Trust-based Rate-Tunable Control Barrier Functions for Non-Cooperative Multi-Agent Systems.

Abstract

For efficient and robust task accomplishment in multi-agent systems, an agent must be able to distinguish cooperative agents from non-cooperative (i.e., uncooperative and adversarial) agents. In this paper, we first develop a trust metric based on which each agent forms its own belief of how cooperative the other agents are, i.e., of how much the other agents contribute to maintaining safety. With safety encoded as Control Barrier Functions (CBFs), the trust metric is in turn used to adjust the rate at which the CBFs allow the system trajectories to approach the boundary of the safe set. This is achieved via a novel Rate-Tunable CBF, which yields less conservative performance compared to an identity-agnostic implementation, where cooperative and non-cooperative agents are treated similarly. The proposed adaptation and control method is evaluated via simulations on heterogeneous multi-agent systems including non-cooperative agents.

Bib

@inproceedings{DBLP:conf/cdc/ParwanaMP22,
  author       = {Hardik Parwana and
                  Aquib Mustafa and
                  Dimitra Panagou},
  title        = {Trust-based Rate-Tunable Control Barrier Functions for Non-Cooperative
                  Multi-Agent Systems},
  booktitle    = {61st {IEEE} Conference on Decision and Control, {CDC} 2022, Cancun,
                  Mexico, December 6-9, 2022},
  pages        = {2222--2229},
  publisher    = {{IEEE}},
  year         = {2022},
  url          = {https://doi.org/10.1109/CDC51059.2022.9992744},
  doi          = {10.1109/CDC51059.2022.9992744},
  timestamp    = {Wed, 18 Jan 2023 15:37:50 +0100},
  biburl       = {https://dblp.org/rec/conf/cdc/ParwanaMP22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}