Jose M. Castiblanco Quintero
My publications prioritise measurable performance criteria, reproducibility, and system-level validation across racing and constrained autonomy applications.
My publications prioritise measurable performance criteria, reproducibility, and system-level validation across racing and constrained autonomy applications.
They cover flight dynamics and control, co-simulation toolchains, and geometry-driven performance optimisation.
1) Optimising Drone Airframes for Racing and Flight Dynamics Performance.
2) Improving Racing Drones Flight Analysis: A Data-Driven Approach Using Motion Capture Systems.
3) Co-simulation platform for geometric design, trajectory control, and guidance of racing drones.
4) Experimental study on the dynamic behaviour of drones designed for racing competitions.
5) Visual Servoing Model Predictive Control for Autonomous Shipboard Rotorcraft Landing in High Sea States.
6) THE TRAM-FPV RACING Open Database: Sequences complete indoor flight tests for the study of racing drones — in-proceedings.
1) Links airframe geometry to measurable high-performance flight-dynamics outcomes through a systematic optimisation workflow, exposing design trade-offs under racing-relevant constraints.
2) Improves high-performance flight analysis by upgrading measurement fidelity with optical motion capture, enabling more reliable performance metrics and evidence-based modelling under aggressive dynamics.
3) Provides a single co-simulation toolchain that connects geometry, guidance, and control—supporting rapid iteration from design changes to trajectory-level performance evaluation.
4) Establishes statistical evidence of how racing-drone geometric structures shape dynamic behaviour, grounding high-performance modelling in real flight data.
5) Demonstrates MPC-based visual servoing for autonomous landing under severe disturbances, targeting robust performance in highly dynamic, constraint-driven environments. (Collaboration)
6) Releases repeatable indoor high-dynamics flight sequences (OMS + onboard sensing) to benchmark aggressive manoeuvres and support reproducible model/estimator validation.