A QP-designed C^∞-smooth vector field paired with an analytic nonlinear controller enables safe, input-constrained unicycle navigation to goals with faster convergence and lower turning effort than baselines.
Clf-cbf based quadratic programs for safe motion control of nonholonomic mobile robots in presence of moving obstacles
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FORMULA integrates MPC with CLFs and neural network CBFs for distributed safe formation control in multi-robot systems.
citing papers explorer
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Planning Smooth and Safe Control Laws for a Unicycle Robot Among Obstacles
A QP-designed C^∞-smooth vector field paired with an analytic nonlinear controller enables safe, input-constrained unicycle navigation to goals with faster convergence and lower turning effort than baselines.
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FORMULA: FORmation MPC with neUral barrier Learning for safety Assurance
FORMULA integrates MPC with CLFs and neural network CBFs for distributed safe formation control in multi-robot systems.