Cloud Robotics Research Articles

Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1

BackgroundTarget identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches.ResultsWe synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development.ConclusionThis study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5’s HydraScreen and Strateos’ automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs.Scientific contributionWe present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery.

ROS Gateway: Enhancing ROS Availability across Multiple Network Environments.

As the adoption of large-scale model-based AI grows, the field of robotics is undergoing significant changes. The emergence of cloud robotics, where advanced tasks are offloaded to fog or cloud servers, is gaining attention. However, the widely used Robot Operating System (ROS) does not support communication between robot software across different networks. This paper introduces ROS Gateway, a middleware designed to improve the usability and extend the communication range of ROS in multi-network environments, which is important for processing sensor data in cloud robotics. We detail its structure, protocols, and algorithms, highlighting improvements over traditional ROS configurations. The ROS Gateway efficiently handles high-volume data from advanced sensors such as depth cameras and LiDAR, ensuring reliable transmission. Based on the rosbridge protocol and implemented in Python 3, ROS Gateway is compatible with rosbridge-based tools and runs on both x86 and ARM-based Linux environments. Our experiments show that the ROS Gateway significantly improves performance metrics such as topic rate and delay compared to standard ROS setups. We also provide predictive formulas for topic receive rates to guide the design and deployment of robotic applications using ROS Gateway, supporting performance estimation and system optimization. These enhancements are essential for developing responsive and intelligent robotic systems in dynamic environments.

Task Scheduling Strategy of Logistics Cloud Robot Based on Edge Computing

Task Scheduling Strategy of Logistics Cloud Robot Based on Edge Computing

Simulation Analysis and Comparative Study of MOSGT with Other Techniques

This paper presents a comparative study of the Multi-Objective Symmetric Game Theory (MOSGT) for task offloading in cloud robots with existing techniques such as Genetic Algorithm 4 Collaborative Computation Offloading (GA4CCO) and Approximation Collaborative Computation Offloading (ACCO). The study employs a simulation analysis to evaluate the performance of the proposed MOSGT model in a cloud robotics context. The results of the simulation study reveal that the MOSGT model reduces system cost by approximately 35% compared to GA4CCO and by 20% compared to ACCO. Furthermore, the MOSGT model exhibits a reduced rate of 30% less than GA4CCO and 25% minimal than ACCO in terms of iteration. These findings underscore the potential of MOSGT as a promising solution for efficient task offloading in cloud robots, offering significant improvements in computational complexity and service quality in edge computing.

Binary task offloading strategy for cloud robots using improved game theory in cloud-edge collaboration

Binary task offloading strategy for cloud robots using improved game theory in cloud-edge collaboration

Designing an Uncrewed Aircraft Systems Control Model for an Air-to-Ground Collaborative System

<div>In autonomous technology, uncrewed aircraft systems have already become the preferred platform for the research and development of flight control systems. Although they are subjected to following and satisfying complicated scenarios of control stations, this high dependency on a specific control framework limits them in their application process and reduces the flight self-organizing network. In this article, we present a developed multilayer control system protocol with the additional supportive manned aircraft layer (<i>Tender</i>). The novelty of the introduced model is that uncrewed aircraft systems are monitored and navigated by the tender, and then based on the suggested scheme, data flows are controlled and transferred across the network by the developed cloud–robotics approach in the ground station layer. Therefore, it has been tried to design a semi-autonomous control network to gather data that combines human observation and the automotive nature of uncrewed aircraft systems. To ensure the accuracy and correctness of the model, we simulate our approach in the software-in-the-loop using its web-based interface with new configurations in the hardware and software architecture of the network. Results will be examined by the in-order per message delay, which has recorded a considerably low latency in both the uplink and downlink data transmission processes. This optimization is achieved along with maintaining the quality of data.</div>

Enhancing Autonomous Operations in Smart Objects and Devices through the Internet of Robotic Things

This study investigates the field of the Internet of Robotic Things (IoRT) and its capacity to transform the functioning of mobile context and robots’ awareness systems. IoRT facilitates autonomous operations in smart objects and devices via the use of data analytics technologies, intelligent data processing tools, deep reinforcement learning, and edge computing techniques. This article examines the use of sensor networks, cloud robotics, machine learning algorithms, and collaborative context-aware robotic networks for the purpose of enhancing job performance, decision-making skills, and operational efficiency in diverse industrial and collaborative settings. The research also investigates the incorporation of route planning tools and motion, cognitive decision-making processes, and sensor data to improve the efficiency of robotic systems in tasks involving object handling. Furthermore, this study investigates the impact of cloud computing, wireless sensor networks, and cognitive approaches on enhancing inventory allocation procedures and company performance. The main purpose of this article is to provide a scholarly contribution to the field of IoRT by exploring its technological advancements and examining its potential applications across many sectors.

XBot2D: towards a robotics hybrid cloud architecture for field robotics.

Nowadays, robotics applications requiring the execution of complex tasks in real-world scenarios are still facing many challenges related to highly unstructured and dynamic environments in domains such as emergency response and search and rescue where robots have to operate for prolonged periods trading off computational performance with increased power autonomy and vice versa. In particular, there is a crucial need for robots capable of adapting to such settings while at the same time providing robustness and extended power autonomy. A possible approach to overcome the conflicting demand of a computational performing system with the need for long power autonomy is represented by cloud robotics, which can boost the computational capabilities of the robot while reducing the energy consumption by exploiting the offload of resources to the cloud. Nevertheless, the communication constraint due to limited bandwidth, latency, and connectivity, typical of field robotics, makes cloud-enabled robotics solutions challenging to deploy in real-world applications. In this context, we designed and realized the XBot2D software architecture, which provides a hybrid cloud manager capable of dynamically and seamlessly allocating robotics skills to perform a distributed computation based on the current network condition and the required latency, and computational/energy resources of the robot in use. The proposed framework leverage on the two dimensions, i.e., 2D (local and cloud), in a transparent way for the user, providing support for Real-Time (RT) skills execution on the local robot, as well as machine learning and A.I. resources on the cloud with the possibility to automatically relocate the above based on the required performances and communication quality. XBot2D implementation and its functionalities are presented and validated in realistic tasks involving the CENTAURO robot and the Amazon Web Service Elastic Computing Cloud (AWS EC2) infrastructure with different network conditions.

Deep Q-Learning-Based Dynamic Management of a Robotic Cluster

The ever-increasing demands for autonomy and precision have led to the development of heavily computational multi-robot system (MRS). However, numerous missions exclude the use of robotic cloud. Another solution is to use the robotic cluster to locally distribute the computational load. This complex distribution requires adaptability to come up with a dynamic and uncertain environment. Classical approaches are too limited to solve this problem, but recent advances in reinforcement learning and deep learning offer new opportunities. In this paper we propose a new Deep Q-Network (DQN) based approaches where the MRS learns to distribute tasks directly from experience. Since the problem complexity leads to a curse of dimensionality, we use two specific methods, a new branching architecture, called Branching Dueling Q-Network (BDQ), and our own optimized multi-agent solution and we compare them with classical Market-based approaches as well as with non-distributed and purely local solutions. Our study shows the relevancy of learning-based methods for task mapping and also highlight the BDQ architecture capacity to solve high dimensional state space problems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A lot of applications in industry like area exploration and monitoring can be efficiently delegated to a group of small-size robots or autonomous vehicles with advantages like reliability and cost in respect of single-robot solutions. But autonomy requires high and increasing compute-intensive tasks such as computer-vision. On the other hand small robots have energy constraints, limited embedded computing capacities and usually restricted and/or unreliable communications that limit the use of cloud resources. An alternative solution to cope with this problem consists in sharing the computing resources of the group of robots. Previous work was a proof of concept limited to the parallelisation of a single specific task. In this paper we formalize a general method that allows the group of robots to learn on the field how to efficiently distribute tasks in order to optimize the execution time of a mission under energy constraint. We demonstrate the relevancy of our solution over market-based and non-distributed approaches by means of intensive simulations. This successful study is a necessary first step towards distribution and parallelisation of computation tasks over a robotic cluster. The next steps, not tested yet, will address hardware in the loop simulation and finally a real-life mission with a group of robots.

Two-stage hybrid genetic algorithm for robot cloud service selection

Robot cloud service platform is a combination of cloud computing and robotics, providing intelligent cloud services for many robots. However, to select a cloud service that satisfys the robot’s requirements from the massive services with different QoS indicator in the cloud platform is an NP hard problem. In this paper, based on the cost model between the cloud platform, cloud services and cloud service robotics, we propose a two-stage service selection strategy, namely, candidate services selection stage according to the specific QoS requirements of service robots and final cost optimization stage. Additionally, with respect to optimizing the final cost for the model, we propose a Dynamic Vector Hybrid Genetic Algorithm (DVHGA) that is integrated with local and global search process as well as a three-phase parameter updating policy. Specifically, inspired by momentum optimization in deep learning, dynamic vector is integrated with DVHGA to modify the weights of QoS and ensure the reasonable allocation of resources. Moreover, we suggest a linear evaluation method for the service robots and the cloud platform concerning time and final cost at the same time, which could be expected to be used in the real application environment. Finally, the empirical results demonstrate that the proposed DVHGA outperforms other benchmark algorithms, i.e., DABC, ESWOA, GA, PGA and GA-PSO, in convergence rate, total final cost and evaluation score.

An intelligent cloud robotics personalized medicine system by assessment of DNA and electronic health records using fuzzy-based progressive alignment sequence (F-Pas)

An intelligent cloud robotics personalized medicine system by assessment of DNA and electronic health records using fuzzy-based progressive alignment sequence (F-Pas)

Co-design of communication and machine inference for cloud robotics

Co-design of communication and machine inference for cloud robotics

Cost-based hierarchy genetic algorithm for service scheduling in robot cloud platform

Service robot cloud platform is effective method to improve the intelligence of robots. An efficient cloud service scheduling algorithm is the basis of ensuring service quality and platform concurrency. In this paper, Hierarchy Genetic Algorithm of robot service(RHGA) is presented to solve the scheduling problem with the crucial constraints. Firstly, the limitations and attributes of the cloud service robots and cloud services are presented and boiled down to an important optimization goal. Secondly, three factors (i.e. evolutionary factor, hunting factor and parent similarity) are integrated with RHGA to promote the efficiency of small-scale service invocations and improve the performance of large-scale service invocations on the platform. Finally, a series of experiments are conducted on several service scheduling algorithms, including four traditional efficient algorithms and two state-of-art algorithms. The experimental results demonstrate that the RHGA can enhance the performance on small-scale service scheduling and ensure its excellent ability in large-scale service scheduling. Moreover, the empirical studies also prove that our proposal has a better performance in service scheduling completion time and cost-savings with comparison to other methods.

AppraisalCloudPCT: A Computational Model of Emotions for Socially Interactive Robots for Autistic Rehabilitation.

Computational models of emotions can not only improve the effectiveness and efficiency of human-robot interaction but also coordinate a robot to adapt to its environment better. When designing computational models of emotions for socially interactive robots, especially for robots for people with special needs such as autistic children, one should take into account the social and communicative characteristics of such groups of people. This article presents a novel computational model of emotions called AppraisalCloudPCT that is suitable for socially interactive robots that can be adopted in autistic rehabilitation which, to the best of our knowledge, is the first computational model of emotions built for robots that can satisfy the needs of a special group of people such as autistic children. To begin with, some fundamental and notable computational models of emotions (e.g., OCC, Scherer's appraisal theory, PAD) that have deep and profound influence on building some significant models (e.g., PRESENCE, iGrace, xEmotion) for socially interactive robots are revisited. Then, a comparative assessment between our AppraisalCloudPCT and other five significant models for socially interactive robots is conducted. Great efforts have been made in building our proposed model to meet all of the six criteria for comparison, by adopting the appraisal theories on emotions, perceptual control theory on emotions, a component model view of appraisal models, and cloud robotics. Details of how to implement our model in a socially interactive robot we developed for autistic rehabilitation are also elaborated in this article. Future studies should examine how our model performs in different robots and also in more interactive scenarios.

Static Algorithm Allocation with Duplication in Robotic Network Cloud Systems

Robotic networks can be used to accomplish tasks that exceed the capacity of a single robot. In a robotic network, robots can work together to accomplish a common task. Cloud robotics allows robots to benefit from the massive storage and computing power of the cloud. Previous studies mainly focus on minimizing the cost of resource retrieval by robots by knowing the resource allocation in advance. Duplicating algorithms on multiple nodes can reduce the total time required to execute a task. We address the question of which algorithms should be duplicated and where the duplicates should be placed to improve overall performance. We have developed a procedure to answer wherein a robotic network cloud system should algorithms be executed and whether they should be duplicated to achieve optimal performance in terms of overall task execution time for all robots. Our proposed duplication procedure is optimal in the sense that the number of duplicated algorithms is minimal, while the result provides minimal overall completion time for all robots.

Hakoniwa-ros2sim：仮想環境を活用したROS 2アプリケーションのシミュレーション手法

We are conducting research and development of Hakoniwa, a virtual simulation environment in the age of IoT and cloud robotics. This research aims to deploy the core functionality of Hakoniwa for simulation of ROS 2 applications. The proposed method mainly consists of Docker and Unity, and allows engineers to easily try out robot development as many times as they wish on multiple platforms independent of OS environment.

Cloud Robotics Cybersecurity: A Novel Survey on Cloud-Based Robotic Platform for Network Security

Cloud Robotics Cybersecurity: A Novel Survey on Cloud-Based Robotic Platform for Network Security

A novel high-efficiency searchable encryption scheme under robot cloud computing environment

This paper proposed a high-efficiency searchable encryption scheme under cloud computing environment. Through the matching calculation of keyword index set and keyword trapdoor generated by the data user, the searchable encrypted mechanism is realised. By utilising the powerful computing resources of the cloud server, the pre-decryption operation is introduced to reduce the computing time cost of the data user. The cloud server does not know anything about the original file in the cloud through a trapdoor corresponding to the user-provided keyword (the trapdoor retrieves files containing a specific keyword from a large number of encrypted files). Security analysis shows that the new scheme cannot leak data privacy information and has stronger security than other state-of-the-art schemes.

Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things

The purpose of our systematic review was to inspect the recently published research on Internet of Robotic Things (IoRT) and harmonize the assimilations it articulates on remote big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms. The research problems were whether robotic manufacturing processes and industrial wireless sensor networks shape IoRT and lead to improved product quality by use of remote big data management tools, whether IoRT devices communicate autonomously regarding event modeling and forecasting by leveraging machine learning and clustering algorithms, sensing and computing technologies, and image processing tools, and whether smart connected objects, situational awareness algorithms, and edge computing technologies configure IoRT systems and cloud robotics in relation to distributed task coordination through visual perception and environment mapping algorithms. A Shiny app was harnessed for Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to configure the flow diagram integrating evidence-based gathered and processed data (the search outcomes and screening procedures). A quantitative literature review of ProQuest, Scopus, and the Web of Science databases was carried out throughout June and October 2022, with search terms including “Internet of Robotic Things” + “remote big data management tools”, “sensing and computing technologies”, and “visual perception and environment mapping algorithms”. Artificial intelligence and intelligent workflows by use of AMSTAR (Assessing the Methodological Quality of Systematic Reviews), Dedoose, DistillerSR, and SRDR (Systematic Review Data Repository) have been deployed as data extraction tools for literature collection, screening, and evaluation, for document flow monitoring, for inspecting qualitative and mixed methods research, and for establishing robust outcomes and correlations. For bibliometric mapping by use of data visualization, Dimensions AI was leveraged and with regards to layout algorithms, VOSviewer was harnessed.

Multi-granularity service composition in industrial cloud robotics

• A multi-granularity service composition model in industrial cloud robotics is built considering both fine-grained and coarse-grained robotic services. • A multi-granularity service matching strategy is designed based on service matching constraints. • Six multi-objective evolutionary algorithms are adopted and compared for the multi-granularity service composition problem . Industrial cloud robotics employs cloud computing technology to provide various operational services, such as robotic control modules that enable customized screwing and welding. Service composition technology enables the flexible implementation of complex industrial robotic applications based on the collaboration of multiple industrial cloud robotic services. Most studies considered cloud robotic services with a single robotic manipulator with a fixed function. To utilize the advantages of coarse-grained services encapsulated by multi-functional robots, manipulators, and control applications, a multi-granularity service composition method is introduced considering the multi-functional resources and capabilities of the cloud robotic services. Then a quality-of-service-aware multi-granularity robotic service composition model is built to evaluate the composition solution. Furthermore, a multi-granularity robotic service matching strategy is proposed according to the matching constraints of coarse-grained services. Six representative multi-objective evolutionary algorithms are adopted to optimize five quality-of-service attributes of the composite service simultaneously. Experiments demonstrate that the proposed multi-granularity robotic service composition method can remarkably improve the quality of robotic composite services for complex manufacturing tasks by utilizing coarse-grained services in addition to fine-grained services. The performances of six multi-objective evolutionary algorithms are compared to determine the most suitable algorithm for the multi-granularity robotic service composition problem.