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

TL;DR

This paper introduces an optimal fuzzy-PI force/motion control system for industrial robots, enhancing autonomy by enabling better self-recognition and adaptation during contact with uncertain environments, validated through real-world experiments.

Contribution

It proposes a novel fuzzy-PI control approach for force/motion control in robots operating in partially unknown environments, improving autonomy and contact handling.

Findings

Fuzzy-PI controller outperforms traditional PI in contact tasks.

System effectively adapts to environmental uncertainties.

Experimental validation confirms improved robot autonomy.

Abstract

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.