Intelligent and Efficient Strategy for Unstructured Environment Sensing Using Mobile Robot Agents |
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Authors: | Vivek A Sujan Marco A Meggiolaro |
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Affiliation: | (1) Air Handling and Combustion Control Division, Cummins Engine Company, Columbus, IN, 47201, U.S.A.;(2) Department of Mechanical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil, 22453-900 |
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Abstract: | In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and
environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based
iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D
models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator
end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information
theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map.
This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the
agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based
relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty
is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion
compensation. Simulation results show the effectiveness of this algorithm. |
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Keywords: | visual mapping cooperative robots information theory unstructured environments data fusion |
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