--The underwater Unmanned V ehicles(UUVs) are pivot tools for offshore engineering and oceanographic research. Most existing UUVs do not facilitate easy integration of new or upgraded sensors. A solution to this problem is to have a modular UUV system with changeable payload sections capable of carrying different sensor to suite different missions. The design and implementation of a modular and decentral UUV named Y ellowCube is presented in the paper . Instead a centralised software architecture which is adopted by the other modular underwater vehicles designs, a Peer-T o-Peer(P2P) communication mechanism is implemented among the UUV's modules. The experiments in the laboratory and sea trials have been executed to verify the performances of the UUV . Over the past few decades, the Unmanned Underwater V ehicles(UUVs) have become the essential tools in the offshore engineering and the ocean research. Their tasks ranges from the offshore engineering, oceanographic research, salvage and rescue to the military monitoring.

In this paper, we address the challenge of navigating through unknown indoor environments using autonomous aerial robots within confined spaces. The core of our system involves the integration of key sensor technologies, including depth sensing from the ZED 2i camera, IMU data, and LiDAR measurements, facilitated by the Robot Operating System (ROS) and RTAB-Map. Through custom designed experiments, we demonstrate the robustness and effectiveness of this approach. Our results showcase a promising navigation accuracy, with errors as low as 0.4 meters, and mapping quality characterized by a Root Mean Square Error (RMSE) of just 0.13 m. Notably, this performance is achieved while maintaining energy efficiency and balanced resource allocation, addressing a crucial concern in UAV applications. Flight tests further underscore the precision of our system in maintaining desired flight orientations, with a remarkable error rate of only 0.1%. This work represents a significant stride in the development of autonomous indoor UAV navigation systems, with potential applications in search and rescue, facility inspection, and environmental monitoring within GPS-denied indoor environments.

The underlying framework for controlling autonomous robots and complex automation applications are Operating Systems (OS) capable of scheduling perception-and-control tasks, as well as providing real-time data communication to other robotic peers and remote cloud computers. In this paper, we introduce CyberCortex.AI, a robotics OS designed to enable heterogeneous AI-based robotics and complex automation applications. CyberCortex.AI is a decentralized distributed OS which enables robots to talk to each other, as well as to High Performance Computers (HPC) in the cloud. Sensory and control data from the robots is streamed towards HPC systems with the purpose of training AI algorithms, which are afterwards deployed on the robots. Each functionality of a robot (e.g. sensory data acquisition, path planning, motion control, etc.) is executed within a so-called DataBlock of Filters shared through the internet, where each filter is computed either locally on the robot itself, or remotely on a different robotic system. The data is stored and accessed via a so-called \textit{Temporal Addressable Memory} (TAM), which acts as a gateway between each filter's input and output. CyberCortex.AI has two main components: i) the CyberCortex.AI.inference system, which is a real-time implementation of the DataBlock running on the robots' embedded hardware, and ii) the CyberCortex.AI.dojo, which runs on an HPC computer in the cloud, and it is used to design, train and deploy AI algorithms. We present a quantitative and qualitative performance analysis of the proposed approach using two collaborative robotics applications: \textit{i}) a forest fires prevention system based on an Unitree A1 legged robot and an Anafi Parrot 4K drone, as well as \textit{ii}) an autonomous driving system which uses CyberCortex.AI for collaborative perception and motion control.

Cloud native technologies have been observed to expand into the realm of Internet of Things (IoT) and Cyber-physical Systems, of which an important application domain is robotics. In this paper, we review the cloudification practice in the robotics domain from both literature and industrial perspectives. We propose RoboKube, an adaptive framework that is based on the Kubernetes (K8s) ecosystem to set up a common platform across the device-cloud continuum for the deployment of cloudified Robotic Operating System (ROS) powered applications, to facilitate the cloud native evolution in robotics. We examine the process of modernizing ROS applications using cloud-native technologies, focusing on both the platform and application perspectives. In addition, we address the challenges of networking setups for heterogeneous environments. This paper intends to serves as a guide for developers and researchers, offering insights into containerization strategies, ROS node distribution and clustering, and deployment options. To demonstrate the feasibility of our approach, we present a case study involving the cloudification of a teleoperation testbed.

Despite the promising future of autonomous robots, several key issues currently remain that can lead to compromised performance and safety. One such issue is latency, where we find that even the latest embedded platforms from NVIDIA fail to execute intelligence tasks (e.g., object detection) of autonomous vehicles in a real-time fashion. One remedy to this problem is the promising paradigm of edge computing. Through collaboration with our industry partner, we identify key prohibitive limitations of the current edge mindset: (1) servers are not distributed enough and thus, are not close enough to vehicles, (2) current proposed edge solutions do not provide substantially better performance and extra information specific to autonomous vehicles to warrant their cost to the user, and (3) the state-of-the-art solutions are not compatible with popular frameworks used in autonomous systems, particularly the Robot Operating System (ROS). To remedy these issues, we provide Genie, an encapsulation technique that can enable transparent caching in ROS in a non-intrusive way (i.e., without modifying the source code), can build the cache in a distributed manner (in contrast to traditional central caching methods), and can construct a collective three-dimensional object map to provide substantially better latency (even on low-power edge servers) and higher quality data to all vehicles in a certain locality. We fully implement our design on state-of-the-art industry-adopted embedded and edge platforms, using the prominent autonomous driving software Autoware, and find that Genie can enhance the latency of Autoware Vision Detector by 82% on average, enable object reusability 31% of the time on average and as much as 67% for the incoming requests, and boost the confidence in its object map considerably over time.

Robotic systems react to different environmental stimuli, potentially resulting in the dynamic reconfiguration of the software controlling such systems. One effect of such dynamism is the reconfiguration of the software architecture reconfiguration of the system at runtime. Such reconfigurations might severely impact the runtime properties of robotic systems, e.g., in terms of performance and energy efficiency. The ROS \emph{rosbag} package enables developers to record and store timestamped data related to the execution of robotic missions, implicitly containing relevant information about the architecture of the monitored system during its execution. In this study, we discuss about our approach for statically extracting (time-windowed) architectural information from ROS bag files. The proposed approach can support the robotics community in better discussing and reasoning the software architecture (and its runtime reconfigurations) of ROS-based systems. We evaluate our approach against hundreds of ROS bag files systematically mined from 4,434 public GitHub repositories.

Metacontrol [6] is a framework that incorporates systems with the capability to self-adapt to maintain their functionalities at an Self-adaptation can be used in robotics to increase system robustness expected performance. Metacontrol has the design goals of being and reliability. This work describes the Metacontrol method reusable and extensible. This is achieved with the design principles: for self-adaptation in robotics. Particularly, it details how the MROS (1) separating the adaptation and application reasoning; (2) exploiting (Metacontrol for ROS Systems) framework implements and packages at runtime the engineering knowledge of how the system is Metacontrol, and it demonstrate how MROS can be applied in designed to reason how and when the system needs to adapt, i.e., a navigation scenario where a mobile robot navigates in a factory by being model-based.

Modern computing platforms for robotics applications comprise a set of heterogeneous elements, e.g., multi-core CPUs, embedded GPUs, and FPGAs. FPGAs are reprogrammable hardware devices that allow for fast and energy-efficient computation of many relevant tasks in robotics. ROS is the de-facto programming standard for robotics and decomposes an application into a set of communicating nodes. ReconROS is a previous approach that can map complete ROS nodes into hardware for acceleration. Since ReconROS relies on standard ROS communication layers, exchanging data between hardware-mapped nodes can lead to a performance bottleneck. This paper presents fpgaDDS, a lean data distribution service for hardware-mapped ROS 2 nodes. fpgaDDS relies on a customized and statically generated streaming-based communication architecture. We detail this communication architecture with its components and outline its benefits. We evaluate fpgaDDS on a test example and a larger autonomous vehicle case study. Compared to a ROS 2 application in software, we achieve speedups of up to 13.34 and reduce jitter by two orders of magnitude.

In recent years, cloud and edge architectures have gained tremendous focus for offloading computationally heavy applications. From machine learning and Internet of Thing (IOT) to industrial procedures and robotics, cloud computing have been used extensively for data processing and storage purposes, thanks to its "infinite" resources. On the other hand, cloud computing is characterized by long time delays due to the long distance between the cloud servers and the machine requesting the resources. In contrast, edge computing provides almost real-time services since edge servers are located significantly closer to the source of data. This capability sets edge computing as an ideal option for real-time applications, like high level control, for resource-constrained platforms. In order to utilize the edge resources, several technologies, with basic ones as containers and orchestrators like Kubernetes, have been developed to provide an environment with many features, based on each application's requirements. In this context, this works presents the implementation and evaluation of a novel edge architecture based on Kubernetes orchestration for controlling the trajectory of a resource-constrained Unmanned Aerial Vehicle (UAV) by enabling Model Predictive Control (MPC).

This paper demonstrates the integration model-based design approaches or vehicle control, with validation in a freely available open-source simulator. Continued interest in autonomous vehicles and their deployment is driven by the potential benefits of their use. However, it can be challenging to transition new theoretical approaches into unknown simulation environments. Thus, it is critical for experts from other fields, whose insights may be necessary to continue to advance autonomy, to be able to create control applications with the potential to transition to practice. In this article, we will explain how to use the CAT Vehicle simulator and ROS packages to create and test vehicle controllers. The methodology of developing the control system in this article takes the approach of model-based design using Simulink, and the ROS Toolbox, followed by code generation to create a standalone C++ ROS node. Such ROS nodes can be integrated through roslaunch in the CAT Vehicle ROS package.