Robohub.org
 

Video: Quadrocopter learns from its mistakes, perfects air racing

by
08 November 2012



share this:

First person view of the quadrocopter racing through a pylon slalom course.

Manual programming of robots only gets you so far. And, as you can see in the video, for quadrocopters that’s not very far at all (see the “Without Learning” part starting at 1:30):

On its first try to navigate the obstacle course, the flying robot attempts to navigate based on a pre-computed flight path. The path is derived using a basic mathematical (“first principles”) model. But quadrocopters have complex aerodynamics – the force produced by the propellers changes depending on the vehicle’s velocity and orientation, and thus the actual amount of force produced is quite different from what the simple math describes.

What’s worse, these flying vehicles use soft propellers for safety, which bend differently depending on how much thrust is applied and wear rapidly with use (and even more rapidly when crashing).

Even with continuous feedback on the robot’s position from the motion capture system, manually programming the robots with a control sequence that takes all these imperfections into account is impractical.

 

My colleague Angela Schoellig and the Flying Machine Arena team here at ETH Zurich have now developed and implemented algorithms that allow their flying robots to race through an obstacle parcours – and learn to improve their performance.

Here is how Angela described the process to me:

The learning algorithm is applied to a quadrocopter that is guided by an underlying trajectory-following controller. The result of the learning is an adapted input trajectory in terms of desired positions. The algorithm has been equipped with several unique features that are particularly important when pushing vehicles to the limits of their dynamic capabilities and when applying the learning algorithm to increasingly complex systems:

1. We designed an input update rule that explicitly takes actuation and sensor limits into account by solving a constrained convex problem.
2. We developed an identification routine that extracts the model data required by the learning algorithm from a numerical simulation of the vehicle dynamics. That is, the algorithm is applicable to systems for which an analytical model is difficult (or impossible) to derive.
3. We combined model data and experimental data, traditional filtering methods and state-of-the- art optimization techniques to achieve an effective and computationally efficient learning strategy that achieves convergence in less than ten iterations.

 

The result is a robot that learns and improves each time it tries to perform a task.

In this example the robot races through a pylon parcours, calling to mind air races, such as the Red Bull Air Racing Championships or the Reno Air Races – except that there are no human pilots that spent their life learning to fly – here it’s robots doing the learning. And they are efficient, taking less than 10 training sessions to find the optimal steering commands!

Moreover, the learning algorithms are not specific to slalom racing, they can be used to learn other tasks. As Angela points out:

Our goal is to enable autonomous systems – such as the quadrocopter in the video – to ‘learn’ the way humans do: through practice.

 

The videos below show how learning algorithms can be used for other robotic tasks:

 

 

 

 

Full disclosure: Angela and the Flying Machine Arena team work in the same lab as I. Also, I’m working on RoboEarth, trying to allow robot learning on a much larger scale.

 



tags: , , , , , , , , ,


Markus Waibel is a Co-Founder and COO of Verity Studios AG, Co-Founder of Robohub and the ROBOTS Podcast.
Markus Waibel is a Co-Founder and COO of Verity Studios AG, Co-Founder of Robohub and the ROBOTS Podcast.





Related posts :



Robot Talk Episode 64 – Rav Chunilal

In the latest episode of the Robot Talk podcast, Claire chatted to Rav Chunilal from Sellafield all about robotics and AI for nuclear decommissioning.
31 December 2023, by

AI holidays 2023

Thanks to those that sent and suggested AI and robotics-themed holiday videos, images, and stories. Here’s a sample to get you into the spirit this season....
31 December 2023, by and

Faced with dwindling bee colonies, scientists are arming queens with robots and smart hives

By Farshad Arvin, Martin Stefanec, and Tomas Krajnik Be it the news or the dwindling number of creatures hitting your windscreens, it will not have evaded you that the insect world in bad shape. ...
31 December 2023, by

Robot Talk Episode 63 – Ayse Kucukyilmaz

In the latest episode of the Robot Talk podcast, Claire chatted to Ayse Kucukyilmaz from the University of Nottingham about collaboration, conflict and failure in human-robot interactions.
31 December 2023, by

Interview with Dautzenberg Roman: #IROS2023 Best Paper Award on Mobile Manipulation sponsored by OMRON Sinic X Corp.

The award-winning author describe their work on an aerial robot which can exert large forces onto walls.
19 November 2023, by

Robot Talk Episode 62 – Jorvon Moss

In the latest episode of the Robot Talk podcast, Claire chatted to Jorvon (Odd-Jayy) Moss from Digikey about making robots at home, and robot design and aesthetics.
17 November 2023, by





©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association