Adaptation for Validation of Consolidated Control Barrier Functions.

Abstract

We develop a novel adaptation-based technique for safe control design in the presence of multiple state constraints. Specifically, we introduce an approach for synthesizing any number of candidate control barrier functions (CBFs), each encoding a different state constraint, into one consolidated CBF (C-CBF) candidate. We then propose a parameter adaptation law for the weights of the C-CBF's constituent functions such that its controllable dynamics are non-vanishing. We prove that the adaptation law certifies the consolidated CBF candidate as valid for a class of nonlinear, control-affine, multi-agent systems, which permits its use in a quadratic program based control law. We highlight the success of our approach in simulation on a multi-robot goal-reaching problem in a warehouse environment, and further demonstrate its efficacy via a laboratory study with an AION ground rover operating amongst other vehicles behaving both aggressively and conservatively.

Bib

@inproceedings{DBLP:conf/cdc/BlackP23,
  author       = {Mitchell Black and
                  Dimitra Panagou},
  title        = {Adaptation for Validation of Consolidated Control Barrier Functions},
  booktitle    = {62nd {IEEE} Conference on Decision and Control, {CDC} 2023, Singapore,
                  December 13-15, 2023},
  pages        = {751--757},
  publisher    = {{IEEE}},
  year         = {2023},
  url          = {https://doi.org/10.1109/CDC49753.2023.10383597},
  doi          = {10.1109/CDC49753.2023.10383597},
  timestamp    = {Mon, 29 Jan 2024 17:31:54 +0100},
  biburl       = {https://dblp.org/rec/conf/cdc/BlackP23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}