Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation

2026-04-14

AI Summary

This work addresses the challenge of ensuring both safety and efficiency in heterogeneous multi-robot systems under decentralized decision-making, where dynamic disparities often render safety constraints infeasible or lead to deadlocks. The authors propose a Capability-Aware Heterogeneous Control Barrier Function (CA-HCBF) framework that unifies robot dynamics at the acceleration level and introduces a directional motion capability–based responsibility allocation mechanism for collision avoidance. By explicitly quantifying and incorporating individual motion capabilities into responsibility assignment, the method guarantees forward invariance of the safe set while ensuring physically realizable constraints, thereby resolving issues of relative degree mismatch and constraint infeasibility. Extensive simulations and real-world experiments with up to 30 heterogeneous robots demonstrate significant improvements over baseline approaches, validating the framework’s safety, efficiency, and practicality in cross-platform coordination.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Safe navigation for multi-robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision-making. Existing decentralized methods often assume robot homogeneity, making shared safety requirements non-uniformly interpreted across heterogeneous agents with structurally different dynamics, which could lead to avoidance obligations not physically realizable for some robots and thus cause safety violations or deadlock. In this paper, we propose Capability-Aware Heterogeneous Control Barrier Function (CA-HCBF), a decentralized framework for consistent safety enforcement and capability-aware coordination in heterogeneous robot teams. We derive a canonical second-order control-affine representation that unifies holonomic and nonholonomic robots under acceleration-level control via canonical transformation and backstepping, preserving forward invariance of the safe set while avoiding relative-degree mismatch across heterogeneous dynamics. We further introduce a support-function-based directional capability metric that quantifies each robot's ability to follow its motion intent, deriving a pairwise responsibility allocation that distributes the safety burden proportionally to each robot's motion capability. A feasibility-aware clipping mechanism further constrains the allocation to each agent's physically achievable range, mitigating infeasible constraint assignments common in dense decentralized CBF settings. Simulations with up to 30 heterogeneous robots and a physical multi-robot demonstration show improved safety and task efficiency over baselines, validating real-world applicability across robots with distinct kinematic constraints.

Problem

• heterogeneous multi-robot systems
• decentralized safe navigation
• control barrier functions
• capability-aware coordination
• safety enforcement

Innovation

• Heterogeneous Multi-Robot Systems
• Control Barrier Functions
• Decentralized Control
• Motion Capability Awareness
• Safety-Critical Navigation