RAPTOR: Robust and Perception-Aware Trajectory Replanning for Quadrotor Fast Flight

By Boyu Zhou, Jie Pan, Fei Gao, Shaojie Shen. IEEE Transactions on robotics, 2020

301 citations recorded by the source corpus.

Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this article, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight, which addresses these issues systematically. A path-guided optimization approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce two perception-aware planning approaches to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively through benchmark comparisons and challenging indoor and outdoor aggressive flights. We release our implementation as an open-source package1 for the community.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/d88efac2806c6a99a2fffe19e153f62d8adb121a

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

In Computer Vision and Pattern Recognition

In Ocean Engineering

Citations in the corpus

Learning high-speed flight in the wild (2021, methodology): “For comparison, state-of-the-art methods with comparable actuation, sensing, and computation [18, 31] achieve in similar environments a maximum average speed of 2.”
FUEL: Fast UAV Exploration Using Incremental Frontier Structure and Hierarchical Planning (2020, methodology): “In our work, we utilize a volumetric map [34], which has been successfully applied to fast autonomous flights [28], [30] in complex scenes.”
EGO-Planner: An ESDF-Free Gradient-Based Local Planner for Quadrotors (2020, background): “[11] incorporate perception awareness to make the system more robust.”