University of Washington, Seattle
About us
The Autonomous Controls Lab (ACL) develops methodologies and algorithms based on convex optimization for trajectory planning, robust control, and optimal recursive decision-making across various autonomous dynamical systems.
- Check out our Publications (Google scholar)
- Check out our Youtube and Github
News
- July 2024: Purna defended his dissertation 🎉
- June, 2024: Abhi, Samet, Kazu, and Skye passed PhD candidacy exams 🎉
- June 2024: Oliver received a Teaching Excellence Award [link]
- June 2024: Check out a recent talk by Behcet for NASA [link]
- Apr 2024: Check out a recent article on our onboard rocket landing guidance [link]
Research Highlights
Successive convexification, a real-time solution for nonconvex trajectory optimization that ensures continuous-time constraint satisfaction, guaranteed convergence, and robust performance.
A smooth, sound and complete robustness measure (D-GMSR) to integrate temporal and logical specifications into optimization problems.
Constrained Visibility Guidance (CVG) presents a novel approach to modeling terrain scanning constraints for powered landing maneuvers using a theory of constrained conic intersections.
HALO presents a combined perception (HALSS) and trajectory planning (Adaptive-DDTO) solution towards contingency planning for landing maneuvers with multiple candidate landing sites.
People
Principal Investigator
Prof. Behçet Açıkmeşe
Professor in Aerospace Optimization and Control
Aeronautics & Astronautics
behcet@uw.edu
Members
Mixed-Integer Programming, Stochastic Optimal Control, Reachability Analysis
Real-time Trajectory Planning, Perception-In-The-Loop, Contingency-Awareness
