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

Abstract

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 paper, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight. A path-guided optimization (PGO) approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce a perception-aware planning strategy 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. We will release our implementation as an open-source package for the community.

Video: https://www.youtube.com/watch?v=NvR8Lq2pmPg | Code: https://github.com/HKUST-Aerial-Robotics/Fast-Planner. arXiv admin note: text overlap with arXiv:1912.12644

References in corpus (3)

• VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
• Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization
• Search-based 3D Planning and Trajectory Optimization for Safe Micro Aerial Vehicle Flight Under Sensor Visibility Constraints

Cited by in corpus (6)

• PANTHER: Perception-Aware Trajectory Planner in Dynamic Environments
• Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents
• UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT
• EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments
• EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors
• FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning