Robohub.org
 

Three new quadrotor videos demonstrate agile control and the power of machine learning

by
18 November 2013



share this:
quadrotor_DAndrea_IDSC_ETHZ_FMA
Quadrocopters assembling tensile structures in the ETH Flying Machine Arena. Photo credit: Professorship for Architecture and Digital Fabrication and the Institute for Dynamic Systems and Control, ETH Zurich.

The team at the ETH Flying Machine Arena has released three new videos, demonstrating quadrotors building tensile structures, tossing a ball back and forth, and refining a figure-eight trajectory using iterative learning. Worth the watch!!

Building Tensile Structures with Flying Machines

Federico Augugliaro: “This video shows quadrocopters assembling prototypical tensile structures. Part of a body of research in aerial construction – a field that addresses the construction of structures with the aid of flying machines – the video demonstrates that flying machines are capable of autonomously spanning a rope between two support points. They can also create surface structures by using already placed ropes as new support points. Furthermore, multiple machines can work together to extend the type of structures that can be created by this means. The project is a collaboration between the Institute for Dynamic Systems and Control and the Professorship for Architecture and Digital Fabrication, both from ETH Zurich, Switzerland.”

See Federico’s research paper, a collaboration with Ammar Mirjan, presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems 2013.

Rapid Trajectory Generation for Quadrotors

Mark Mueller: “We have developed a method for rapidly generating and evaluating quadrocopter interception trajectories. Each trajectory goes from an arbitrary initial state (position, velocity and acceleration) to an arbitrary final state. The evaluation of the trajectory includes determining input feasibility, and state feasibility (e.g. that the position of the quadrocopter remains inside a box). Per trajectory, this requires less than two microseconds on a modern laptop computer.

The trajectory generator is used here to generate trajectories to hit a ball towards a target, and determines:

  •  when to hit the ball
  •  how high to return the ball
  •  how much thrust to use at the end of the trajectory.

The trajectory generator is used in a receding horizon implementation, where the optimization is run in closed loop at 50Hz, and only the first part of the trajectory is used as inputs. This allows the system to cope with sensor noise, and deviations in the ball’s flight path.”

See Mark’s research paper, presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems 2013.

Iterative Learning for Periodic Quadrocopter Maneuvers

Markus Hehn: “This video demonstrates an iterative learning algorithm that allows accurate trajectory tracking for quadrocopters executing periodic maneuvers.

The algorithm uses measurements from past executions in order to find corrections that lead to better tracking performance. In order to do this, we measure the tracking error over two laps of the maneuver. The new correction is then computed and applied. After waiting for one lap, we begin measuring again and the next learning step follows. For particularly dynamic maneuvers, we begin the learning process at lower execution speeds. This allows us to initially improve performance under safer conditions, and the algorithm provides a means to then transfer the learned corrections from the lower execution speed to higher speeds. The experience gained at lower speeds thus helps us when flying at high speeds, similar to how people learn skills such as martial arts or playing the piano. The method is also applicable to more complex tasks, shown here by the example of the quadrocopter balancing a pole while following a trajectory.”

See Markus’ research paper, presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems 2013.



tags: , , , , , , , , , , , ,


Hallie Siegel robotics editor-at-large
Hallie Siegel robotics editor-at-large





Related posts :



ep.

339

podcast

High Capacity Ride Sharing, with Alex Wallar

Robohub Podcast · Public Transit https://www.youtube.com/watch?v=vgstBxWFFZQ [space] In this episode, our interviewer Lilly speaks to Alex Wallar, co-founder and CTO of The Routing Company. ...
12 October 2021, by

50 women in robotics you need to know about 2021

It’s Ada Lovelace Day and once again we’re delighted to introduce you to “50 women in robotics you need to know about”! From the Afghanistan Girls Robotics Team to K.G.Engelhardt who in 1989 ...
12 October 2021, by and

Join the Women in Robotics Photo Challenge

How can women feel as if they belong in robotics if we can't see any pictures of women building or programming robots? The Civil Rights Activist Marian Wright Edelson aptly said, "You can't be what yo...
12 October 2021, by

Sense Think Act Podcast: Melonee Wise

In this episode, Audrow Nash speaks with Melonee Wise, former CEO of Fetch Robotics and current VP of Robotics Automation at Zebra Technologies. Melonee speaks about the origin of Fetch Robotics, her ...
11 October 2021, by and

Online events to look out for on Ada Lovelace Day 2021

On the 12th of October, the world will celebrate Ada Lovelace Day to honor the achievements of women in science, technology, engineering and maths (STEM). After a successful worldwide online celebrati...
10 October 2021, by

Flying high-speed drones into the unknown with AI

When it comes to exploring complex and unknown environments such as forests, buildings or caves, drones are hard to beat. They are fast, agile and small, and they can carry sensors and payloads virtua...
08 October 2021, by





©2021 - ROBOTS Association


 












©2021 - ROBOTS Association