics of Mobile Robots

Comparative Analysis of Control Barrier Functions and Artificial Potential Fields for Obstacle Avoidance

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Singletary, Andrew, Klingebiel, Karl, Bourne, Joseph, Browning, Andrew, Tokumaru, Phil, Ames, Aaron·2021·2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
DOI 10.1109/iros51168.2021.9636670
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Abstract

Artificial potential fields (APFs) and their variants have been a staple for collision avoidance of mobile robots and manipulators for almost 40 years. Its model-independent nature, ease of implementation, and real-time performance have played a large role in its continued success over the years. Control barrier functions (CBFs), on the other hand, are a more recent development, commonly used to guarantee safety for nonlinear systems in real-time in the form of a filter on a nominal controller. In this paper, we address the connections between APFs and CBFs. At a theoretic level, we show that given a broad class of APFs, one can construct a CBF that guarantees safety. Additionally, we prove that CBFs obtained from these APFs have additional beneficial properties and can be applied to nonlinear systems. Practically, we compare the performance of APFs and CBFs in the context of obstacle avoidance on simple illustrative examples and for a quadrotor with unknown dynamics, both in simulation and on hardware using onboard sensing.

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  • Purely local safety filters may lead to undesirable behavior such as late obstacle avoidance, loss of feasibility, or deadlock situations.

    “While effective in ensuring safety, purely local safety filters may lead to undesirable behavior such as late obstacle avoidance, loss of feasibility, or deadlock situations [4, 5].”

    From Where to Put Safety? Control Barrier Function Placement in Networked Control Systems (2026) · cites this paper as [5]

    The Control Barrier Functions section (p. 4) describes a safety filter that intervenes only when necessary. The quadrotor results (pp. 5–6) report oscillations, failures to maintain safety under some settings, and cases where the drone cannot reach its goal. The paper does not establish late obstacle avoidance, loss of filter feasibility, or deadlock as stated in the claim. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 21

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Cited by

Sorted by citations

Authors
Singletary, A., Klingebiel, K., Bourne, J., Browning, A., Tokumaru, P., & Ames, A.
Affiliations
California Institute of Technology (US)
ORCID
Venue
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Publisher
IEEE
Published
2021-09-27
Pages
8129-8136
ISSN
2153-0858 (ISSN-L)
DOI
10.1109/iros51168.2021.9636670
OpenAlex
W3094402048
Cited by
182
References
0
Citations by year
2026: 19 · 2025: 61 · 2024: 49 · 2023: 32 · 2022: 13 · 2021: 8
FWCI
8.34
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • CaltechAUTHORS (California Institute of Technology) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Formal Methods in Verification
  • Physical Sciences › Engineering › Control and Systems Engineering › Control and Dynamics of Mobile Robots
Keywords
control barrier functions, artificial potential field, obstacle avoidance, collision avoidance, safety guarantees, nonlinear systems, quadrotor, unknown dynamics, onboard sensing, mobile robots, manipulators

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