Core Concepts
Enhancing drone racing with safety guarantees and data-driven dynamics for time-optimal flight.
Abstract
This analysis focuses on the challenges of time-optimal quadrotor flight in drone racing. It introduces the SMPCC approach, which enhances safety guarantees and performance through a spatial constraint and terminal set, residual dynamics, and TuRBO tuning. The comparison with RL controllers and real-world experiments highlights the effectiveness of SMPCC in achieving 100% success rate in preventing gate collisions while maintaining competitive lap times.
I. Introduction
Time-optimal flight in drone racing is challenging due to limited control authority.
Model Predictive Contouring Control (MPCC) is a leading approach for time optimization.
Safety guarantees and data-driven dynamics are crucial for enhancing drone racing performance.
II. Related Work
Model-based and learning-based approaches in quadrotor flight.
MPCC formulation for time-optimal trajectories.
Safety considerations in machine learning frameworks.
III. Preliminaries
Nominal quadrotor dynamics model and MPCC algorithm overview.
Augmentation of dynamics with a residual term capturing aerodynamic effects.
TuRBO tuning for controller parameters.
IV. Methodology
Safety set design with prismatic tunnel constraints.
Dynamics augmentation with real-world data.
TuRBO tuning for controller optimization.
V. Experiments
Simulation results comparing MPCC, SMPCC, and RL controllers.
Real-world deployment showcasing SMPCC's success rate and performance.
Comparison of thrust and velocity profiles among different controllers.
VI. Discussion
Advantages of SMPCC in balancing performance and safety.
Limitations in centerline determination and training time.
Comparison with RL controllers and real-world robustness.
Stats
The safety set is designed as a prismatic tunnel.
The residual term captures unmodeled aerodynamic effects.
TuRBO is used to tune controller parameters.
Quotes
"Our approach achieves similar lap times to the best state-of-the-art RL and outperforms the best time-optimal controller while satisfying constraints."
"SMPCC consistently prevents gate crashes with a 100% success rate in real-world experiments."
"The tunnel's dimensions provide a mechanism to intuitively trade off safety against performance."