# Model Predictive Contouring Control for Near-Time-Optimal Quadrotor Flight

**Authors:** Angel Romero, Sihao Sun, Philipp Foehn, Davide Scaramuzza  
**arXiv:** [2108.13205](https://arxiv.org/abs/2108.13205)  
**Date:** 2021-08-30  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2108.13205v1/

## Key points

- Authors: Angel Romero, Sihao Sun, Philipp Foehn, Davide Scaramuzza
- Traditional approaches involve separate planning and control phases, leading to slow computation times.
- Model Predictive Contouring Control (MPCC) proposed for real-time planning and control adjustments.
- MPCC dynamically selects optimal future states to maximize progress along a reference path while minimizing deviations.
- MPCC produces paths close to true time-optimal solutions in real-time.
- Outperforms existing techniques and human pilots in real-world experiments.
- Achieves lap times at speeds of up to 60 km/h in navigating complex flight paths efficiently.

## Summary

In their paper titled "Model Predictive Contouring Control for Near-Time-Optimal Quadrotor Flight," authors Angel Romero, Sihao Sun, Philipp Foehn, and Davide Scaramuzza address the challenge of flying time-optimal trajectories with quadrotors through multiple waypoints. <br/>
Traditional approaches to this problem involve separating it into a planning phase, where a global time-optimal trajectory is computed, and a control phase, where the trajectory is followed accurately. However, these methods struggle to efficiently consider the full quadrotor model and can take minutes or even hours to compute, hindering quick replanning in response to disturbances. <br/>
To overcome this limitation and enable real-time planning and control adjustments, the authors propose Model Predictive Contouring Control (MPCC). This approach tackles both planning and control tasks simultaneously by dynamically selecting optimal future states for the quadrotor during runtime. The MPCC algorithm aims to maximize progress along a predefined reference path while minimizing deviations from it. Remarkably, even when tracking simplified trajectories, MPCC produces paths that closely approximate true time-optimal solutions in real-time. <br/>
The effectiveness of MPCC is demonstrated through real-world experiments where it outperforms existing state-of-the-art techniques as well as skilled human pilots. The authors showcase that their method achieves lap times at speeds of up to 60 km/h, showcasing its superior performance in navigating complex flight paths efficiently. Overall,<br/> Model Predictive Contouring Control offers a promising solution for enhancing quadrotor navigation capabilities by enabling near-time-optimal trajectory planning and execution in dynamic environments.