An optimal fuzzy-PI force/motion controller to increase industrial robot autonomy

This paper presents a method for robot self-recognition and self-adaptation through the analysis of the contact between the robot end-effector and its surrounding environment. Often, in off-line robot programming the idealized robotic environment (the virtual one) does not reflect accurately the real one. In this situation we are in the presence of a Partially Unknown Environment (PUE). Thus, robotic systems must have some degree of autonomy to overcome this situation, especially when contact exists. The proposed force/motion control system has an external control loop based on forces and torques exerted on the robot end-effector and an internal control loop based on robot motion. The external control loop is tested with an optimal Proportional Integrative (PI) and a fuzzy-PI controller. The system performance is validated with real-world experiments involving contact in PUEs.