Efficient surface detection for assisting Collaborative Robots

Abstract

Collaborative Robots need to read the surfaces they are walking on to keep their dynamic equilibrium, regardless of whether the ground is flat or uneven. Although accelerometers are frequently employed for this task, previous efforts have centered on retrofitting the quadruped robots with new sensors. The second technique is to collect lots of samples for machine learning algorithms, which are not widely implemented. Learning-based approaches altered the traditional way of data analytics. The advanced deep learning algorithms provide better accuracy and prove more efficient when the data size is large. This paper introduced a novel architecture of Convolutional Neural Network, a deep learning-based approach for efficiently classifying the surface on which the robots are walking. The dataset contains reading captured by Inertia Measurement Unit sensors. The proposed model achieved an overall classification accuracy of 88%. The proposed architecture is compared with the existing deep and machine learning techniques to show its effectiveness. The proposed model can be installed on collaborative robots’ onboard processors to identify the surfaces effectively.