A framework using 3D Poisson Safety Functions creates a single CBF for full-body dynamic collision avoidance on manipulators, proven to guarantee safety from sampled points and validated on a 7-DOF arm.
Composing control barrier functions for complex safety specifications,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A data-driven CBF converts alpha-confidence sets on unknown obstacle dynamics into probabilistic safety guarantees for vehicles with arbitrary relative-degree dynamics.
citing papers explorer
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Full-Body Dynamic Safety for Robot Manipulators: 3D Poisson Safety Functions for CBF-Based Safety Filters
A framework using 3D Poisson Safety Functions creates a single CBF for full-body dynamic collision avoidance on manipulators, proven to guarantee safety from sampled points and validated on a 7-DOF arm.
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CBF-based Probabilistic Safe Navigation under Unknown Nonlinear Obstacle Dynamics
A data-driven CBF converts alpha-confidence sets on unknown obstacle dynamics into probabilistic safety guarantees for vehicles with arbitrary relative-degree dynamics.