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

• URL: http://arxiv.org/abs/2604.13245v1
• Date: Tue, 14 Apr 2026 19:21:46 GMT
• Title: Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation
• Abstract summary: Existing decentralized methods assume robot homogeneity, making shared safety requirements non-uniformly interpreted across heterogeneous agents.<n>We propose Capability-Aware Heterogeneous Control Barrier Function (CA-HCBF), a decentralized framework for consistent safety enforcement and capability-aware coordination.
• Score: 19.794822517328654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats [76.98547580312116]
  This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs)<n>To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent avoidance.
  arXiv  Detail & Related papers  (2026-07-22T16:37:05Z)
• SAGE: A Socially-Aware Generative Engine for Heterogeneous Multi-Agent Navigation [20.851820019451587]
  This paper proposes SAGE, a socially-aware generative engine for heterogeneous multi-agent navigation.<n>During inference, SAGE refines sampled robot trajectories using differentiable collision, kinematic, task-progress, and role-conditioned social-compliance terms.<n>The proposed guidance mechanism consistently reduces collision and social-violation rates, scales to teams of up to 20 robots, and enables explicit control of the safety-accuracy-task trade-off without retraining.
  arXiv  Detail & Related papers  (2026-07-18T03:40:59Z)
• Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning [56.240199425429445]
  Multi-robot motion planning requires each robot to generate collision-free trajectories from local observations.<n>Most existing planners generate trajectories from a static snapshot of the local observation.<n>This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models.
  arXiv  Detail & Related papers  (2026-05-26T21:17:53Z)
• Safety-Critical Whole-Body Control for Humanoid Robots via Input-to-State Safe Control Barrier Functions [6.59514190732471]
  This paper presents a hierarchical safety-critical whole-body control framework for humanoid robots.<n>The proposed architecture integrates a kinematic-level whole-body controller (KinWBC), an ISSf-CBF safety filter, and a dynamic-level whole-body controller (DynWBC)
  arXiv  Detail & Related papers  (2026-05-25T08:02:30Z)
• Control Barrier Functions Solved with Hierarchical Quadratic Programming for Safe Physical Human-Robot Interaction [7.06220923441206]
  Control Barrier Function (CBF) has emerged as an effective approach to enforce safety guarantees.<n>CBFs can be solved in a Quadratic Programming (QP) problem, which can include many CBF-formulated tasks.<n>We propose to use a CBF-based Hierarchical Quadratic Programming framework in physical human-robot interaction.
  arXiv  Detail & Related papers  (2026-04-24T21:54:48Z)
• Safety-Critical Centralized Nonlinear MPC for Cooperative Payload Transportation by Two Quadrupedal Robots [3.7624387468002483]
  The interconnected robot-payload system is modeled as a discrete-time nonlinear differential-algebraic system.<n>We develop a control barrier function (CBF)-based NMPC formulation that enforces collision avoidance constraints for both the robots and the payload.<n>The proposed approach retains the interaction wrenches as decision variables, resulting in a structured DAE-constrained optimal control problem.
  arXiv  Detail & Related papers  (2026-04-03T17:19:47Z)
• BarrierSteer: LLM Safety via Learning Barrier Steering [83.12893815611052]
  BarrierSteer is a novel framework that formalizes safety by embedding learned non-linear safety constraints directly into the model's latent representation space.<n>We show that BarrierSteer substantially reduces adversarial success rates, decreases unsafe generations, and outperforms existing methods.
  arXiv  Detail & Related papers  (2026-02-23T18:19:46Z)
• PoSafeNet: Safe Learning with Poset-Structured Neural Nets [49.854863600271614]
  existing approaches often enforce multiple safety constraints uniformly or via fixed priority orders, leading to infeasibility and brittle behavior.<n>We formalize this setting as poset-structured safety, modeling safety constraints as a partially ordered set and treating safety composition as a structural property of the policy class.<n>Building on this formulation, we propose PoSafeNet, a differentiable neural safety layer that enforces safety via sequential closed-form projection.
  arXiv  Detail & Related papers  (2026-01-29T22:03:32Z)
• Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecasting [13.020006323600251]
  Uncertainty-Aware Predictive Control Barrier Functions fuses probabilistic human hand motion forecasting with the formal safety guarantees of Control Barrier Functions.<n> UA-PCBFs empower collaborative robots with a deeper understanding of future human states.<n>Relative to state-of-the-art HRI architectures, UA-PCBFs show better performance in task-critical metrics.
  arXiv  Detail & Related papers  (2025-08-28T14:11:26Z)
• Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning [56.240199425429445]
  Multi-Robot Motion Planning (MPMP) involves generating trajectories for multiple robots operating in a shared continuous workspace.<n>While discrete multi-agent finding (MAPF) methods are broadly adopted due to their scalability, their coarse discretization trajectory quality.<n>This paper tackles limitations of two approaches by introducing discrete MAPF solvers with constrained generative diffusion models.
  arXiv  Detail & Related papers  (2025-08-27T17:59:36Z)
• Towards provable probabilistic safety for scalable embodied AI systems [79.31011047593492]
  Embodied AI systems are increasingly prevalent across various applications.<n> Ensuring their safety in complex operating environments remains a major challenge.<n>This Perspective offers a pathway toward safer, large-scale adoption of embodied AI systems in safety-critical applications.
  arXiv  Detail & Related papers  (2025-06-05T15:46:25Z)
• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• Safe Human Robot Navigation in Warehouse Scenario [15.277331501780488]
  This work proposes a novel methodology that leverages control barrier functions (CBFs) to enhance safety in warehouse navigation.<n>By integrating learning-based CBFs with the Open Robotics Middleware Framework (OpenRMF), the system achieves adaptive and safety-enhanced controls in multi-robot, multi-agent scenarios.
  arXiv  Detail & Related papers  (2025-03-27T04:12:27Z)
• Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation [36.52509571098292]
  We present a risk-aware decentralized control framework that provides guidance on how much responsibility share an individual agent should take to avoid collisions with others. We propose a novel Control Barrier Function (CBF)-inspired risk measurement to characterize the aggregate risk agents face from potential collisions under motion uncertainty. We are able to leverage the flexibility of robots with lower risk to improve the motion flexibility for those with higher risk, thus achieving improved collective safety.
  arXiv  Detail & Related papers  (2023-05-22T20:21:49Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties [36.587645093055926]
  Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time.
  arXiv  Detail & Related papers  (2020-04-11T00:56:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.