Publications
2025¶
Infrared-Constellation-Aided Landing of eVTOL Aircraft¶
This paper presents an active fiducial light pattern localization (AFLPL) system to aid the navigation of an eVTOL aircraft during landing without reliance on GPS. AFLPL utilizes a constellation of active fiducial IR lights arranged to enable localization of the aircraft using an onboard IR camera and algorithms for detection, data association, and pose estimation. In experimental tests conducted onboard a multirotor aircraft, the AFLPL system achieved pose estimates at a rate of 33 Hz, with ranges extending up to 200 m and accuracies ranging from 2 to 3 m, down to decimeter-level accuracy at ranges under 40 m. When fused with IMU measurements through an extended Kalman filter (EKF), pose estimates were produced at 200 Hz, with accuracies under 1 m at larger ranges down to sub-decimeter precision at ranges below 40 m. Using pose estimates from the AFLPL-based EKF in a position-feedback loop, accurate tracking of glide-slope landing trajectory by a multirotor aircraft was demonstrated.
Safety for Time-Varying Parameterized Sets Using Control Barrier Function Methods¶
Accepted to the 2025 American Control Conference, Denver, CO.
A fundamental and classical problem in mobile autonomous systems is maintaining the safety of autonomous agents during deployment. Prior literature has presented techniques using control barrier functions (CBFs) to achieve this goal. These prior techniques utilize CBFs to keep an isolated point in state space away from the unsafe set. However, various situations require a non-singleton set of states to be kept away from an unsafe set. Prior literature has addressed this problem using nonsmooth CBF methods, but no prior work has solved this problem using only "smooth" CBF methods. This paper addresses this gap by presenting a novel method of applying CBF methods to non-singleton parameterized convex sets. The method ensures differentiability of the squared distance function between ego and obstacle sets by leveraging a form of the log-sum-exp function to form strictly convex, arbitrarily tight overapproximations of these sets. Safety-preserving control inputs can be computed via convex optimization formulations. The efficacy of our results is demonstrated through multi-agent simulations.
External Lists¶
Dr. Tim McLain
Dr. Randy Beard
Dr. Cammy Peterson
Dr. James Usevitch