Robot Arm Touch Detection by Signal Separation with Higher Order Statistics and Compliance Control

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The objectives of this research are two-folds:1) To complete an analysis method for control systems by means of signal separation with higher order statistics. A novel method in statistical learning, named independent component analysis, enables us to analyze signals based on higher order statistics such as Kurtosis. Thereby we can separate independent components from the mixture of signals and recover the source signal. In this research, we modify this method and develop a method for analysis and synthesis of control systems.2) To derive a mechanism for touch detection of a robot arm to some obstacle. By real-time detection we can stop or relax the arm motion. Furthermore, we aim at developing a compliance control technique.Concerning 1),we have completed the method and applied it to some problems in control. Then, by reversing the idea of separation with inverse operation of observed signals, we have developed multivariable version of what is called Feedback Error Learning (FEL), where a feedforward controller for command inputs is adjusted by some learning law to obtain higher accuracy of the motion. For the former result, we have obtained a best paper award from SICE (our main academic society), and for the latter result, we have obtained a best presentation award from SI division of SICE.On the other hand, we have not yet succeeded in the item2), partly because we have concentrated on the above topics. But we have succeeded in writing one-stroke characters by a robot arm, which is a side-effect from 2). We are continuing efforts on this issue.