Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight
By Boyu Zhou, Fei Gao, Luqi Wang, Chuhao Liu, Shaojie Shen. IEEE Robotics and Automation Letters, 2019
575 citations recorded by the source corpus.
In this letter, we propose a robust and efficient quadrotor motion planning system for fast flight in three-dimensional complex environments. We adopt a kinodynamic path searching method to find a safe, kinodynamic feasible, and minimum-time initial trajectory in the discretized control space. We improve the smoothness and clearance of the trajectory by a B-spline optimization, which incorporates gradient information from a Euclidean distance field and dynamic constraints efficiently utilizing the convex hull property of B-spline. Finally, by representing the final trajectory as a non-uniform B-spline, an iterative time adjustment method is adopted to guarantee dynamically feasible and non-conservative trajectories. We validate our proposed method in various complex simulational environments. The competence of the method is also validated in challenging real-world tasks. We release our code as an open-source package.11Open-source implementation is available at https://github.com/HKUST-Aerial-Robotics/Fast-Planner.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/484c1092de7c427723ed6a0b79ba8243539760d1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Shaojie Shen
- Davide Scaramuzza
- Vladlen Koltun
- René Ranftl
- Jie Pan
- Antonio Loquercio
- Boyu Zhou
- Yichen Zhang
In Computer Vision and Pattern Recognition
- Davide Scaramuzza
- Vladlen Koltun
- René Ranftl
- Shaojie Shen
- Luc Van Gool
- Matthias Müller
- Antonio Loquercio
- Jitendra Malik
Citations in the corpus
- FUEL: Fast UAV Exploration Using Incremental Frontier Structure and Hierarchical Planning (2020, methodology): “Our planner takes inspiration from the recent hierarchical quadrotor planning paradigm [21], [22], [28], and makes decisions in three phases, as shown in Fig.”
- RAPTOR: Robust and Perception-Aware Trajectory Replanning for Quadrotor Fast Flight (2020, methodology): “For brevity, we refer the readers to [15] for more details.”
- Learning high-speed flight in the wild (2021, methodology): “Forward Speed (m/s) Forward Speed (m/s) Theoretical Limit (4) FastPlanner (18) Reactive (32) Ours”
- EGO-Planner: An ESDF-Free Gradient-Based Local Planner for Quadrotors (2020, unknown): “The problem formulation in this letter is based on the current state-of-the-art quadrotor local planning framework Fast-Planner [14].”