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.
Comparative analysis of control barrier functions and artificial potential fields for obstacle avoidance,
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Flow-Opt combines a flow-matching DiT model with a custom differentiable safety filter and learned initialization to enable fast centralized trajectory optimization for tens of robots.
A perception-driven composite CBF safety filter from 3D LIDAR data enables real-time collision avoidance for robots in dynamic constrained environments by using a body-frame ellipsoid safety region with per-point time-varying constraints.
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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Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization
Flow-Opt combines a flow-matching DiT model with a custom differentiable safety filter and learned initialization to enable fast centralized trajectory optimization for tens of robots.
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Reactive Robot-Centric Safety for Autonomous Navigation in Constrained and Dynamic Environments
A perception-driven composite CBF safety filter from 3D LIDAR data enables real-time collision avoidance for robots in dynamic constrained environments by using a body-frame ellipsoid safety region with per-point time-varying constraints.