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Robotic systems are more connected, networked, and distributed than ever. New architectures that comply with the \textit{de facto} robotics middleware standard, ROS\,2, have recently emerged to fill the gap in terms of hybrid systems deployed from edge to cloud. This paper reviews new architectures and technologies that enable containerized robotic applications to seamlessly run at the edge or in the cloud. We also overview systems that include solutions from extension to ROS\,2 tooling to the integration of Kubernetes and ROS\,2. Another important trend is robot learning, and how new simulators and cloud simulations are enabling, e.g., large-scale reinforcement learning or distributed federated learning solutions. This has also enabled deeper integration of continuous interaction and continuous deployment (CI/CD) pipelines for robotic systems development, going beyond standard software unit tests with simulated tests to build and validate code automatically. We discuss the current technology readiness and list the potential new application scenarios that are becoming available. Finally, we discuss the current challenges in distributed robotic systems and list open research questions in the field.

The increased use of Internet of Things (IoT) devices -- from basic sensors to robust embedded computers -- has boosted the demand for information processing and storing solutions closer to these devices. Edge computing has been established as a standard architecture for developing IoT solutions, since it can optimize the workload and capacity of systems that depend on cloud services by deploying necessary computing power close to where the information is being produced and consumed. However, as the network scale in size, reaching consensus becomes an increasingly challenging task. Distributed ledger technologies (DLTs), which can be described as a network of distributed databases that incorporate cryptography, can be leveraged to achieve consensus among participants. In recent years DLTs have gained traction due to the popularity of blockchains, the most-well known type of implementation. The reliability and trust that can be achieved through transparent and traceable transactions are other key concepts that bring IoT and DLT together. We present the design, development and conducted experiments of a proof-of-concept system that uses DLT smart contracts for efficiently selecting edge nodes for offloading computational tasks. In particular, we integrate network performance indicators in smart contracts with a Hyperledger Blockchain to optimize the offloading on computation under dynamic connectivity solutions. The proposed method can be applied to networks with varied topologies and different means of connectivity. Our results show the applicability of blockchain smart contracts to a variety of industrial use cases., Comment: 8 pages, 8 figures, 5 tables and 2 algorithms