Uploaded July 2020 | Updated September 2026, 2 hours ago
Video for the ISER 2020 submission.
Preprint: arxiv.org/abs/2007.03271
Code: github.com/ZJU-FAST-Lab/CMPCC
In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as a middle layer between the quadrotor planner and controller. Our method is named as corridor-based model predictive contouring control (CMPCC) since it builds upon on MPCC and utilizes the flight corridor as hard safety constraints. It optimizes the flight aggressiveness and tracking accuracy simultaneously, thus improving our system’s robustness by overcoming unmeasured disturbances. Our method features its online flight speed optimization, strict safety and feasibility, and real-time performance, and will be released1 as a low-level plugin for a large variety of quadrotor systems.
Video for the ISER 2020 submission.
Preprint: arxiv.org/abs/2007.03271
Code: github.com/ZJU-FAST-Lab/CMPCC
In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as a middle layer between the quadrotor planner and controller. Our method is named as corridor-based model predictive contouring control (CMPCC) since it builds upon on MPCC and utilizes the flight corridor as hard safety constraints. It optimizes the flight aggressiveness and tracking accuracy simultaneously, thus improving our system’s robustness by overcoming unmeasured disturbances. Our method features its online flight speed optimization, strict safety and feasibility, and real-time performance, and will be released1 as a low-level plugin for a large variety of quadrotor systems.
![Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios [S2]](https://i.ytimg.com/vi/fBwW93Zq9ss/mqdefault.jpg "Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios [S2]
Video for the paper Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios.
[S2] - Simulations and benchmarks.")
