Abstract
The authors present techniques developed for real-time monitoring of assembly operations and detection of errors due to parts mishandling such as incorrect parts manipulation, insertion, and placement in the workspace or due to interference between robots sharing the workspace. Two-dimensional maps are created by adaptively projecting the typically three or more dimensional assembly problems to a simpler two-dimensional problem space which provides the flexible and efficient associative properties needed for effective real-time monitoring and diagnosis. A self-organizing neural network (Kohonen map) is used for organizing the various inputs regarding parts, tools, and sensors feedback during robotic assembly and diagnosing the sources of errors. Replanning of the assembly to recover from errors would then proceed based on this feedback. >
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