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.
The distance function in the presence of an obstacle,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.