Proceedings PaperCoordinating robotic sensors in a complex environment for data collection and object recognition
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Most robotic systems used today rely heavily on prior knowledge of the location of specific objects in the workspace. Uncertainty occurs even in these `fixed' manufacturing workspaces, where over time, the precision about the knowledge of the location of objects degrades. Therefore, the entire system must be reprogrammed when adjustments are made. A robot that is able to adapt to changes automatically, must be able to sense its surroundings and identify objects that are encountered. The objective of this work was to design control algorithms that would enable a multisensor robotic system to perform specified tasks in a real world environment. The robotic system presented in this paper operates with little or no prior knowledge. Therefore the robot is given the task of collecting information about its surroundings. Data may be collected for the purpose of object recognition, environment mapping or object manipulation. The main sensing capabilities chosen for this system are machine vision and tactile force sensing. Each sensing technique has its relative advantages and disadvantages. Therefore, it is best to combine and coordinate sensing techniques depending on the work environment and the task requirements. Experiments are performed to show that the algorithms allow the system to meet the goals of safety, accuracy and economical use of computational resources.