A finite-horizon NMPC framework with a smooth point-to-cloud distance metric and control barrier functions achieves accurate set-point tracking and smooth obstacle avoidance for aerial robots.
Ex- tended euclidean distance-based model predictive control for safety- critical dynamic obstacle avoidance,
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Point-to-Cloud NMPC with Smooth Avoidance Constraints
A finite-horizon NMPC framework with a smooth point-to-cloud distance metric and control barrier functions achieves accurate set-point tracking and smooth obstacle avoidance for aerial robots.