# Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors

**Authors:** Yunfan Ren, Fangcheng Zhu, Wenyi Liu, Zhepei Wang, Yi Lin, Fei Gao, Fu Zhang  
**arXiv:** [2202.12177](https://arxiv.org/abs/2202.12177)  
**Date:** 2022-02-24  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2202.12177v1/

## Key points

- Authors address the challenge of achieving fully autonomous high-speed flight with quadrotors
- Research focuses on developing a motion planning algorithm based on corridor-constrained minimum control effort trajectory optimization (MINCO) framework
- Key innovation is using overlapping spheres to represent free space for real-time planning of high-speed quadrotor trajectories
- Introduce Receding Horizon Corridors (RHC) strategy for adapting previously generated corridor segments in each replan
- Algorithm demonstrates superiority in simulation scenarios through benchmarking and ablation studies against state-of-the-art methods
- Method evaluated on an autonomous LiDAR-navigated quadrotor UAV in wooded environments, achieving flight speeds exceeding 13.7 m/s without relying on prior maps or external localization facilities

## Summary

In the study titled "Bubble Planner: Planning High-speed Smooth Quadrotor Trajectories using Receding Corridors," authors Yunfan Ren, Fangcheng Zhu, Wenyi Liu, Zhepei Wang, Yi Lin, Fei Gao, and Fu Zhang address the challenge of achieving fully autonomous high-speed flight with quadrotors. Their research focuses on developing a motion planning algorithm based on the corridor-constrained minimum control effort trajectory optimization (MINCO) framework to tackle the obstacles of autonomous flight. The key innovation of their approach lies in using a series of overlapping spheres to represent free space within the environment. This enables real-time planning of high-speed quadrotor trajectories by generating corridors with large overlapped areas between neighboring spheres. Additionally, they introduce a Receding Horizon Corridors (RHC) strategy where previously generated corridor segments are reused in each replan to adapt to the quadrotor's current state. Through comprehensive benchmarking and ablation studies against state-of-the-art methods, their algorithm demonstrates superiority in simulation scenarios. Furthermore, they evaluate their method on an autonomous LiDAR-navigated quadrotor UAV operating in wooded environments and achieve remarkable flight speeds exceeding 13.7 m/s without relying on prior maps or external localization facilities. Overall, this research contributes valuable insights into enabling high-speed autonomous flight for quadrotors through innovative motion planning strategies and demonstrates its effectiveness through rigorous testing and real-world evaluations.