Carnegie Mellon Robotics Institute
doctoral dissertation, tech. report CMU-RI-TR-01-30, Robotics Institute, Carnegie Mellon University, August, 2001
|Exploration gathers information about the unknown. This information can come in many forms, from knowledge of new terrain, to rock geology, to lifeforms. The value of these different information forms to an explorer is determined by a set of information metrics, one for each form of information, that depend on the goal of the exploration task. As explorations become more complex, increasing numbers of information metrics must be considered in order to succeed. These multiple information metrics must be considered simultaneously during exploration and often conflict with each other to compete for the finite resources of the explorer. Exploration also involves making decisions, based on the collected information, to test hypotheses and collect more information in an efficient manner. This thesis introduces a new exploration technique which actively considers how much information can be gained from taking sensor readings as well as the cost of collecting this information. The methodology can consider multiple metrics of information simultaneously - such as finding new terrain and identifying rock type - as it explores and these information metrics can be easily changed to perform new and different exploration tasks. The method considers the costs, such as driving, sensing and planning times, associated with collecting the information. Exploration plans are produced which maximize the utility, information gain minus exploration costs, to the exploring robot. The multiple information metric exploration planner is used to solve two exploration problems: creating traversability maps and exploring cliffs. These tasks are performed in simulation and the information gain and exploration path lengths are compared as the information metrics are changed. The multiple information metric exploration planner is further demonstrated in a field experiment to explore a cliff, starting at the cliff top the planner found a route to the bottom and collected sensor information from the face of the cliff.|
|exploration, autonomy, field robots|
Note: PDF file size is wrong; the correct size is 3418K.
|Stewart Moorehead, "Autonomous Surface Exploration for Mobile Robots," doctoral dissertation, tech. report CMU-RI-TR-01-30, Robotics Institute, Carnegie Mellon University, August, 2001|
author = "Stewart Moorehead",
title = "Autonomous Surface Exploration for Mobile Robots",
booktitle = "",
school = "Robotics Institute, Carnegie Mellon University",
month = "August",
year = "2001",
address= "Pittsburgh, PA",
Notes = "PDF file size is wrong; the correct size is 3418K."
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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