Learning High-Speed Flight in the Wild

10/11/2021
by   Antonio Loquercio, et al.
0

Quadrotors are agile. Unlike most other machines, they can traverse extremely complex environments at high speeds. To date, only expert human pilots have been able to fully exploit their capabilities. Autonomous operation with on-board sensing and computation has been limited to low speeds. State-of-the-art methods generally separate the navigation problem into subtasks: sensing, mapping, and planning. While this approach has proven successful at low speeds, the separation it builds upon can be problematic for high-speed navigation in cluttered environments. Indeed, the subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline. Here we propose an end-to-end approach that can autonomously fly quadrotors through complex natural and man-made environments at high speeds, with purely onboard sensing and computation. The key principle is to directly map noisy sensory observations to collision-free trajectories in a receding-horizon fashion. This direct mapping drastically reduces processing latency and increases robustness to noisy and incomplete perception. The sensorimotor mapping is performed by a convolutional network that is trained exclusively in simulation via privileged learning: imitating an expert with access to privileged information. By simulating realistic sensor noise, our approach achieves zero-shot transfer from simulation to challenging real-world environments that were never experienced during training: dense forests, snow-covered terrain, derailed trains, and collapsed buildings. Our work demonstrates that end-to-end policies trained in simulation enable high-speed autonomous flight through challenging environments, outperforming traditional obstacle avoidance pipelines.

READ FULL TEXT

page 2

page 4

page 6

page 8

page 10

page 18

page 22

page 23

research
06/19/2018

Experiments in Fast, Autonomous, GPS-Denied Quadrotor Flight

High speed navigation through unknown environments is a challenging prob...
research
02/24/2022

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

Quadrotors are agile platforms. With human experts, they can perform ext...
research
12/22/2020

High-Speed Robot Navigation using Predicted Occupancy Maps

Safe and high-speed navigation is a key enabling capability for real wor...
research
05/31/2019

Fast and Agile Vision-Based Flight with Teleoperation and Collision Avoidance on a Multirotor

We present a multirotor architecture capable of aggressive autonomous fl...
research
09/19/2021

Fast Obstacle Avoidance Motion in SmallQuadcopter operation in a Cluttered Environment

The autonomous operation of small quadcopters moving at high speed in an...
research
09/05/2019

Is two greater than one?: Analyzing Multipath TCP over Dual-LTE in the Wild

Multipath TCP (MPTCP) is a standardized TCP extension which allows end-h...
research
09/06/2023

Robotic Table Tennis: A Case Study into a High Speed Learning System

We present a deep-dive into a real-world robotic learning system that, i...

Please sign up or login with your details

Forgot password? Click here to reset