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.
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
LMI-Net enforces LMI constraints in neural networks by construction using a differentiable projection layer based on Douglas-Rachford splitting and implicit differentiation.
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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LMI-Net: Linear Matrix Inequality--Constrained Neural Networks via Differentiable Projection Layers
LMI-Net enforces LMI constraints in neural networks by construction using a differentiable projection layer based on Douglas-Rachford splitting and implicit differentiation.