activity
20242026
collaborators

16 papers

cs.RO2026

FF-MPCC: High-speed Agile Formation Flight with Model Predictive Contouring Control

Aditya Dandwate, Vit Kratky, Parakh M. Gupta +2

Flying in a prescribed formation in an agile manner remains a challenging problem in the field of UAVs, particularly when following highly-demanding trajectories that require fligh…

cs.RO2026

BC-NMPC: Battery-Constrained NMPC with Propulsion Prediction and Replanning for High-Speed Flight

Parakh M. Gupta, Matej Mihulka, Matej Novosad +2

Trajectory tracking performance of Uncrewed Aerial Vehicles (UAVs) degrades during high-speed and agile flight due to the depletion of the battery and subsequent loss of maximum av…

cs.RO2026

Motor Angular Speed Preintegration for Multirotor UAV State Estimation

Matěj Petrlík, Filip Novák, Robert Pěnička +1

A precise state estimate is crucial for a tight feedback control that enables agile and near-obstacle flights of UAVs. The state-of-the-art methods fuse slow pose measurements with…

cs.RO2026

Autonomous Inspection of Power Line Insulators with UAV on an Unmapped Transmission Tower

Václav Riss, Vít Krátký, Robert Pěnička +1

This paper introduces an online inspection algorithm that enables an autonomous UAV to fly around a transmission tower and obtain detailed inspection images without a prior map of…

cs.RO2026

Geometric Model Predictive Path Integral for Agile UAV Control with Online Collision Avoidance

Pavel Pochobradský, Ondřej Procházka, Robert Pěnička +2

In this letter, we introduce Geometric Model Predictive Path Integral (GMPPI), a sampling-based controller capable of tracking agile trajectories while avoiding obstacles. In each…

cs.RO2026

Vision-only UAV State Estimation for Fast Flights Without External Localization Systems: A2RL Drone Racing Finalist Approach

Filip Novák, Matěj Petrlík, Matej Novosad +3

Fast flights with aggressive maneuvers in cluttered GNSS-denied environments require fast, reliable, and accurate UAV state estimation. In this paper, we present an approach for on…