Olin College Intelligent Vehicle

[hobbs9]

Above, the Olin College vehicle follows a patrol route pre-taught by a human driver. The video shows one lap of the patrol circuit.

Over the past nine months, through Olin's Senior Capstone Program for Engineering (SCOPE) program Draper Laboratory andOlin College have successfully continued their collaboration to build a capable, autonomous off-road vehicle. This partnership began last year when Draper Laboratory sponsored its first SCOPE project and charged Olin students with converting a standard John Deere Gator XUV into a robot capable of being controlled by a computer. This year, Draper's second SCOPE sponsorship got them a new team of five Olin seniors (Nicholas Hobbs, Jacob Izraelevitz, Daniel Grieneisen, Ann Wu and Arash Ushani), who have enhanced this capable platform to command the Gator to drive itself through complex environments that require the vehicle to detect paths, and identify and avoid obstacles.

The vehicle first tackled navigation of parking lots and roads on the Olin College campus. Developing this capability required the creation of software that could enable the vehicle to navigate towards GPS waypoints while detecting and avoiding static and dynamic obstacles. Next, the Olin team took the vehicle to the forest where further development added the ability to recognize organic objects as obstacles and navigate through partially GPS-denied environments. During field testing, the vehicle has autonomously transported supplies through more than a quarter mile of densely vegetated dirt paths. Additionally, the vehicle has autonomously patrolled open field environments for extended durations.

To achieve these objectives, the Olin team has overcome challenging technical problems spanning many engineering domains, from artificial intelligence to advanced sensors and high speed computation. However, the students have not attacked these problems alone. Between their capable project partner Draper Laboratory and vehicle sponsor National Instruments, the team has had access to the tools they needed to accomplish this complicated task.

Team Members: Daniel Grieneisen, Nicholas Hobbs, Jacob Izraelevitz, Arash Ushani, Ann Wu

In this video, the vehicle navigates a wooded road. When it encounters an unanticipated team member blocking the road, the vehicle avoids the team member and returns to its path.

In this video, the team showcases the vehicle's dynamic teach and playback capabilities. During the first lap, a human driver teaches the vehicle a route. In the next lap, the vehicle repeats the route. In the final lap, the dynamic playback avoids unanticipated obstacles. Shown at 4x speed.

[LPS]

Hi Daniel Grieneisen, Nicholas Hobbs, Jacob Izraelevitz, Arash Ushani, Ann Wu,

Thank you so much for your project submission into the NI LabVIEW Student Design Competition. It's great to see your enthusiasm for NI LabVIEW! Make sure you share your project URL(https://decibel.ni.com/content/docs/DOC-16346) with your peers so you can collect votes for your project and win. Collecting the most "likes" gives you the opportunity to win cash prizes for your project submission. I'm curious to know, what's your favorite part about using LabVIEW and how did you hear about the competition?

Good Luck, Liz in Austin, TX.

[jcallaway]

Hi Daniel, Nicholas, Jacob, Arash, and Ann,

We wish you the best of luck in this year's NI LabVIEW Student Design Competition! Would it be possible for you to provide your faculty advisor's name for documentation, as well as specific products you used in you project and the best email address by which to reach your team?

Thanks and good luck!

Jessica in Austin, TX.

[jcallaway]

Hi Daniel, Nicholas, Jacob, Arash, and Ann,

Just a friendly reminder to provide your faculty advisor's name, specific products used in your project, and the best email address by which to reach your team before June 10th so your submission can be considered complete.

Thanks and good luck!

Jessica in Austin, TX.