The vision of USD’s Modular Robotics Lab is to develop modular robots whose software and hardware can be adapted to perform a wide range of tasks by a non-expert user. The lab is interested in all questions related to realizing this vision. Questions central to our current work include: what is a good set of basic modules from which to build modular robots? What is a good implementation of these basic modules in terms of mechatronics? How do we control a distributed robot such as a modular robot? Can we create programming languages that will accelerate and ease the programming of modular robots?
USD Modular Robotics Lab including the ODIN robot will participate in the play Robottens Anatomi at Odense Theater (buy tickets!), April and September 2009
USD Modular Robotics Lab hosted The Second International Conference on Robots and Communication 2009 (ROBOCOMM 2009). Robocomm 2009, 31/3-2009
Press
Marts 2009 Article on the Locomorph EU project in the magazine Ny Viden in Danish
22/07-2008 USD Modular Robotics Lab's Co-director, Kasper Stoy, was interviewed by Robots Podcast & Community. Robots Podcast's Interview
30/05-2008 An article in the Japanese magazine "ROBOT WATCH" described the ICRA2008's robot challenge in which our group participated. ROBOT WATCH's article
30/05-2008 BBC Radio covered the ICRA2008's robot challenge event in which our group participated. The transmission focuses on the autonomous robot's challenge, called: The Sandbox Event. BBC Radio's transmission
23/05-2008 The Information Sciences Institute at the University of Southern California wrote an article about the ICRA2008's robot challenge, where their group was the winner of the event. Some pictures of our robots can be found using the links embedded in the article. ISI's article
Newest video
The video shows an experimental trial of online learning on a simulated ATRON robot. The purpose is to study adaptation of gait after self-reconfiguration and module fault. Initially the robot is of a crawler type, it then self-reconfigures to a quadrupedal, then a module fails and finally the module again becomes functional. In each new case, the learning enables the robot to adapt to the new situation, by changing the locomotion gait.
For full info see:
D.J. Christensen, U.P. Schultz, K. Stoy, A Distributed Strategy for Gait Adaptation in Modular Robots, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA2010). (to appear) [pdf][bib]
