A data-driven CBF converts alpha-confidence sets on unknown obstacle dynamics into probabilistic safety guarantees for vehicles with arbitrary relative-degree dynamics.
Motion planning in dynamic environments using velocity obstacles
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SANDO achieves high success rates with collision-free guarantees in dynamic unknown 3D environments via spatiotemporal safe flight corridors, variable-elimination MIQP, and formal analysis under bounded velocity and estimation error.
citing papers explorer
-
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.
-
SANDO: Safe Autonomous Trajectory Planning for Dynamic Unknown Environments
SANDO achieves high success rates with collision-free guarantees in dynamic unknown 3D environments via spatiotemporal safe flight corridors, variable-elimination MIQP, and formal analysis under bounded velocity and estimation error.