An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.

Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communication and high computational power to deploy autonomous mobile robots for indoor applications. Deployment of usable indoor service robots requires uninterrupted movement and enhanced robot vision with a robust classification of objects and obstacles using vision sensor data in the indoor environment. However, state-of-the-art methods face degraded indoor object and obstacle recognition for multiobject vision frames and unknown objects in complex and dynamic environments. From these points of view, this paper proposes a new object segmentation model to separate objects from a multiobject robotic view-frame. In addition, we present a support vector data description (SVDD)-based one-class support vector machine for detecting unknown objects in an outlier detection fashion for the classification model. A cloud-based convolutional neural network (CNN) model with a SoftMax classifier is used for training and identification of objects in the environment, and an incremental learning method is introduced for adding unknown objects to the robot knowledge. A cloud–robot architecture is implemented using a Node-RED environment to validate the proposed model. A benchmarked object image dataset from an open resource repository and images captured from the lab environment were used to train the models. The proposed model showed good object detection and identification results. The performance of the model was compared with three state-of-the-art models and was found to outperform them. Moreover, the usability of the proposed system was enhanced by the unknown object detection, incremental learning, and cloud-based framework.

Since the Internet of Robotic Things (IoRT) is composed of robots with actuators, interferences with the real-world activities are necessary, and safety is essential. In addition, some IoRT services may require bidirectional communication between multiple machines. One of the communication protocols that satisfy these requirements is AMQP, a broker architecture that emphasizes reliability and high functionality in communication. Therefore, using AMQP as the communication infrastructure between components in an IoRT system, it can contribute to improving the reliability of robot operations in IoRT and diversifying messaging between robots. To verify AMQP’s communication advantages, we have implemented the communication interface of AMQP in RT-Middleware, which is one of the robot middlewares, and have conducted performance and reliability tests on the effectiveness of RT-Middleware as a platform for constructing a reliable IoRT system. In the tests, we compared the communication performance of the platform with CORBA and MQTT communication interfaces implemented in RT-Middleware. The results show that although AMQP causes a small amount of latency compared to other communication interfaces, the distribution range of the latency is small, and relatively stable communication is performed. Furthermore, in the messaging quality test results, the data loss during communication by AMQP is almost a hundred times better than CORBA, and ten times better than MQTT, which mean that highly reliable messaging is possible. Based on this study’s findings, we conclude that AMQP should be fully used as a communication protocol for constructing IoRT systems.

The integration of Cyber-Physical Systems (CPS), Digital Twins (DT), and robotics with Advanced Data Science Platforms (ADSP) is rapidly transforming industrial and research landscapes by enabling real-time data processing, intelligent decision-making, and enhanced automation. Over the past decades, with the growing demand for adaptable, expandable, and secure platforms, numerous groundbreaking studies have emerged in the field of ADSP for CPS, DT, and robotics. However, most existing research addresses isolated aspects of AI, CPS, DTs, or robotics, lacking a holistic view of how ADSP expands and optimizes these technologies. To address this gap, this survey provides a comprehensive and structured review of the existing methodologies, tools, and techniques at each step of the ADSP workflow, focusing on their applications in CPS, DT, and robotics. Firstly, this survey analyzes the data ingestion phase, focusing on raw data collection architecture, advanced data pre-processing techniques, and data lake integration, while identifying integration and scalability challenges. Secondly, the development methodologies and data analysis techniques within experimental workflows are explored by highlighting widely used tools and real-world case studies. Thirdly, a detailed overview of optimization techniques and deployment strategies is presented, including cloud, edge, and hybrid models, supported by practical deployment examples. Finally, the continuous learning mechanisms are investigated for adaptive system updates, challenges in real-time adaptation, and expanding model performance. Additionally, this survey focuses on evaluation metrics, benchmark studies, and performance comparisons to assess platform efficiency across various domains. This study also explores emerging challenges such as data quality & availability, platform expandability, model transparency, and security, offering insights into future research directions. To ensure the rigor and breadth, this survey is conducted on 319 high-quality studies, state-of-the-art methodologies, and platforms that are selected using the PRISMA methodology from over 600 reviewed publications. By presenting a structured overview of ADSPs, this work aims to serve as a valuable resource for researchers, engineers, and industry professionals aiming to utilize data science for innovation in CPS, DT, and robotics.

Modern consumer electronics include consumer robots, which are made by heterogeneous Internet of Things, and Internet of robotics things are integral members of the Internet of Everything (IoE). IoE plays an incumbent role in smart cities and generates big data. Moreover, smart city IoE maintains various levels of security provisions. However, those are still vulnerable to security and privacy breaches. Nonetheless, mobility increases the risk of security and privacy breaches. As a result, in the internet era, providing seamless security and privacy for inter- and intra-communication of heterogeneous things is a significant challenge. Furthermore, blockchain is one of the established communication techniques that ensure both security and privacy. On the other hand, blockchain inherits trivial constraints with respect to scalability, latency, and interoperability. Cross-chain is a blockchain concept that aims to alleviate the aforementioned concerns. Therefore, we proposed a multifactor aware (privacy, security, interoperability, and scalability) model-driven reference architecture for the Internet of cross-chains, also known as cross-chain as a service platform.

The realm of the Industrial Internet of Things (IIoT) encompasses a broad spectrum of sensors that are integral to distributed smart manufacturing (DSM). The miscellaneous IIoT sensors deployed for DSM are distributed and operate in a hierarchical and federated structure. Nonetheless, fulfilling essential QoS requirements, such as ensuring security and privacy (integrity) whilst maintaining scalability and interoperability (robustness), poses a profound challenge for the DSM cloud service platform. Although blockchain technologies have been employed to safeguard integrity, the first two generations have imposed constraints on robustness. By contrast, the third-generation blockchain, a.k.a. decentralized crosschain ecosystem, complements Web 3.0 and metaverse and can mitigate the constraints of previous generations. Moreover, blockchain-based ad-hoc solutions for DSM use cases are abundant; they often suffer from limited adaptability and unique or homogeneous use cases from a software engineering perspective. A holistic architectural modeling process (AMP) leading to a software reference architecture (SRA) is preferred when alleviating ad-hoc constraints. Therefore, we proposed an AMP for SRA for crosschain-based DSM that safeguards integrity while preserving robustness. In the work described in this paper, we have conducted the following system-modeling process. First, we propose a novel software AMP for DSM. Second, we deduce a modeled SRA based on a crosschain. Third, we infer a modeled system architecture. Empirical experiment results demonstrate that our proposed crosschain-based method outperforms the widely used on-chain-based method while achieving our objectives efficiently.