DeltaCharger: Charging Robot with Inverted Delta Mechanism and CNN-driven High Fidelity Tactile Perception for Precise 3D Positioning

TL;DR

DeltaCharger is a novel robotic charging system utilizing an inverted delta mechanism and CNN-based tactile perception to accurately detect electrode misalignments, enhancing safety and precision in mobile robot charging.

Contribution

The paper introduces a new charging robot design with high-fidelity tactile sensing and machine learning for precise 3D electrode alignment prediction.

Findings

Achieved over 95% accuracy in angle misalignment detection.

Demonstrated CNN effectiveness in interpreting tactile pressure data.

Proposed system enhances safety and reliability in robot charging.

Abstract

DeltaCharger is a novel charging robot with an Inverted Delta structure for 3D positioning of electrodes to achieve robust and safe transferring energy between two mobile robots. The embedded high-fidelity tactile sensors allow to estimate the angular, vertical and horizontal misalignments between electrodes on the charger mechanism and electrodes on the target robot using pressure data on the contact surfaces. This is crucial for preventing a short circuit. In this paper, the mechanism of the developed prototype and evaluation study of different machine learning models for misalignment prediction are presented. The experimental results showed that the proposed system can measure the angle, vertical and horizontal values of misalignment from pressure data with an accuracy of 95.46%, 98.2%, and 86.9%, respectively, using a Convolutional Neural Network (CNN). DeltaCharger can potentially bring a new level of charging systems and improve the prevalence of mobile autonomous robots.