Digital technology has evolved towards a new way of processing information: web searches, social platforms, internet forums, and video games have substituted reading books and writing essays. Trainers and educators currently face the challenge of providing natural training and learning environments for digital-natives. In addition to physical spaces, effective training and education require virtual spaces. Digital twins enable trainees to interact with real hardware in virtual training environments. Interactive real-world elements are essential in the training of robot operators. A natural environment for the trainee supports an interesting learning experience while including enough professional substances. We present examples of how virtual environments utilizing digital twins and extended reality can be applied to enable natural and effective robotic training scenarios. Scenarios are validated using cross-platform client devices for extended reality implementations and safety training applications.

Digitalization shapes the ways of learning, working, and entertainment. The Internet, which enables us to connect and socialize is evolving to become the metaverse, a post-reality universe, enabling virtual life parallel to reality. In addition to gaming and entertainment, industry and academia have noticed the metaverse’s benefits and possibilities. For industry, the metaverse is the enabler of the future digital workplace, and for academia, digital learning spaces enable realistic virtual training environments. A connection bridging the virtual world with physical production systems is required to enable digital workplaces and digital learning spaces. In this publication, extended reality–digital twin to real use cases are presented. The presented use cases utilize extended reality as high-level user interfaces and digital twins to create a bridge between virtual environments and robotic systems in industry, academia, and underwater exploration.

Effective data collection in collaborative information-gathering systems relies heavily on maintaining uninterrupted connectivity. Yet, real-world communication disruptions often pose challenges to information-gathering processes. To address this issue, we introduce a novel method —a limited communication decentralized information gathering system for multiple robots to explore environmental phenomena characterized as unknown spatial fields. Our method leverages quadtree structures to ensure comprehensive workspace coverage and efficient exploration. Unlike traditional systems that depend on global and synchronous communication, our method enables robots to share local experiences within a limited transmission range and coordinate their tasks through pairwise and asynchronous communication. Information estimation is facilitated by a Gaussian Process with an Attentive Kernel, allowing adaptive capturing of crucial behavior and data patterns. Our proposed system is validated through simulated scalar field studies in non-stationary environments where multiple robots explore spatial fields. Theoretical guarantees ensure the convergence of distributed area coverage and the regret bounds of distributed online scalar field mapping. We also validate our method empirically in a water quality monitoring scenario featuring three Autonomous Surface Vehicles, tasked with constructing a spatial field.

Low-power IoT sensing nodes can be deployed on the surface of different water bodies for various purposes, including water quality monitoring and pollution detection. Two of the most formidable challenges towards such goals are (1) making the nodes resilient against rough water and extreme weather conditions, and (2) enabling the nodes to establish reliable wireless links. In this paper we share our experience in deploying low-power and resilient IoT nodes on the surface of different water bodies – on a small lake, North Biscayne Bay, Crandon Beach, and South Beach, in Miami, Florida. Furthermore, the paper closely examines how link quality was affected by pre-deployment configurations as well as the characteristics and the motion of the waters. Based on the analyses of a vast amount of statistics, the paper establishes a theoretical (mathematical) and generalized model to characterize and predict link quality fluctuations. We shall show that the realization of the model using the Kalman Filter enables link quality prediction with accuracy exceeding 90%.