CRB Celebrates the MARS Omnid Team
From the MARS Conference in Ojai, California, The Omnid Team and their robots returned to the Center for Robotics and Biosystems (CRB), and were honored at a lively celebration in appreciation for their long hours and hard work. The CRB invited students, faculty, staff, and university leadership to experience first-hand the ease of engaging with the dynamic robot trio, the Omnids, and celebrate the team's accomplishment.
In March 2024, Northwestern University's Center for Robotics and Biosystems demonstrated the Omnid mobile collaborative robots (mocobots) at MARS, a conference on Machine learning, Automation, Robotics, and Space, hosted by Jeff Bezos. The "swarm" of mocobots is designed to collaborate with humans, allowing a human to easily manipulate large, heavy, or awkward payloads. In this case, the mocobots cancel the effect of gravity, so the human can easily manipulate the mock airplane wing in six degrees of freedom. In general, human-cobot systems combine the best of human capabilities with the best of robot capabilities.
From January to early March, a team of students worked late nights to modify the electronics, mechanical systems, and software of the Omnids to improve their performance and make them robust enough for extended offsite use.
At the MARS conference the Omnid demonstration wow'd tech leaders from Amazon, NASA, Nasdaq, Toyota, Disney, Nvidia and more, as they engaged and played with the robots. See more from the conference and how the team prepared on the CRB YouTube channel.
Read more about the Omnid expedition here.
Omnid team members at MARS were Kevin Lynch and Matt Elwin (project leads), Lin Liu (PhD student and lead engineer), Davin Landry (research engineer), and Damien Koh (undergraduate). The team behind the scenes were students Terry Zhang, Gautham Anne, Aya Ben-Saghroune, Jonathan Chan, Adwait Ganguly, Thomas Hoang, Yanni Kechriotis, Ami Takamatsu, Alazar Tegegnework, Cristian Villazhanay, and Mark Zhang.
To learn more about the Omnids, check out the paper at https://arxiv.org/abs/2206.14293 (final version: https://ieeexplore.ieee.org/document/9968284)
