1 Environment Mapping with a Low-Cost Robot Colony Meeting of the Minds Symposium May 6, 2009 Justin Scheiner and Bradley Yoo Colony Project, Robotics Club

Colony Project Ongoing Robotics Club project, started in 2003 Swarm robotics (many small robots) About 20 undergraduates – All years represented – Mostly Engineering and CS majors Weekly status meeting plus work nights

3 Colony Robot Bearing and Orientation Module (BOM) Dragonfly Microcontroller Board Motors and Encoders Sharp IR GP2D12 Rangefinders Tri-color LED x 2

Goals Low-cost Robots (~$350) Develop applications that are robust to non-idealities: – Noisy sensor data – Limited computation – Communication delays Use the Colony as a research platform for: – Emergent behaviors – Path planning – Controls – Cooperation and task management – Simultaneous Localization and Mapping (SLAM)

Why Mapping? Explore unknown areas Adapt to its own environment Applications – Building navigation – Service robots

6 Possible Approaches Previous Approaches Expensive! ($ $5000) Lasers, Sonar Processing intensive (slow) Feature based (image processing) 3d Laser Mapping - 3DR1

Low Cost Mapping Insertion in environments where robot recovery may not be possible Rapid coverage of the environment Potential commercial applications – Robotic vacuum cleaners

Lots of robots! How?

Rangefinders Economical! Non-ideal Proximity problem Nonlinear resolution Acroname Robotics

Occupancy Grids Grid representing obstacle probabilities at each cell “Robot Evidence Grids,” (Martin, Moravec) Developed at CMU in the late 1980’s Alberto Elfes, Hans Moravec Primary Reference: “Probabilistic Robotics,” (Thrun, Burgard, Fox) Develop a sensor model and conquer! D

Robot Behavior Autonomous wall following Obstacle detection – Using rangefinders Online position estimation – Using encoders Wireless data collection – Using Zigbee module (IEEE ) Zigbee Wireless Model

Results: Point cloud:

12 More Results: Occupancy grid generation Single robot

Problems Slow More robots speed up mapping, but slow down processing. Drift error Occupancy Grid Mapping assumes perfect position estimation

Direction of Further Research Establish real-time cooperative mapping Research methods of “stitching” or quickly correlating separate maps of the same environment Tackle more complicated environments

Colony Members Austin Buchan Christopher Mar Brian Coltin Siyuan Feng James Kong Brad Neuman Justin Scheiner Andrew Yeager Kevin Woo Emily Hart Abe Levkoy Nico Paris Martin Herrmann Megan Dority Jimmy Bourne Rich Hong Evan Mullinix Ben Poole John Sexton David Schultz Bradley Yoo Prof. George Kantor

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