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Dynamical interferences have been thought that they should be erased to improve control accuracy. However it may be possible to improve performances of total motion using the interferences. In this paper, we propose a method to acquire a kind of machine intelligence to utilize dynamically interfered motion. The machine intelligence is defined here as an ability that the machine can find by itself a way to use dynamical interferences and nonlinear friction to obtain a desired motion. We propose a strategy how a machine uses effects of the dynamical interferences, and how it acquires the way to achieve an objective motion. In this paper, the desired motion is traveling of a 1-link mobile manipulator by using interfering motion of the mounted link, which does not possess driving motors nor brakes. The proposed method is composed of functions to give the machine sample motions using Fourier cries and to improve the Fourier coefficients by evaluating the motion results based on a function used in Genetic Algorithm as a fitness function. Further, an ability to avoid collisions between mounted manipulator and floor is added to the traveling ability to confirm that the proposed method could be adapted to plural objectives. We confirmed by simulations and real experiments that the mobile manipulator could find effective motion that makes it travel forward without colliding against the floor.

A Machine Learning Method of Mobile Robot with Mounted Link to Utilize Dynamically Interfered Motion and Nonlinear Friction.