Eurico Pedrosa

The calibration of mobile manipulators requires accurate estimation of both the transformations provided by the localization system and the transformations between sensors and the motion coordinate system. Current works offer limited flexibility when dealing with mobile robotic systems with many different sensor modalities. In this work, we propose a calibration approach that simultaneously estimates these transformations, enabling precise calibration even when the localization system is imprecise. This approach is integrated into Atomic Transformations Optimization Method (ATOM), a versatile calibration framework designed for multi-sensor, multi-modal robotic systems. By formulating calibration as an extended optimization problem, ATOM estimates both sensor poses and calibration pattern positions. The proposed methodology is validated through simulations and real-world case studies, demonstrating its effectiveness in improving calibration accuracy for mobile manipulators equipped with diverse sensor modalities.

The growing interest in remote collaboration is driven by multiple factors. Advances in digital technology have made it increasingly feasible to collaborate from anywhere, breaking down geographical barriers. Despite the rise of new tools several challenges still exist. These include limitations on the remote member's perspective, confined to what on-site members can capture, as well as potential shortcomings for interacting with on-site physical objects. This position paper discusses the potential of leveraging mobile robots within eXtended Reality (XR) environments to transform how remote collaboration is performed. Mobile robots, equipped with cameras, microphones, articulated arms and various sensors, can serve as physical avatars for remote team members, allowing to navigate through physical spaces, enabling real-time, immersive experiences. Combined with XR tools, these robots may empower on-site and remote collaborators to engage in shared activities. These and other arguments in favor of this position are presented and future directions are proposed.

Mobile robots combine digital technologies and automation, enabling a new era of operations in Industry 4.0 scenarios. These robots can navigate through assembly lines and logistics warehouse autonomously, making them catalysts for increased efficiency and productivity. Despite this, various challenges arise, including maintaining multiple robots up and running over large spaces, handling navigation during unexpected situations, ensuring operators safety, among others. To help overcome these, Augmented Reality (AR), a key pillar of Industry 4.0 can be used to assist with Human-Robot Interaction (HRI), contributing to a more productive and optimized logistics environment. This work proposes a framework for enhancing the understanding of the status of logistics robots through the use of a mobile AR tool. Its goal is to provide operators with an effective way of obtaining more information about the surrounding robots, as well as remote control them. The framework was iteratively tested during the various phases of design and development, allowing to integrate the feedback collected in each stage.

This article proposes a general approach to solve the hand–eye calibration problem. The system is general since it is able to calibrate any number of cameras and, moreover, is able to simultaneously perform the calibration of several instances of the two common hand–eye calibration use cases: eye-on-hand and eye-to-base. The calibration is solved with a nonlinear least squares method, and the reprojection error is used as a metric to guide the optimization procedure. Our approach is seamlessly integrated with the robot operating system framework and allows for the interactive positioning of sensors and labeling of data, facilitating both the data acquisition and labeling and the calibration procedures. Results show that the proposed approach is able to handle any calibration use case with a minimal initial configuration. The approach is compared with several other state-of-the-art hand–eye calibration algorithms. Results show that the proposed approach produces very accurate calibrations when compared to the state of the art.