Machine intelligence of a mobile manipulator to utilize dynamically interfered motion and nonlinear friction

Dynamical interferences have been thought that they should be erased to improve control accuracy. However it may be possible to improve the performance of total motion using the interferences. 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 of how a machine uses the effects of the dynamical interferences, and how it acquires the way to achieve an objective motion. 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 series and to improve the Fourier coefficients by evaluating the motion results based on a function used in a genetic algorithm as a fitness function. Further, an ability to avoid collisions between the mounted manipulator and the floor is added to the traveling ability to confirm that the proposed method could be adapted to many 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.