CA-HCBF creates a unified acceleration-level safety framework for mixed holonomic and nonholonomic robots and allocates avoidance duties proportionally to each robot's capability using a support-function metric and clipping.
Distributed implementation of control barrier functions for multi-agent systems
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A two-time-scale dynamic implementation enables locally computable approximations of networked control barrier function safety filters with explicit bounds on trajectory mismatch and safety degradation.
Presents a distributed data-driven zeroing control barrier function (3D-ZCBF) framework that derives explicit safety conditions from data to preserve communication network connectivity in leader-follower multi-agent systems.
citing papers explorer
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Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation
CA-HCBF creates a unified acceleration-level safety framework for mixed holonomic and nonholonomic robots and allocates avoidance duties proportionally to each robot's capability using a support-function metric and clipping.
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Local Safety Filters for Networked Systems via Two-Time-Scale Design
A two-time-scale dynamic implementation enables locally computable approximations of networked control barrier function safety filters with explicit bounds on trajectory mismatch and safety degradation.
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A Distributed Framework for Data-Driven Safe Coordination in Leader-Follower Networks
Presents a distributed data-driven zeroing control barrier function (3D-ZCBF) framework that derives explicit safety conditions from data to preserve communication network connectivity in leader-follower multi-agent systems.