CMPCC: Corridor-based Model Predictive Contouring Control for Aggressive Drone Flight
This work addresses robust and safe navigation for quadrotor drones, representing an incremental improvement by building on existing MPCC methods.
The paper tackles the problem of aggressive drone flight by developing CMPCC, a corridor-based model predictive contouring control method that optimizes flight aggressiveness and tracking accuracy simultaneously, achieving real-time performance with strict safety constraints.
In this paper, we propose an efficient, receding horizon, local adaptive low-level planner as the middle layer between our original 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 released as a low-level plugin for a large variety of quadrotor systems.