In recent years, the evolution of quadrotor online planning makes drones fly out of laboratories and appear in numerous real-world applications. In this area, traditional gradient-based planners rely on a pre-built ESDF map to evaluate the risk of collision. However, construction ESDF consumes significant computation. This project is an ESDF-free gradient-based local planning framework, and we incorporate careful engineering considerations to make it lightweight and robust.
Among the criteria of designing autonomous quadrotors, generating optimized trajectories and tracking the flight paths precisely are two critical components in the action aspect. In industrial applications, the planner and controller of a quadrotor are mostly independently designed, making it hard to tune the joint performance in different applications.
To bridge this gap, 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 realtime performance and it is released as a low-level plugin (https://github.com/ZJU-FAST-Lab/CMPCC) for a large variety of quadrotor systems.
CMPCC: Corridor based Model Predictive Contouring Control for Aggressive Drone Flight, Jialin Ji, Xin Zhou, Chao Xu, Fei Gao, International Symposium on Experimental Robotics (ISER 2020). [paper] [code]