In its Antarctic configuration, each perception sensor (stereo and laser) takes its data and creates a map where each cell contains a goodness value, how good it is for the robot to be in that position, and a certainty, the sensor’s belief in the data. These maps are then passed to Morphin, which merges the maps and then plans which direction to travel to maximize goodness. This methodology allows for the easy addition of sensors but more importantly the goodness value may refer to something other than terrainability such as science value, solar availability, etc.
A special form of autonomous navigation will also be evaluated this year: In patterned coverage search the user will define a polygonal area to cover and the pattern to use. The robot then starts to follow that pattern to maximize the coverage of the area. While the robot is moving, the user can, in the panoramic view, select an object of interest and enter it into the database. Various sensor readings can also be taken. If this requires deviating from the pattern, the maneuver or path planner creates a route to the region of interest and the robot goes there. When the measurement is complete, the user indicates to the robot to resume pattern, the robot will return to the point it left the pattern and continue.
After return from Antarctica, the navigation group will focus on two areas of research. First, the data collected in Antarctica will be examined thoroughly. This will provide a greater understanding of the terrain and environmental conditions which a future meteorobot will encounter and guide our development. Second, the method used to create a terrain map from stereo data will be improved. The current method of fitting planes to the data has difficulty detecting some obstacles quickly. A method employing fitting wavelets to the data will be examined.
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