Flying through a narrow gap using neural network: an end-to-end planning and control approach

03/21/2019
by   Jiarong Lin, et al.
0

In this paper, we investigate the problem of enabling a drone to fly through a tilted narrow gap, without a traditional planning and control pipeline. To this end, we propose an end-to-end policy network, which imitates from the traditional pipeline and is fine-tuned using reinforcement learning. Unlike previous works which plan dynamical feasible trajectories using motion primitives and track the generated trajectory by a geometric controller, our proposed method is an end-to-end approach which takes the flight scenario as input and directly outputs thrust-attitude control commands for the quadrotor. Key contributions of our paper are: 1) presenting an imitate-reinforce training framework. 2) flying through a narrow gap using an end-to-end policy network, showing that learning based method can also address the highly dynamic control problem as the traditional pipeline does (see attached video: https://www.youtube.com/watch?v=jU1qRcLdjx0). 3) propose a robust imitation of an optimal trajectory generator using multilayer perceptrons. 4) show how reinforcement learning can improve the performance of imitation learning, and the potential to achieve higher performance over the model-based method.

READ FULL TEXT

page 1

page 2

page 3

page 5

page 6

page 7

research
08/29/2021

Flying Through a Narrow Gap Using End-to-end Deep Reinforcement Learning Augmented with Curriculum Learning and Sim2Real

Traversing through a tilted narrow gap is previously an intractable task...
research
02/22/2023

Learning Agile Flights through Narrow Gaps with Varying Angles using Onboard Sensing

This paper addresses the problem of traversing through unknown, tilted, ...
research
04/03/2021

No Need for Interactions: Robust Model-Based Imitation Learning using Neural ODE

Interactions with either environments or expert policies during training...
research
03/28/2022

Learning Minimum-Time Flight in Cluttered Environments

We tackle the problem of minimum-time flight for a quadrotor through a s...
research
05/08/2022

Learning to Brachiate via Simplified Model Imitation

Brachiation is the primary form of locomotion for gibbons and siamangs, ...
research
02/25/2020

Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning

Mobile manipulation is usually achieved by sequentially executing base a...
research
04/19/2023

Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive Imitation

Domain-adaptive trajectory imitation is a skill that some predators lear...

Please sign up or login with your details

Forgot password? Click here to reset