LabVIEW helps Virginia Tech team win third place in DARPA Urban Challenge
It’s great to see LabVIEW once again at the forefront of autonomous robotics. This time with an impressive finish by Virginia Tech team Victor Tango and their vehicle named Odin.
Virginia Tech, along with TORC Technologies, won the $500,000 third place prize last weekend at the Defense Advanced Research Projects Agency (DARPA) Urban Challenge. In a close race with teams from Carnegie Mellon and Stanford universities, the Virginia Tech team used National Instruments LabVIEW software and CompactRIO hardware in its vehicle. Virginia Tech’s team, Victor Tango, was one of only six robotic teams to finish the 55-mile DARPA Urban Challenge course.
“National Instruments congratulates team Victor Tango on its remarkable achievement,” said Ray Almgren, NI vice president of academic relations. “Team Victor Tango is a great example of how domain experts, rather than computer scientists, use NI LabVIEW graphical system design to quickly design, prototype and deploy sophisticated robotic designs. NI is proud to offer technologies for applications in this exciting and growing field of mobile robotics.”
As part of the competition, TORC Technologies created a set of LabVIEW tools for Joint Architecture for Unmanned Systems (JAUS), an autonomous ground vehicle standard for passing messages and status information between various vehicle subsystems. LabVIEW running on a separate Microsoft Windows Server performed image processing and path planning. The team integrated an NI touch panel with the vehicle dashboard to select appropriate modes of operation.
“This exceptional team of Virginia Tech graduate and undergraduate students has been a true joy to work with, as they share the same passion for robotics as TORC,” said Michael Fleming, president of TORC Technologies. “With LabVIEW, the team implemented parallel processing of high-end vision algorithms running on two quad-core servers that perform the primary perception in our vehicle. The ability of LabVIEW to automatically multithread our application, in addition to the optimizations we performed in the language itself, drastically reduced our development time.”
