Object Classification on a High-Resolution Tactile Floor for Human-Robot Collaboration

Abstract

The growing trend in the manufacturing industry towards human-robot collaboration as a new paradigm has the potential to increase the efficiency of robot-enabled production lines. This development requires safety-oriented sensor systems, frameworks and reliable algorithms in order to avoid hazardous situations. These systems should be able to distinguish between different objects, mobile vehicles and human workers to allow adaptive control strategies of the robots. In this paper, we introduce a tactile flooring system with high spatial resolution to determine the humans in a workplace and to continue the robot's task when mobile units are entering the workspace and no life-threatening situation is expected. The pressure data of the tactile floor is transformed to image data which is used to determine the objects presented. There are several categories to distinguish between specific vehicles, humans and users resulting in a multi-label classification problem with up to three categories presented in one pattern. Our CNN classifies five different objects with an exact match of 75%.