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Industry 4.0 (I4.0) brings a set of challenges that need to be settled to enable the expected industrial revolution. Cyber-Physical Systems (CPS) and Autonomous Robots are two of mainstream I4.0 technologies and can be combined with other Enabling Technologies to overcome main I4.0 challenges. This Systematic Literature Review (SLR) aimed to understand how these Enabling Technologies were combined with CPS and Autonomous Robots to achieve fully featured I4.0 solutions. The 5C is a CPS architecture which was used as a measurement to CPS compliance. After filtering and processing 15,144 papers from six different academic search engines, it was found that full 5C compliant solutions are rare to find; that not all enabling technologies were integrated; and most of existing research focused in implementing practical solutions, but comparison to conventional approaches is scarce. We conclude that there is a need for further research to accomplish full 5C I4.0. [ABSTRACT FROM AUTHOR]

This editorial note is dedicated to the 10th Scientific Conference which was held on June 2022 in Galati, Romania, and was organized by the Council of Doctoral Schools of the "Dunarea de Jos" University of Galati (SCDS-UDJG). Three articles in this Special Issue present findings in the field of mechanical and industrial engineering. Four articles present studies related to artificial intelligence technologies, representing the majority of papers published in this Special Issue. [Extracted from the article]

AUVs are autonomous underwater robots equipped with advanced sensors and navigation systems. Due to the complexity and uncertainty of the marine environment, AUVs are susceptible to the effects of the marine environment and may experience communication delays or even accidents. Based on the aforementioned issues, this paper proposes a prediction method for lost AUVs based on an adaptive optimization depth BiLSTM (AWOA-DBiLSTM) neural network model. To enhance prediction accuracy, AWOA-DBiLSTM employs a double BiLSTM to extract AUV features from positional information and physical attitude. Additionally, AWOA-DBiLSTM utilizes a gating mechanism to filter and reset physical attitude feature information to obtain features associated with positional information. After undergoing filtering operations, the physical attitude information of the AUV is fused with the position information to achieve trajectory prediction. For the first time, the differentiation and stratified extraction of AUV data features are presented in this paper. The experimental results demonstrate that the model achieves significant improvements in prediction accuracy and generalization, and the present study is of great significance for application in the task of predicting the trajectories of lost AUVs. [ABSTRACT FROM AUTHOR]

Nowadays, the use of advanced sensors, such as terrestrial, mobile 3D scanners and photogrammetric imaging, has become the prevalent practice for 3D Reality Modeling (RM) and the digitization of large-scale monuments of Cultural Heritage (CH). In practice, this process is heavily related to the expertise of the surveying team handling the laborious planning and time-consuming execution of the 3D scanning process tailored to each site's specific requirements and constraints. To minimize human intervention, this paper proposes a novel methodology for autonomous 3D Reality Modeling of CH monuments by employing autonomous robotic agents equipped with the appropriate sensors. These autonomous robotic agents are able to carry out the 3D RM process in a systematic, repeatable, and accurate approach. The outcomes of this automated process may also find applications in digital twin platforms, facilitating secure monitoring and the management of cultural heritage sites and spaces, in both indoor and outdoor environments. The main purpose of this paper is the initial release of an Industry 4.0-based methodology for reality modeling and the survey of cultural spaces in the scientific community, which will be evaluated in real-life scenarios in future research. [ABSTRACT FROM AUTHOR]

In this paper, we will compare two solution options to determine whether the deployment of an autonomous mobile robot will be a beneficial investment for a company not only in terms of cost, time, and manpower savings, but also in terms of efficiency and reliability in the implementation of selected logistics processes to ensure the smooth running of production. In this paper, we would like to analyse the technology in terms of functions, carrying capacity, and interconnection within the infrastructure in the enterprise. The logistics processes from material order to export to the final customer will be analysed when an automatically controlled robot is deployed. One of the solutions discusses the use of personnel and material handling equipment (tractor with transport kit). The second solution discusses the deployment of the robot and selected logistics processes. The paper concludes with a summary of both options in terms of time, cost, and efficiency of the solution. [ABSTRACT FROM AUTHOR]

Featured Application: An autonomous mobile robot (AMR) is a self-navigating vehicle capable of executing specific tasks such as transportation and cruising. As a crucial component of the logistics system in Industry 4.0 and smart factories, AMRs leverage sensors, including cameras, to gather information about their external environment and chart navigational paths. Their applications are diverse, spanning across warehousing, manufacturing, healthcare, hazardous locations, and specialized industries. Road recognition, a key feature of AMRs, employs technologies like semantic segmentation to extract high-level scene information, including object locations and semantic data from images. This technology can be integrated with Visual Simultaneous Localization and Mapping (SLAM), enhancing the AMR's ability to navigate complex environments. This integration is pivotal in enabling AMRs to successfully complete their navigation tasks. The rapid development and application of AMRs is important for Industry 4.0 and smart logistics. For large-scale dynamic flat warehouses, vision-based road recognition amidst complex obstacles is paramount for improving navigation efficiency and flexibility, while avoiding frequent manual settings. However, current mainstream road recognition methods face significant challenges of unsatisfactory accuracy and efficiency, as well as the lack of a large-scale high-quality dataset. To address this, this paper introduces IndoorPathNet, a transfer-learning-based Bird's Eye View (BEV) indoor path segmentation network that furnishes directional guidance to AMRs through real-time segmented indoor pathway maps. IndoorPathNet employs a lightweight U-shaped architecture integrated with spatial self-attention mechanisms to augment the speed and accuracy of indoor pathway segmentation. Moreover, it surmounts the challenge of training posed by the scarcity of publicly available semantic datasets for warehouses through the strategic employment of transfer learning. Comparative experiments conducted between IndoorPathNet and four other lightweight models on the Urban Aerial Vehicle Image Dataset (UAVID) yielded a maximum Intersection Over Union (IOU) of 82.2%. On the Warehouse Indoor Path Dataset, the maximum IOU attained was 98.4% while achieving a processing speed of 9.81 frames per second (FPS) with a 1024 × 1024 input on a single 3060 GPU. [ABSTRACT FROM AUTHOR]

The artificial potential field method has efficient obstacle avoidance ability, but this traditional method suffers from local minima, unreasonable paths, and sudden changes in heading angles during obstacle avoidance, leading to rough paths and increased energy consumption. To enable autonomous mobile robots (AMR) to escape from local minimum traps and move along reasonable, smooth paths while reducing travel time and energy consumption, in this paper, an artificial potential field method based on subareas is proposed. First, the optimal virtual subgoal was obtained around the obstacles based on the relationship between the AMR, obstacles, and goal points in the local environment. This was done according to the virtual subgoal benefit function to solve the local minima problem and select a reasonable path. Secondly, when AMR encountered an obstacle, the subarea-potential field model was utilized to solve problems such as path zigzagging and increased energy consumption due to excessive changes in the turning angle; this helped to smooth its planning path. Through simulations and actual testing, the algorithm in this paper demonstrated smoother heading angle changes, reduced energy consumption, and a 10.95% average reduction in movement time when facing a complex environment. This proves the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present HoloSLAM which is a novel solution to landmark detection issues in the simultaneous localization and mapping (SLAM) problem in autonomous robot navigation. The approach integrates real and virtual worlds to create a novel mapping robotic environment employing a mixed-reality technique and a sensor, namely Microsoft HoloLens. The proposed methodology allows the robot to interact and communicate with its new environment in real-time and overcome the limitations of conventional landmark-based SLAMs by creating and placing some virtual landmarks in situations where real landmarks are scarce, non-existent, or hard to be detected. The proposed approach enhances the robot's perception and navigation capabilities in various robot environments. The overall process contributes to the robot's more accurate understanding of its environment; thus, enabling it to navigate with greater efficiency and effectiveness. In addition, the newly implemented HoloSLAM offers the option to guide the robot to a specific location eliminating the need for explicit navigation instructions. The open-source framework proposed in this paper can benefit the robotics community by providing a more reliable, realistic, and robust mapping solution. The experiments show that the Ellipsoidal-HoloSLAM system is accurate and effectively overcomes the limitations of conventional Ellipsoidal-SLAMs, providing a more precise and detailed mapping of the robot's environment. [ABSTRACT FROM AUTHOR]

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach referred to as the audio-based virtual landmark-based HoloSLAM. This innovative method leverages a single sound source and microphone arrays to estimate the voice-printed speaker's direction. The system allows an autonomous robot equipped with a single microphone array to navigate within indoor environments, interact with specific sound sources, and simultaneously determine its own location while mapping the environment. The proposed method does not require multiple audio sources in the environment nor sensor fusion to extract pertinent information and make accurate sound source estimations. Furthermore, the approach incorporates Robotic Mixed Reality using Microsoft HoloLens to superimpose landmarks, effectively mitigating the audio landmark-related issues of conventional audio-based landmark SLAM, particularly in situations where audio landmarks cannot be discerned, are limited in number, or are completely missing. The paper also evaluates an active speaker detection method, demonstrating its ability to achieve high accuracy in scenarios where audio data are the sole input. Real-time experiments validate the effectiveness of this method, emphasizing its precision and comprehensive mapping capabilities. The results of these experiments showcase the accuracy and efficiency of the proposed system, surpassing the constraints associated with traditional audio-based SLAM techniques, ultimately leading to a more detailed and precise mapping of the robot's surroundings. [ABSTRACT FROM AUTHOR]

This work addresses the challenge of calibrating multiple solid-state LIDAR systems. The study focuses on three different solid-state LIDAR sensors that implement different hardware designs, leading to distinct scanning patterns for each system. Consequently, detecting corresponding points between the point clouds generated by these LIDAR systems—as required for calibration—is a complex task. To overcome this challenge, this paper proposes a method that involves several steps. First, the measurement data are preprocessed to enhance its quality. Next, features are extracted from the acquired point clouds using the Fast Point Feature Histogram method, which categorizes important characteristics of the data. Finally, the extrinsic parameters are computed using the Fast Global Registration technique. The best set of parameters for the pipeline and the calibration success are evaluated using the normalized root mean square error. In a static real-world indoor scenario, a minimum root mean square error of 7 c m was achieved. Importantly, the paper demonstrates that the presented approach is suitable for online use, indicating its potential for real-time applications. By effectively calibrating the solid-state LIDAR systems and establishing point correspondences, this research contributes to the advancement of multi-LIDAR fusion and facilitates accurate perception and mapping in various fields such as autonomous driving, robotics, and environmental monitoring. [ABSTRACT FROM AUTHOR]

The flexibility and versatility associated with autonomous mobile robots (AMR) have facilitated their integration into different types of industries and tasks. However, as the main objective of their implementation on the factory floor is to optimize processes and, consequently, the time associated with them, it is necessary to take into account the environment and congestion to which they are subjected. Localization, on the shop floor and in real time, is an important requirement to optimize the AMRs' trajectory management, thus avoiding livelocks and deadlocks during their movements in partnership with manual forklift operators and logistic trains. Threeof the most commonly used localization techniques in indoor environments (time of flight, angle of arrival, and time difference of arrival), as well as two of the most commonly used indoor localization methods in the industry (ultra-wideband, and ultrasound), are presented and compared in this paper. Furthermore, it identifies and compares three industrial indoor localization solutions: Qorvo, Eliko Kio, and Marvelmind, implemented in an industrial mobile platform, which is the main contribution of this paper. These solutions can be applied to both AMRs and other mobile platforms, such as forklifts and logistic trains. In terms of results, the Marvelmind system, which uses an ultrasound method, was the best solution. [ABSTRACT FROM AUTHOR]

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigation. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy. To address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. First, the fruit tree trunk is identified based on the improved YOLOv4 model; second, the camera coordinates of the tree trunk are calculated using the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined, the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall rate of the improved YOLOv4 model for fruit tree trunk detection are 5.92% and 7.91% higher, respectively, than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows and provide a reference for the autonomous visual navigation of orchard robots. [ABSTRACT FROM AUTHOR]

In order to guide the orchard management robot to realize autonomous steering in the row ends of a complex orchard environment, this paper proposes setting up steering markers in the form of fruit trees at the end of the orchard rows and realizing the rapid detection of the steering markers of the orchard management robot through the fast and accurate recognition and classification of different steering markers. First, a high-precision YOLOv7 model is used, and the depthwise separable convolution (DSC) is used instead of the 3 × 3 ordinary convolution, which improves the speed of model detection; at the same time, in order to avoid a decline in detection accuracy, the Convolutional Block Attention Module (CBAM) is added to the model, and the Focal loss function is introduced to improve the model's attention to the imbalanced samples. Second, a binocular camera is used to quickly detect the steering markers, obtain the position information of the robot to the steering markers, and determine the starting point position of the robot's autonomous steering based on the position information. Our experiments show that the average detection accuracy of the improved YOLOv7 model reaches 96.85%, the detection time of a single image reaches 15.47 ms, and the mean value of the localization error is 0.046 m. Comparing with the YOLOv4, YOLOv4-tiny, YOLOv5-s, and YOLOv7 models, the improved YOLOv7 model outperforms the other models in terms of combined detection time and detection accuracy. Therefore, the model proposed in this paper can quickly and accurately perform steering marker detection and steering start point localization, avoiding problems such as steering errors and untimely steering, shortening the working time and improving the working efficiency. This model also provides a reference and technical support for research on robot autonomous steering in other scenarios. [ABSTRACT FROM AUTHOR]

In smart factories, several mobile and autonomous robots are being utilized in warehouses to reduce overhead and operating costs. In this context, this paper presents a consensus-based fault-resilient intelligent mechanism called Consensual Fault-Resilient Behavior (CFRB). The proposed approach is based on three hierarchical plans: imposition, negotiation, and consensus. Fault resilience is achieved using the collective behavior of a multi-robot system that applies ternary decisions based on these plans. The difference between this paper and our previous work is on the consensual level. As it is suitable for the analysis and design of coordinated behavior between autonomous robots, the consensus plan is restructured and enhanced. The proposed approach is tested and evaluated in a virtual warehouse based on a real environment. In addition, it is compared with other current approaches, and the results are presented, demonstrating its efficiency. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact worldwide, impacting schools, undergraduate, and graduate university education. More than half a million lives have been lost due to COVID-19. Moving towards contactless learning activities has become a research area due to the rapid advancement of technology, particularly in artificial intelligence and robotics. This paper proposes an autonomous service robot for handling multiple teaching assistant duties in the educational field to move towards contactless learning activities during pandemics. We use SLAM to map and navigate the environment to proctor an exam. We also propose a human–robot voice interaction and an academic content personalization algorithm. Our results show that our robot can navigate the environment to proctor students avoiding any static or dynamic obstacles. Our cheating detection system obtained a testing accuracy of 86.85%. Our image-based exam paper scanning system can scan, extract, and process exams with high accuracy. [ABSTRACT FROM AUTHOR]

The article discusses the importance of mathematics in the field of robot control. It highlights the advancements in mathematical methods for solving theoretical and applied problems in various areas of robotics, such as solar tracking systems, robotic manipulators, computer vision, and navigation. The article presents a collection of research papers that demonstrate how mathematics can be used to solve complex problems and improve the practical use of robotic systems. The papers cover a wide range of topics, including adaptive control, logical paradigms, encryption, kinematic modeling, cooperative control, and autonomous navigation. Overall, the article emphasizes the central role of mathematics in advancing the field of robot control. [Extracted from the article]

In recent years, research has been conducted on swarm robot systems in which multiple autonomous mobile robots cooperate to perform tasks. Swarm robot systems are expected to perform high functionality as a group by cooperating with each other, in spite of the limited capabilities of the individual robots. This paper explores a method of simplifying swarm robot controllers as much as possible for swarm robot navigation. If we can achieve autonomous navigation of swarm robots to a target area with minimal resource consumption, they only need to implement the task execution function in that area. This leads to lower costs for swarm robot design and more efficient system architecture design. To address the above aims, this study focuses on heterogeneity. Specifically, we introduce a navigator robot that indirectly guides swarm robots named the worker robots. Heterogeneity in this paper refers to the worker robots and the navigator robots. We design the interaction between the navigator robot and the worker robots to provide a system that guides the worker robots to the destination. [ABSTRACT FROM AUTHOR]

To accomplish the task of detecting and avoiding road signs by mobile robots for autonomous running, in this paper, we propose a method of road sign detection and visual depth perception based on improved Yolov5 and improved centroid depth value filtering. First, the Yolov5 model has a large number of parameters, a large computational volume, and a large model size, which is difficult to deploy to the CPU side (industrial control computer) of the robot mobile platform. To solve this problem, the study proposes a lightweight Yolov5-SC3FB model. Compared with the original Yolov5n model, the Yolov5-SC3FB model only loses lower detection accuracy, the parameter volume is reduced to 0.19 M, the computational volume is reduced to 0.5 GFLOPS, and the model size is only 0.72 MB, making it easy to deploy on mobile robot platforms. Secondly, the obtained depth value of the center point of the bounding box is 0 due to the influence of noise. To solve this problem, we proposed an improved filtering method for the depth value of the center point in the study, and the relative error of its depth measurement is only 2%. Finally, the improved Yolov5-SC3FB model is fused with the improved filtering method for acquiring centroid depth values and the fused algorithm is deployed to the mobile robot platform. We verified the effectiveness of this fusion algorithm for the detection and avoidance of road signs of the robot. Thus, it can enable the mobile robot to correctly perceive the environment and achieve autonomous running. [ABSTRACT FROM AUTHOR]

Vrchota et al. [[21]] evaluated precision agriculture technologies' practical use in agricultural enterprises in the Czech Republic, which is a reference for the development and implementation of precision agriculture technology and equipment in each country. The global agriculture faces critical pressures, including an aging population, rising production costs, and labor shortages. 10.3390/agriculture12071051 14 Liu L., Yuan J., Gong L., Wang X., Liu X. Dynamic Fresh Weight Prediction of Substrate-Cultivated Lettuce Grown in a Solar Greenhouse Based on Phenotypic and Environmental Data. For intelligent agricultural machines, Bai et al. [[1]] designed a monitoring system for the sowing quality of cotton precision planters, to realize the real-time monitoring of the cotton precision seeding operation processes and improve the intelligence level of cotton precision planters. [Extracted from the article]

Localization is an important method for autonomous indoor robots to recognize their positions. Generally, the navigation of a mobile robot is conducted using a camera, Lidar, and global positioning system. However, for an indoor environment, GPS is unavailable. Therefore, a, state-trajectory tracking method is utilized based on a Lidar map. This paper presents the path following of an autonomous indoor mobile robot, that is, a shuttle robot, using a state-flow method via a robot operating system network. MATLAB and Linux high-level computers and an inertial measurement unit sensor are used to obtain the Cartesian coordinate information of a bicycle-type mobile robot. The path following problem can be solved in the state-flow block by setting appropriate time and linear and angular velocity variables. After the predetermined time, the linear and angular velocities are set based on the length of the path and radius of the quarter-circle of the left and right turns in the state-flow block, path planning, which can execute the work effectively, is established using the state-flow algorithm. The state-flow block produces time-series data that are sent to Linux system, which facilitates real-time mobile platform path following scenario. Several cases within the path-following problem of the mobile robot were considered, depending on the linear and angular velocity settings: the mobile robot moved forward and backward, turned in the right and left directions on the circular path. The effectiveness of the method was demonstrated using the desktop-based indoor mobile robot control results. Thus, the paper focuses on the application of the state-flow algorithm to the shuttle robot specifically in the narrow indoor environment. [ABSTRACT FROM AUTHOR]

As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments. [ABSTRACT FROM AUTHOR]

Featured Application: This research contributes to the development of a technological method to obtain highly accurate vehicle trajectory data. The reconstructed trajectory data play a key role in traffic state prediction, traffic management and the decision making of autonomous vehicles and robots. Vehicle trajectory usually suffers from a large number of outliers and observation noises. This paper proposes a novel framework for reconstructing vehicle trajectories. The framework integrates the wavelet transform, Lagrange interpolation and Kalman filtering. The wavelet transform based on waveform decomposition in the time and frequency domain is used to identify the abnormal frequency of a trajectory. Lagrange interpolation is used to estimate the value of data points after outliers are removed. This framework improves computation efficiency in data segmentation. The Kalman filter uses normal and predicted data to obtain reasonable results, and the algorithm makes an optimal estimation that has a better denoising effect. The proposed framework is compared with a baseline framework on the trajectory data in the NGSIM dataset. The experimental results showed that the proposed framework can achieve a 45.76% lower root mean square error, 26.43% higher signal-to-noise ratio and 25.58% higher Pearson correlation coefficient. [ABSTRACT FROM AUTHOR]

This paper presents and discusses the development and deployment of a tour guide robot as part of the 5 g-TOURS EU research project, aimed at developing applications enabled by 5G technology in different use cases. The objective is the development of an autonomous robotic application where intelligence is off-loaded to a remote machine via 5G network, so as to lift most of the computational load from the robot itself. The application uses components that have been widely studied in robotics, (i.e., localization, mapping, planning, interaction). However, the characteristics of the network and interactions with visitors in the wild introduce specific problems which must be taken into account. The paper discusses in detail such problems, summarizing the main results achieved both from the methodologicaland the experimental standpoint, and is completed by the description of the general functional architecture of the whole system, including navigation and operational services. The software implementation is also publicly available. [ABSTRACT FROM AUTHOR]

Autonomous mobile robots have become integral to daily life, providing crucial services across diverse domains. This paper focuses on path following, a fundamental technology and critical element in achieving autonomous mobility. Existing methods predominantly address tracking through steering control, neglecting velocity control or relying on path-specific reference velocities, thereby constraining their generality. In this paper, we propose a novel approach that integrates the conventional pure pursuit algorithm with deep reinforcement learning for a nonholonomic mobile robot. Our methodology employs pure pursuit for steering control and utilizes the soft actor-critic algorithm to train a velocity control strategy within randomly generated path environments. Through simulation and experimental validation, our approach exhibits notable advancements in path convergence and adaptive velocity adjustments to accommodate paths with varying curvatures. Furthermore, this method holds the potential for broader applicability to vehicles adhering to nonholonomic constraints beyond the specific model examined in this paper. In summary, our study contributes to the progression of autonomous mobility by harmonizing conventional algorithms with cutting-edge deep reinforcement learning techniques, enhancing the robustness of path following. [ABSTRACT FROM AUTHOR]

Delivering robots impact many facets of our life, including food delivery and restaurant services, with advancements enabling obstacle overcome, faster delivery, and minimizing human intervention. However, delivering robots remained to experience poor vertical mobilityelevator usage in multi-floor buildings. Incorporating new elevator models into the robot's elevator usage capabilities involves a long process of manufacturer approval and authentication. Furthermore, strict fire-code regulations pose communication barriers between the robot and the elevator. In this paper, we introduce MirrorVision-a novel approach designed for accurate floor detection during vertical mobility, regardless of obstructions blocking the robot's direct line of sight to the elevator number panel. First, we collected and pre-processed a dataset of direct and reflective views of elevator number panels via the preinstalled mirrors. Then, we trained mirrored images in various possibilities to accomplish accurate floor detection. MirrorVision provides a solid mechanism to understand floor numbers at the level of distorted images. Extensive evaluations show that MirrorVision achieves 98.8% accuracy for floor detection in a crowded elevator, while state-of-the-art EfficientDet and YOLOv5 achieved 90.8% and 93.3%, respectively. [ABSTRACT FROM AUTHOR]

Complex and high-computational-cost algorithms are usually the state-of-the-art solution for autonomous driving cases in which non-holonomic robots must be controlled in scenarios with spatial restrictions and interaction with dynamic obstacles while fulfilling at all times safety, comfort, and legal requirements. These highly complex software solutions must cover the high variability of use cases that might appear in traffic conditions, especially when involving scenarios with dynamic obstacles. Reinforcement learning algorithms are seen as a powerful tool in autonomous driving scenarios since the complexity of the algorithm is automatically learned by trial and error with the help of simple reward functions. This paper proposes a methodology to properly define simple reward functions and come up automatically with a complex and successful autonomous driving policy. The proposed methodology has no motion planning module so that the computational power can be limited like in the reactive robotic paradigm. Reactions are learned based on the maximization of the cumulative reward obtained during the learning process. Since the motion is based on the cumulative reward, the proposed algorithm is not bound to any embedded model of the robot and is not being affected by uncertainties of these models or estimators, making it possible to generate trajectories with the consideration of non-holonomic constrains. This paper explains the proposed methodology and discusses the setup of experiments and the results for the validation of the methodology in scenarios with dynamic obstacles. A comparison between the reinforcement learning algorithm and state-of-the-art approaches is also carried out to highlight how the methodology proposed outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Considering the current world trends, the most challenging issue industry is facing revolves around how to reduce the power consumption of electronic systems. Since the invention of computers, electrical energy consumption has increased dramatically; this is due to the emergence of new systems in industry. Systems like industrial robots and autonomous vehicles—including electric vehicles (EVs) and unmanned aerial vehicles (UAVs)—have had a great impact in making human life easier but have also led to higher energy consumption. At present, researchers and developers are actively seeking solutions and patents to optimize the energy consumption of the mentioned systems and generate savings, with the goal of reducing their environmental impact and improving their efficiency and effectiveness. From the literature review, papers related to energy optimization and energy consumption are considered vital, and a huge number of research publications and survey papers discuss it. This paper presents a systematic review of the classification and analysis of various methodologies and solutions that have been developed to enhance the energy performance of robotic systems, focusing on industrial robots, autonomous vehicles, and embedded systems. The aim of this research is to provide a reference point for the existing methods, techniques, and technologies that are available. It compares and evaluates different hardware and software methods related to industrial robots, autonomous vehicles, and embedded systems, highlighting the possible future perspectives in the field. [ABSTRACT FROM AUTHOR]

The development of agricultural robots is an increasingly popular research field aiming at addressing the widespread labor shortages in the farming industry and the ever-increasing food production demands. In many cases, multiple cooperating robots can be deployed in order to reduce task duration, perform an operation not possible with a single robot, or perform an operation more effectively. Building on previous results, this application paper deals with a cooperation strategy that allows two heterogeneous robots to cooperatively carry out grape harvesting, and its implementation is demonstrated. More specifically, the cooperative grape harvesting task involves two heterogeneous robots, where one robot (i.e., the expert) is assigned the grape harvesting task, whereas the second robot (i.e., the helper) is tasked with supporting the harvesting task by carrying the harvested grapes. The proposed cooperative harvesting methodology ensures safe and effective interactions between the robots. Field experiments have been conducted in order firstly to validate the effectiveness of the coordinated navigation algorithm and secondly to demonstrate the proposed cooperative harvesting method. The paper reports on the conclusions drawn from the field experiments, and recommendations for future enhancements are made. The potential of sophisticated as well as explainable decision-making based on logic for enhancing the cooperation of autonomous robots in agricultural applications is discussed in the context of mathematical lattice theory. [ABSTRACT FROM AUTHOR]

Simultaneous location and mapping (SLAM) technology is key in robot autonomous navigation. Most visual SLAM (VSLAM) algorithms for dynamic environments cannot achieve sufficient positioning accuracy and real-time performance simultaneously. When the dynamic object proportion is too high, the VSLAM algorithm will collapse. To solve the above problems, this paper proposes an indoor dynamic VSLAM algorithm called YDD-SLAM based on ORB-SLAM3, which introduces the YOLOv5 object detection algorithm and integrates deep information. Firstly, the objects detected by YOLOv5 are divided into eight subcategories according to their motion characteristics and depth values. Secondly, the depth ranges of the dynamic object and potentially dynamic object in the moving state in the scene are calculated. Simultaneously, the depth value of the feature point in the detection box is compared with that of the feature point in the detection box to determine whether the point is a dynamic feature point; if it is, the dynamic feature point is eliminated. Further, multiple feature point optimization strategies were developed for VSLAM in dynamic environments. A public data set and an actual dynamic scenario were used for testing. The accuracy of the proposed algorithm was significantly improved compared to that of ORB-SLAM3. This work provides a theoretical foundation for the practical application of a dynamic VSLAM algorithm. [ABSTRACT FROM AUTHOR]

The potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must be scheduled and optimized to guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at runtime. In this paper, we consider a necessary first step towards this goal of computational awareness in autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot's operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal. [ABSTRACT FROM AUTHOR]

With the advancement in artificial intelligence technology, autonomous mobile robots have been utilized in various applications. In autonomous driving scenarios, object classification is essential for robot navigation. To perform this task, light detection and ranging (LiDAR) sensors, which can obtain depth and height information and have higher resolution than radio detection and ranging (radar) sensors, are preferred over camera sensors. The pillar-based method employs a pillar feature encoder (PFE) to encode 3D LiDAR point clouds into 2D images, enabling high-speed inference using 2D convolutional neural networks. Although the pillar-based method is employed to ensure real-time responsiveness of autonomous driving systems, research on accelerating the PFE is not actively being conducted, although the PFE consumes a significant amount of computation time within the system. Therefore, this paper proposes a PFE hardware accelerator and pillar-based object classification model for autonomous mobile robots. The proposed object classification model was trained and tested using 2971 datasets comprising eight classes, achieving a classification accuracy of 94.3%. The PFE hardware accelerator was implemented in a field-programmable gate array (FPGA) through a register-transfer level design, which achieved a 40 times speedup compared with the firmware for the ARM Cortex-A53 microprocessor unit; the object classification network was implemented in the FPGA using the FINN framework. By integrating the PFE and object classification network, we implemented a real-time pillar-based object classification acceleration system on an FPGA with a latency of 6.41 ms. [ABSTRACT FROM AUTHOR]

For successful human–robot collaboration, it is crucial to establish and sustain quality interaction between humans and robots, making it essential to facilitate human–robot interaction (HRI) effectively. The evolution of robot intelligence now enables robots to take a proactive role in initiating and sustaining HRI, thereby allowing humans to concentrate more on their primary tasks. In this paper, we introduce a system known as the Robot-Facilitated Interaction System (RFIS), where mobile robots are employed to perform identification, tracking, re-identification, and gesture recognition in an integrated framework to ensure anytime readiness for HRI. We implemented the RFIS on an autonomous mobile robot used for transporting a patient, to demonstrate proactive, real-time, and user-friendly interaction with a caretaker involved in monitoring and nursing the patient. In the implementation, we focused on the efficient and robust integration of various interaction facilitation modules within a real-time HRI system that operates in an edge computing environment. Experimental results show that the RFIS, as a comprehensive system integrating caretaker recognition, tracking, re-identification, and gesture recognition, can provide an overall high quality of interaction in HRI facilitation with average accuracies exceeding 90% during real-time operations at 5 FPS. [ABSTRACT FROM AUTHOR]

This paper presents a complete system architecture for multi-robot coordination for unbalanced task assignments, where a number of robots are supposed to visit and accomplish missions at different locations. The proposed method first clusters tasks into clusters according to the number of robots, then the assignment is done in the form of one-cluster-to-one-robot, followed by solving the traveling salesman problem (TSP) to determine the visiting order of tasks within each cluster. A nonlinear model predictive controller (NMPC) is designed for robots to navigate to their assigned tasks while avoiding colliding with other robots. Several simulations are conducted to evaluate the feasibility of the proposed architecture. Video examples of the simulations can be viewed at https://youtu.be/5C7zTnv2sfo and https://youtu.be/-JtSg5V2fTI?si=7PfzZbleOOsRdzRd. Besides, we compare the cluster-based assignment with a simulated annealing (SA) algorithm, one of the typical solutions for the multiple traveling salesman problem (mTSP), and the result reveals that with a similar optimization effect, the cluster-based assignment demonstrates a notable reduction in computation time. This efficiency becomes increasingly pronounced as the task-to-agent ratio grows. [ABSTRACT FROM AUTHOR]

Addressing the challenges for detecting underwater damage to the structures with pile foundation, this study presents the design of an adsorption-operated robotic system. Initially, a prototype is proposed for an adsorption-operated robot that is equipped with an automatic movement mechanism. To account for the disturbance of the drag force in the control system, a hydrodynamic numerical model for the detection robot is established. The numerical results of the drag force are then integrated into the control system, leading to the design of a force-redeemed active disturbance rejection controller. Furthermore, a novel detection algorithm based on the image segmentation network UNet is developed for the automatic identification of damages. The performance of the controller and detection algorithm is evaluated against traditional methods. The results indicate that the proposed force-redeemed active disturbance rejection controller outperforms traditional PID and ADR controllers in terms of overshoot and adjustment time. Moreover, the developed detection algorithm exhibits superior performance in precision, F1 score, and mean intersection over union compared to the traditional algorithms of FCN, PSPnet, Deeplabv3, and UNet. Overall, this paper advances the technology of autonomous robots for underwater detection in the structures with pile foundation. [ABSTRACT FROM AUTHOR]

This paper introduces the Color Histogram Contouring (CHC) method, a new training-less approach to object detection that emphasizes the distinctive features in chrominance components. By building a chrominance-rich feature vector with a bin size of 1, the proposed CHC method exploits the precise information in chrominance features without increasing bin sizes, which can lead to false detections. This feature vector demonstrates invariance to lighting changes and is designed to mimic the opponent color axes used by the human visual system. The proposed CHC algorithm iterates over non-zero histogram bins of unique color features in the model, creating a feature vector for each, and emphasizes those matching in both the scene and model histograms. When both model and scene histograms for these unique features align, it ensures the presence of the model in the scene image. Extensive experiments across various scenarios show that the proposed CHC technique outperforms the benchmark training-less Swain and Ballard method and the algorithm of Viola and Jones. Additionally, a comparative experiment with the state-of-the-art You Only Look Once (YOLO) technique reveals that the proposed CHC technique surpasses YOLO in scenarios with limited training data, highlighting a significant advancement in training-less object detection. This approach offers a valuable addition to computer vision, providing an effective training-less solution for real-time autonomous robot localization and mapping in unknown environments. [ABSTRACT FROM AUTHOR]

Robotic inspection is advancing in performance capabilities and is now being considered for industrial applications beyond laboratory experiments. As industries increasingly rely on complex machinery, pipelines, and structures, the need for precise and reliable inspection methods becomes paramount to ensure operational integrity and mitigate risks. AI-assisted autonomous mobile robots offer the potential to automate inspection processes, reduce human error, and provide real-time insights into asset conditions. A primary concern is the necessity to validate the performance of these systems under real-world conditions. While laboratory tests and simulations can provide valuable insights, the true efficacy of AI algorithms and robotic platforms can only be determined through rigorous field testing and validation. This paper aligns with this need by evaluating the performance of one-stage models for object detection in tasks that support and enhance the perception capabilities of autonomous mobile robots. The evaluation addresses both the execution of assigned tasks and the robot's own navigation. Our benchmark of classification models for robotic inspection considers three real-world transportation and logistics use cases, as well as several generations of the well-known YOLO architecture. The performance results from field tests using real robotic devices equipped with such object detection capabilities are promising, and expose the enormous potential and actionability of autonomous robotic systems for fully automated inspection and maintenance in open-world settings. [ABSTRACT FROM AUTHOR]

This paper explores the application of Deep Reinforcement Learning (DRL) and Sim2Real strategies to enhance the autonomy of beach-cleaning robots. Experiments demonstrate that DRL agents, initially refined in simulations, effectively transfer their navigation skills to real-world scenarios, achieving precise and efficient operation in complex natural environments. This method provides a scalable and effective solution for beach conservation, establishing a significant precedent for the use of autonomous robots in environmental management. The key advancements include the ability of robots to adhere to predefined routes and dynamically avoid obstacles. Additionally, a newly developed platform validates the Sim2Real strategy, proving its capability to bridge the gap between simulated training and practical application, thus offering a robust methodology for addressing real-life environmental challenges. [ABSTRACT FROM AUTHOR]

This paper explores two non‐linear control techniques for designing an effective control system for an omnidirectional autonomous mobile robot with four Mecanum wheels. Due to the unique wheel structure and four separate wheels, the robot has non‐linear dynamics, multiple inputs and outputs. The first technique uses the state‐dependent Riccati equation (SDRE) to address optimal non‐linear control while considering energy and time constraints. The second technique, using an intermediate variable θ$\theta $, has expanded the Hamilton‐Jacobi‐Belman equation in terms of the power series. Consequently, these equations are reduced to a set of recursive Lyapunov algebraic equations, leading to a closed‐form solution for solving the non‐linear optimal control problem. Finally, the maneuverability and path‐tracking capability of the robot are examined by highlighting the non‐linear term through numerical simulation. [ABSTRACT FROM AUTHOR]

Robotic control is a fundamental part of autonomous robots. Modular legged and climbing robots are complex machines made up of a variety of subsystems, ranging from a single robot with simple legs to a complex system composed of multiple legs (or modules) with computing power and sensitivity. Their complexity, which is increased by the fact of needing elements for climbing, makes a correct structure crucial to achieve a complete, robust, and versatile system during its operation. Control architectures for legged robots are distinguished from other software architectures because of the special needs of these systems. In this paper, we present an original classification of modular legged and climbing robots, a comprehensive review of the most important control architectures in robotics, focusing on the control of modular legged and climbing robots, and a comparison of their features. The control architecture comparison aims to provide the analytical tools necessary to make informed decisions tailored to the specific needs of your robotic applications. This article includes a review and classification of modular legged and climbing robots, breaking down each category separately. [ABSTRACT FROM AUTHOR]

This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios. [ABSTRACT FROM AUTHOR]