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7 <updatetime type="date">01.08.2010 10:44:08 CDT</updatetime>
9 <time type="date">01.08.2010 10:44:08 CDT</time>
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11 <registrant>alan-schoen.myopenid.com</registrant>
12 <pname>Autonomous Underwater Vehicle</pname>
13 <user>alan-schoen.myopenid.com</user>
14 <body>Our goal is to implement the control and vision for our Autonomous Underwater Vehicle, a robotic submarine which navigates without any human input. The vehicle must navigate a course which contains visual cues to help guide the robot.
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15 Success will involve two separate sub-projects. First, we will implement high-level control routines. This means combining physical readings from all of the different sensors and determining the course of action for the robot. The robot has a variety of sensors including accelerometers, gyroscopes, a depth sensor, a compass, and cameras. Interpreting all of this information and making it useful will require an implementation of Kalman Filtering, PID control, and a state-machine. The state machine will contain high level instructions like "maintain heading", and "pursue object." The second sub-project is a set of computer vision routines. These will be implemented in OpenCV, and include segmenting objects from their surroundings, determining their orientation and distance, and distinguishing patterns. We have determined that 3 processed images per second is sufficient for our objectives, so we expect that a beagle board will have adequate computational power. The small size and low power consumption make them ideal for use in a closed system like a submarine. Battery power is limited, and a beagle board will consume less than one tenth the power of our current computer.</body>
17 <homepage>http://mart.cs.mcgill.ca</homepage>
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19 <shortdesc>A robotic submarine which combines input from several different kinds of sensors, including cameras, accelerometers, gyroscopes, a depth sensor, and a compass. Vision code in OpenCV will test the limits of the beagle board's processing power.</shortdesc>