artificial color 3D point cloud of a room

New (old) project!

I've finally added a page about my summer at NASA Goddard Space Flight Center in 2011! I worked with over forty interns at Mike Comberiate's Engineering Boot Camp. The project I worked on was called LARGE: LIDAR-Assisted Robotic Group Exploration. Essentially, a small fleet of robots were designed to autonomously explore and map novel areas. Check it out!!

Finished year one!

I've recently finished my second semester of grad school at MIT! It was amazing. Updates soon -- my summer plans include revamping the website, adding more recent projects, and documenting some of the exciting things that have happened this year. We'll see how I do.


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four people standing around a pair of boxy robots

Summer at NASA

In 2011, the summer after I graduated college, I headed to Greenbelt, Maryland to work with an international team of engineers and computer scientists at NASA Goddard Space Flight Center. The catch: we were all students! Over forty interns from at least four countries participated in Mike Comberiate's Engineering Boot Camp.

two men crouching over a boxy robot

Overview

The boot camp included several different projects. The most famous was GROVER, the Greenland Rover, a large autonomous vehicle that's now driving across the Greenland ice sheets mapping and exploring.

The main project I worked on was called LARGE: LIDAR-Assisted Robotic Group Exploration. A small fleet of robots -- a mothership and some workerbots -- used 3D LIDAR data to explore novel areas. My software team developed object recognition, mapping, path planning, and other software autonomously control the workerbots between infrequent contacts with human monitors. We wrote control programs using ROS.

artificial color 3D LIDAR image of an area

Later in the summer, we presented demonstrations of our work at both NASA Wallops Flight Facility and at NASA Goddard Space Flight Center.

The LARGE team

  • Mentors: NASA Mike, Jaime Cervantes, Cornelia Fermuller, Marco Figueiredo, Pat Stakem

  • Software team: Felipe Farias, Bruno Fernades, Thomaz Gaio, Jacqueline Kory, Christopher Lin, Austin Myers, Richard Pang, Robert Taylor, Gabriel Trisca

  • Hardware team: Andrew Gravunder, David Rochell, Gustavo Salazar, Matias Soto, Gabriel Sffair

  • Others involved: Mike Huang, William Martin, Randy Westlund

a group of men standing around a robot

Project description

The goal of the LARGE project is to assemble a networked team of autonomous robots to be used for three-dimensional terrain mapping, high-resolution imaging, and sample collection in unexplored territories. The software we develop in this proof-of-concept project will be transportable from our test vehicles to actual flight vehicles, which could be sent anywhere from toxic waste dumps or disaster zones on Earth to asteroids, moons, and planetary surfaces beyond.

artificial color 3D point cloud image

The robot fleet consists of a single motherbot and a set of workerbots. The motherbot is capable of recognizing the location and orientation of each workerbot, allowing her to designate target destinations for any worker and track their progress. Presently, localization and recognition is performed via the detection of spheres mounted in a unique configuration atop each robot. Each worker can independently plot a safe path through the terrain to the goal assigned by the motherbot. Communication between robots is interdependent and redundant, with messages sent over a local network. If communication between workers and the motherbot is lost, the workers will be able to establish a new motherbot and continue the mission. The failure of any single robot or device will not prevent the mission from being completed.

The robots use LIDAR sensors to take images of the terrain, stitching successive images together to create global maps. These maps can then be used for navigation. Eventually, several of the workers will carry other imaging sensors, such as cameras for stereo vision or a Microsoft Kinect, to complement the LIDAR and enable the corroboration of data across sensory modalities.

Articles and other media

In the media:

three metal boxy robots with treads

On my blog:

Videos

I spent the summer writing code, learning ROS, and dealing with our LIDAR images. Other people took videos! (Captions, links to videos, & credits are below the corresponding videos.) More may be available on Geeked on Goddard or from nasagogblog's youtube channel.


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My summer lab at NASA GSFC included a high school media team, who continuously had the rest of us on film. They compiled a great documentary describing the two main projects going on in the lab -- the Greenland ROVER and Lidar-Assisted Robotic Group Exploration:

Engineering Boot Camp Documentary 2011

There are some other videos up now, too -- click over to the GSFC robotics youtube channel; there are more than I'm linking here:

GROVER on the beach: The Greenland ROVER during a test run on the beach, during our trip to Wallops.

LIDAR image test: Watch a 360-degree image from the LIDAR sensor on one of my team's robots as it's formed.

I encourage you to take a look!

EDIT

Looks like these videos are no longer up. Whoever was hosting them has taken them down. Alas.

You can find others videos and articles about the summer here!


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_a group of people leaning over stuff_

Questions vs answers

Recently, I had a discussion with a friend about the key difference between science and engineering.

As a computer engineer, my friend found that the more advanced his coursework got and the more he learned about electronics, circuits, and microprocessors, the better he understood the subjects as a whole.

Which shouldn't be too surprising. That's the point of a college engineering degree: learn how stuff works and how to make stuff work.

But me, I find that as I learn more about brains and minds, filled with complex interactions between neurons, glial cells, neurotransmitters, and hormones, the picture gets steadily more complicated. The universe is one big dynamic system, full of chaotic pieces, and I keep finding more questions. The more I learn, the less I know.

That's the scientist's perspective on the world: more knowledge means more questions. More astonishment, more confusion.

(This is not a novel pronouncement, merely a recent observation supporting previously suggested differences between the two disciplines.)


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