CMPCC: Corridor-based Model Predictive Contouring Control for Aggressive Drone Flight

Abstract

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.

arXiv commentsource code: https://github.com/ZJU-FAST-Lab/CMPCC video: https://www.youtube.com/watch?v=_7CzBh-0wQ0 summited to: iser2020

References in corpus (6)

• OSQP: An Operator Splitting Solver for Quadratic Programs
• Optimization-Based Autonomous Racing of 1:43 Scale RC Cars
• Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer
• An Open-Source System for Vision-Based Micro-Aerial Vehicle Mapping, Planning, and Flight in Cluttered Environments
• Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths
• Fast and Safe Path-Following Control using a State-Dependent Directional Metric

Cited by in corpus (4)

• Towards Time-Optimal Tunnel-Following for Quadrotors
• Mapless-Planner: A Robust and Fast Planning Framework for Aggressive Autonomous Flight without Map Fusion
• EVA-Planner: Environmental Adaptive Quadrotor Planning
• VID-Fusion: Robust Visual-Inertial-Dynamics Odometry for Accurate External Force Estimation