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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]

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 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]

We seem to be on the cusp of an AI-driven revolution in military affairs. Scholars have explored many aspects of this revolution but one of the most vibrant debates has addressed the question of lethal autonomous weapons. Some scholars believe that autonomous weapons, and especially autonomous drone swarms, are about to colonise the battlefield. This paper assesses this argument. It identifies three common mistakes in discussions of lethal autonomy. Scholars overestimate the capability of autonomous drone swarms. They underestimate their dependence on other weapon systems. Finally, they presume that autonomous weapons will favour the offence. This paper rejects all three claims. [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 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]

This paper presents an obstacle detection and tracking framework for mobile robots and autonomous vehicles equipped with LiDAR (Light Detecting and Range finding) sensors. The framework contains a detection module (DM) for clustering the point cloud and modeling the obstacles, and a tracking module (TM) for recognizing the obstacles and estimating their velocities. In order to detect the obstacles, DM segments the point cloud by finding the gaps in it. Detected obstacles are modeled by one or two line segments depending on their geometry. TM gets the line segments returned by DM as the obstacles to track. To this end, first, the obstacles are labeled and their features are stored. Thereafter, a set of equations are solved to recognize the labeled obstacles. Finally, the Kalman filter is used to calculate the translational and rotational velocities of the obstacles. The framework is evaluated in experiments on a robot platform and using the KITTI dataset. The results indicate satisfactory performance in terms of effectiveness and quickness and confirm that DM and TM are qualified enough to perform in real time as ancillary modules of mobile robots and autonomous vehicles. Especially, because of robust and accurate obstacle modeling, the velocity diagrams are smooth and coherent, a point which is not seen in similar researches. [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]

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]

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]

Cameras are the prevalent sensors used for perception in autonomous robotic systems, but their initial calibration may degrade over time due to dynamic factors. This may lead to a failure of downstream tasks, such as simultaneous localization and mapping (SLAM) or object recognition. Hence, a computationally lightweight process that detects the decalibration is of interest. We describe a modification of StOCaMo, an online calibration monitoring procedure for a stereoscopic system. The method uses robust kernel correlation based on epipolar constraints; it validates extrinsic calibration parameters on a single frame with no temporal tracking. In this paper, we present a modified StOCaMo with an improved recall rate on small decalibrations through a confirmation technique based on resampled variance. With fixed parameters learned on a realistic synthetic dataset from CARLA, StOCaMo and its proposed modification were tested on multiple sequences from two real-world datasets: KITTI and EuRoC MAV. The modification improved the recall of StOCaMo by 25 % (to 91 % and 82 %, respectively), and the accuracy by 12 % (to 94.7 % and 87.5 %, respectively), while labeling at most one-third of the input data as uninformative. The upgraded method achieved the rank correlation between StOCaMo V-index and downstream SLAM error of 0.78 (Spearman). [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]

Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

Autonomous robots are designed to support humans in diverse ways including companionship and assistance, as technology evolves, the burden of physical tasks which were performed by humans in the past are getting reduced. Autonomous guidance and navigation are popular fields that use mobile robots to replace humans. These robots are widely used in space, defence, education, and industry and require navigating safely through different environments. A significant aspect of Autonomous Robot Navigation is an obstacle and pedestrian avoidance. Most of the outdoor guiding robots follow the concept of SLAM (Simultaneous Localisation and Mapping) for moving from the initial point to the goal point. Moreover, image processing plays a vital role in the detection and recognition between humans and objects. The existing methodology comprises implementing autonomous tour guide robots are based in embedded systems which has a high degree of recognition accuracy. This paper provides a comprehensive review of the existing autonomous campus and tour guiding robots. This paper is steered at providing a satellite view of these existing assemblies and the advances made with them. [ABSTRACT FROM AUTHOR]

This paper addresses the control problem of complex temporal logic tasks for nonlinear systems. A new control barrier function (CBF) called time‐varying extent‐compatible control barrier function (TV‐ECCBF) is proposed, which extends the traditional CBF by considering time‐varying extent or volumes. The main aim of TV‐ECCBF is to tackle the issue of avoiding obstacles in real‐world situations that have irregular or dynamically changing shapes, which cannot be efficiently resolved through traditional CBF methods. To encode complex task specifications, a control framework is presented based on signal temporal logic (STL). Additionally, we introduce robust semantics to discuss the satisfaction of target formulas. Moreover, the control framework generates controllers automatically based on different types of tasks, exhibiting global performance in achieving complex tasks. Finally, two examples of mobile robot systems are presented to demonstrate the effectiveness of the TV‐ECCBF and the proposed design framework. [ABSTRACT FROM AUTHOR]

Autonomous mobile robots are essential to the industry, and human–robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a long short-term memory (LSTM) neural network that obtains information from the mobile robot's LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack motion capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%. [ABSTRACT FROM AUTHOR]

Digital solutions have become increasingly important for manufacturing companies to increase their productivity, effectiveness, and competitiveness in a global market, which demands low prices, high quality, and fast delivery times. In order to improve production efficiency, it is also necessary to optimize transportation activities in the production floor via digitization and automation of those processes. Many companies have already used or are planning to use autonomous mobile robots (AMR) to manage production logistics more effectively. The rapid development of the Internet of Things (IoT) and the advanced hardware and software of AMR allow them to perform autonomous tasks in dynamic environments, where they can communicate and independently coordinate with other resources, such as machines and systems, and thus decentralize the decisionmaking steps of manufacturing processes. Decentralized decision making allows the manufacturing system to dynamically adapt to changes in the system state and environment. Such developments have affected traditional planning and control methods and decisionmaking processes, but they also require the software and embedded artificial intelligence (AI) algorithms to be more capable of executing these decisions. In this study, we describe how to use a 3D virtual factory concept to integrate an AMR system with AI functionality into the production logistics of the food industry. The paper presents an approach to analyze the performance of AMR in the transportation of goods on the manufacturing plant floor, based on the creation and simulation of the 3D layout, the monitoring of key performance indicators (KPI), and the use of AI for proactive decision making in production planning. A case study of the food industry demonstrates the relevance and feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Geomagnetic matching navigation is extensively utilized for localization and navigation of autonomous robots and vehicles owing to its advantages such as low cost, wide-area coverage, and no cumulative errors. However, due to the influence of magnetometer measurement noise, geomagnetic localization algorithms based on single-point particle filters may encounter mismatches during continuous operation, consequently limiting their long-range localization performance. To address this issue, this paper proposes a real-time sequential particle filter-based geomagnetic localization method. Firstly, this method mitigates the impact of noise during continuous operation while ensuring real-time performance by performing real-time sequential particle filtering. Then, it enhances the long-range positioning accuracy of the method by rectifying the trajectory shape of the odometry through odometry calibration parameters. Finally, by performing secondary matching on the preliminary matching results via the MAGCOM algorithm, the positioning error of the method is further minimized. Experimental results show that the proposed method has higher positioning accuracy compared to related algorithms, resulting in reductions of over 28.58%, 37.11%, and 0.77% in RMSE, max error, and error at the end, respectively. [ABSTRACT FROM AUTHOR]

Various positioning techniques such as Wi-Fi positioning system have been proposed to use in situations where satellite navigation is unavailable. One such system, the muometric positioning system (muPS), was invented for navigation which operates in locations where even radio waves cannot reach such as underwater or underground. muPS takes advantage of a key feature of its probe, cosmic-ray muons, which travel straightforwardly at almost a speed of light in vacuum regardless of the matter they traverse. Similar to other positioning techniques, muPS is a technique to determine the position of a client's muPS receiver within the coordinate defined by reference detectors. This can be achieved either by using time-of-flight (ToF) or angle of arrival (AoA) measurements. The latter configuration (AoA), called the Vector-muPS has recently been invented and the present paper describes the developments of the first prototype of a vector muometric wireless navigation system (MuWNS-V) with this new vector-muPS concept and its demonstration. With MuWNS-V, the reference tracker and the receiver ran wirelessly with fully independent readout systems, and a positioning accuracy of 3.9 cm (RMS) has been achieved. We also evaluated the outcome of measuring continuous indoor localization of a moving receiver with this prototype. Our results indicated that further improvements in positioning accuracy will be attainable by acquiring higher angular resolution of the reference trackers. It is anticipated that "sub-cm level" navigation will be possible for muPS which could be applied to many situations such as future autonomous mobile robot operations. [ABSTRACT FROM AUTHOR]

This paper presents a method that employs an evolutionary normal distributions transform (NDT) for simultaneous localization and mapping (SLAM) using light detection and ranging (LiDAR) for autonomous mobile robots. An adaptive inertia weight and two genetic operators were employed in the Taguchi-based whale optimization algorithm (WOA) to improve the search diversity and avoid local optima. NDT was used to model the environment of differential-drive mobile robots and WOA was applied to optimize the scan matching for the robotic SLAM problem. The NDT-WOA determines the SLAM pose estimation using sensed data from the physical world. A nonholonomic mobile robot was steered to achieve the NDT-WOA SLAM task with the derived robot kinematics and actuator dynamics. The proposed method was implemented in a TurtleBot3 Burger robotic development kit, which includes a single-board computer and an OpenCR control board. The Robot Operating System (ROS) was utilized to implement the evolutionary NDT-WOA SLAM system due to its flexibility, open source, and client library. Simulation and comparisons were conducted to illustrate the efficiency of the proposed NDT-WOA SLAM method compared to other SLAM paradigms. The experimental results show the effectiveness of the proposed evolutionary NDT-WOA SLAM for autonomous mobile robots. The results could have theoretical and practical significance for robotics research. [ABSTRACT FROM AUTHOR]

Recent advancements in autonomous mobile robots have led to significant progress in area coverage tasks. However, challenges persist in optimizing the efficiency and computational complexity of complete coverage path planner (CCPP) algorithms for multi-robot systems, particularly in scenarios requiring revisiting or a double pass in specific locations, such as cleaning robots addressing spilled consumables. This paper presents an innovative approach to tackling the double-pass complete coverage problem using an autonomous inter-reconfigurable robot path planner. Our solution leverages a modified Glasius bio-inspired neural network (GBNN) to facilitate double-pass coverage through inter-reconfiguration between two robots. We compare our proposed algorithm with traditional multi-robot path planning in a centralized system, demonstrating a reduction in algorithm iterations and computation time. Our experimental results underscore the efficacy of the proposed solution in enhancing the efficiency of area coverage tasks. Furthermore, we discuss the implementation details and limitations of our study, providing insights for future research directions in autonomous robotics. [ABSTRACT FROM AUTHOR]

Developing efficient path-planning algorithms is an essential topic in modern robotics and control theory. Autonomous rovers and wheeled and tracked robots require path generators that can efficiently cover the explorable space with minimal redundancy. In this paper, we present a new path-planning algorithm based on the chaotic behavior of the Courbage–Nekorkin neuron model with a coverage control parameter. Our study aims to reduce the number of iterations required to cover the chosen investigated area, which is a typical efficiency criterion for this class of algorithms. To achieve this goal, we implemented a pseudorandom bit generator (PRBG) based on a Courbage–Nekorkin chaotic map, which demonstrates chaotic behavior and successfully passes all statistical tests for randomness. The proposed PRBG generates a bit sequence that can be used to move the tracked robot in four or eight directions in an operation area of arbitrary size. Several statistical metrics were applied to evaluate the algorithm's performance, including the percentage of coverage of the study area and the uniformity of coverage. The performance of several competing path-planning algorithms was analyzed using the chosen metrics when exploring two test areas of the sizes 50 × 50 cells and 100 × 100 cells, respectively, in four and eight directions. The experimental results indicate that the proposed algorithm is superior compared to known chaotic path-planning methods, providing more rapid and uniform coverage with the possibility of controlling the covered area using tunable parameters. In addition, this study revealed the high dependence of the coverage rate on the starting point. To investigate how the coverage rate depends on the choice of chaotic map, we implemented six different PRBGs using various chaotic maps. The obtained results can be efficiently used for solving path-planning tasks in both real-life and virtual (e.g., video games) applications. [ABSTRACT FROM AUTHOR]

This paper proposes an autonomous robotic system to prune sweet pepper leaves using semantic segmentation with deep learning and an articulated manipulator. This system involves three main tasks: the perception of crop parts, the detection of pruning position, and the control of the articulated manipulator. A semantic segmentation neural network is employed to recognize the different parts of the sweet pepper plant, which is then used to create 3D point clouds for detecting the pruning position and the manipulator pose. Eventually, a manipulator robot is controlled to prune the crop part. This article provides a detailed description of the three tasks involved in building the sweet pepper pruning system and how to integrate them. In the experiments, we used a robot arm to manipulate the pruning leaf actions within a certain height range and a depth camera to obtain 3D point clouds. The control program was developed in different modules using various programming languages running on the ROS (Robot Operating System). [ABSTRACT FROM AUTHOR]

Reliable obstacle avoidance, which is essential for safe autonomous robot interaction with the real world, raises various challenges such as difficulties with obstacle perception and latent factor cognition impacting multi-modal obstacle avoidance. In this paper, we propose a Depth visUal Ego-motion Learning (DUEL) model, consisting of a cognitive generation network, a policy decision network and a potential partition network, to learn autonomous obstacle avoidance from expert policies. The DUEL model takes advantage of binocular vision to perceive scene depth. This serves as the input to the cognitive generation network which generates obstacle avoidance policies by maximizing its causal entropy. The policy decision network then optimizes the generation of the policies referring to expert policies. The generated obstacle avoidance policies are simultaneously transferred to the potential partition network to capture the latent factors contained within expert policies and perform multi-modal obstacle avoidance. These three core networks iteratively optimize the multi-modal policies relying on causal entropy and mutual information theorems, which are proven theoretically. Experimental comparisons with state-of-the-art models on 7 metrics demonstrate the effectiveness of the DUEL model. It achieves the best performance with an average ADE (Average Displacement Error) of 0.29 and average FDE (Final Displacement Error) of 0.55 across five different scenarios. Results show that the DUEL model can maintain an average obstacle avoidance success rate of 97% for both simulated and real world scenarios with multiple obstacles, demonstrating its success at capturing latent factors from expert policies. Our source codes are available at https://github.com/ACoTAI/DUEL. [ABSTRACT FROM AUTHOR]

Recently, multi-agent systems have become widespread as essential technologies for various practical problems. An essential problem in multi-agent systems is collaborative automating picking and delivery operations in warehouses. The warehouse commissioning task involves finding specified items in a warehouse and moving them to a specified location using robots. This task is defined as a spatial task-allocation problem (SPATAP) based on a Markov decision process (MDP). It is considered a decentralized multi-agent system rather than a system that manages and optimizes agents in a central manner. Existing research on SPATAP involving modeling the environment as a MDP and applying Monte Carlo tree searches has shown that this approach is efficient. However, there has not been sufficient research into scenarios in which all agents are provided a common plan despite the fact that their actions are decided independently. Thus, previous studies have not considered cooperative robot behaviors with different goals, and the problem where each robot has different goals has not been studied extensively. In terms of the cooperative element, the item exchange approach has not been considered effectively in previous studies. Therefore, in this paper, we focus on the problem of each robot being assigned a different task to optimize the percentage of picking and delivering items in time in social situations. We propose an action-planning method based on the Monte Carlo tree search and an item-exchange method between agents. We also generate a simulator to evaluate the proposed methods. The results of simulations demonstrate that the achievement rate is improved in small- and medium-sized warehouses. However, the achievement rate did not improve in large warehouses because the average distance from the depot to the items increased. [ABSTRACT FROM AUTHOR]

Robot vision technology based on binocular vision holds tremendous potential for development in various fields, including 3D scene reconstruction, target detection, and autonomous driving. However, current binocular vision methods used in robotics engineering have limitations such as high costs, complex algorithms, and low reliability of the generated disparity map in different scenes. To overcome these challenges, a cross-domain stereo matching algorithm for binocular vision based on transfer learning was proposed in this paper, named Cross-Domain Adaptation and Transfer Learning Network (Ct-Net), which has shown valuable results in multiple robot scenes. First, this paper introduces a General Feature Extractor to extract rich general feature information for domain adaptive stereo matching tasks. Then, a feature adapter is used to adapt the general features to the stereo matching network. Furthermore, a Domain Adaptive Cost Optimization Module is designed to optimize the matching cost. A disparity score prediction module was also embedded to adaptively adjust the search range of disparity and optimize the cost distribution. The overall framework was trained using a phased strategy, and ablation experiments were conducted to verify the effectiveness of the training strategy. Compared with the prototype PSMNet, on KITTI 2015 benchmark, the 3PE-fg of Ct-Net in all regions and non-occluded regions decreased by 19.3 and 21.1% respectively, meanwhile, on the Middlebury dataset, the proposed algorithm improves the sample error rate at least 28.4%, which is the Staircase sample. The quantitative and qualitative results obtained from Middlebury, Apollo, and other datasets demonstrate that Ct-Net significantly improves the cross-domain performance of stereo matching. Stereo matching experiments in real-world scenes have shown that it can effectively address visual tasks in multiple scenes. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of autonomous robot sorting steel ingots, this paper proposes a two‐dimensional weighted equivalent clustering‐based progressive probabilistic hough transform (2D‐WEC‐PPHT) algorithm for the problem of identifying and locating strip ingots in automatic picking strip ingot palletizing. First, the steel ingot image is preprocessed and the PPHT linear detection method is used to extract the edge linear information of the steel ingot. Second, the angle and position information are normalized and weighted, and a two‐dimensional clustering distance calculation method is proposed for two‐dimensional clustering of the extracted line information. Besides, the clustering line clusters with close angles were processed into equivalent lines by mean fitting. Then, the average threshold method was used to segment the ingot between each two fitting lines, and the minimum rectangle was used to box the segmentation part. The centroid of the box‐selected rectangle is the positioning center of the ingot, and the long side angle of the box‐selected rectangle is the deviation angle information of the ingot. Finally, the experimental results show that the number of redundant lines detected by the 2D‐WEC‐PPHT is significantly less than that of traditional methods such as HT and PPHT. The positioning speed of ingot is faster than that of HT and PPHT, and the processing time is reduced from 20 s to 10 s. In the case of large proportion of old ingots with weak reflection, the recognition accuracy reached 93.5%, and the angular and position positioning accuracy were 2.270° and 11.675 mm, respectively. The recognition accuracy of new ingots with strong reflection reached 99.574%, which met the requirements of picking positioning accuracy. In order to improve the efficiency of autonomous robot sorting steel ingots, this paper proposes a two‐dimensional weighted equivalent clustering‐based progressive probabilistic hough transform (2D‐WEC‐PPHT) algorithm for the problem of identifying and locating strip ingots in automatic picking strip ingot palletizing. [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]

Recent applications of autonomous agents and robots have brought attention to crucial trust-related challenges associated with the current generation of artificial intelligence (AI) systems. AI systems based on the connectionist deep learning neural network approach lack capabilities of explaining their decisions and actions to others, despite their great successes. Without symbolic interpretation capabilities, they are ‘black boxes’, which renders their choices or actions opaque, making it difficult to trust them in safety-critical applications. The recent stance on the explainability of AI systems has witnessed several approaches to eXplainable Artificial Intelligence (XAI); however, most of the studies have focused on data-driven XAI systems applied in computational sciences. Studies addressing the increasingly pervasive goal-driven agents and robots are sparse at this point in time. This paper reviews approaches on explainable goal-driven intelligent agents and robots, focusing on techniques for explaining and communicating agents’ perceptual functions (e.g., senses, vision) and cognitive reasoning (e.g., beliefs, desires, intentions, plans, and goals) with humans in the loop. The review highlights key strategies that emphasize transparency, understandability, and continual learning for explainability. Finally, the paper presents requirements for explainability and suggests a road map for the possible realization of effective goal-driven explainable agents and robots. [ABSTRACT FROM AUTHOR]

The paper describes both the method and the preliminary outcomes of the "Blind-assistive autonomous Droid Device: BUDD-e" research experience, funded by Polisocial Award 2021 of Politecnico di Milano, and designed to exploit a multidisciplinary approach to bridge the gap of inclusive design, making spaces and services accessible, functional, and usable by Blind and Visually Impaired (BVI) people. The research purpose is to develop accessible public places and indoor environments where the autonomous robot BUDD-e will act as guide for the BVI people. User-centred design principles will be applied to support accessibility to - both outdoor and indoor - public places, where the robot guide BUDD-e will be a primary feature in allowing access to services. [ABSTRACT FROM AUTHOR]

Self-vibrating systems based on active materials have been widely developed, but most of the existing self-oscillating systems are complex and difficult to control. To fulfill the requirements of different functions and applications, it is necessary to construct more self-vibrating systems that are easy to control, simple in material preparation and fast in response. This paper proposes a liquid crystal elastomer (LCE) string–mass structure capable of continuous vibration under steady illumination. Based on the linear elastic model and the dynamic LCE model, the dynamic governing equations of the LCE string–mass system are established. Through numerical calculation, two regimes of the LCE string–mass system, namely the static regime and the self-vibration regime, are obtained. In addition, the light intensity, contraction coefficient and elastic coefficient of the LCE can increase the amplitude and frequency of the self-vibration, while the damping coefficient suppresses the self-oscillation. The LCE string–-mass system proposed in this paper has the advantages of simple structure, easy control and customizable size, which has a wide application prospect in the fields of energy harvesting, autonomous robots, bionic instruments and medical equipment. [ABSTRACT FROM AUTHOR]

A new type of self-oscillating system has been developed with the potential to expand its applications in fields such as biomedical engineering, advanced robotics, rescue operations, and military industries. This system is capable of sustaining its own motion by absorbing energy from the stable external environment without the need for an additional controller. The existing self-sustained oscillatory systems are relatively complex in structure and difficult to fabricate and control, thus limited in their implementation in practical and complex scenarios. In this paper, we creatively propose a novel light-powered liquid crystal elastomer (LCE) fiber-cantilever system that can perform self-sustained oscillation under steady illumination. Considering the well-established LCE dynamic model, beam theory, and deflection formula, the control equations for the self-oscillating system are derived to theoretically study the dynamics of self-vibration. The LCE fiber-cantilever system under steady illumination is found to exhibit two motion regimes, namely, the static and self-vibration regimes. The positive work done by the tension of the light-powered LCE fiber provides some compensation against the structural resistance from cantilever and the air damping. In addition, the influences of system parameters on self-vibration amplitude and frequency are also studied. The newly constructed light-powered LCE fiber-cantilever system in this paper has a simple structure, easy assembly/disassembly, easy preparation, and strong expandability as a one-dimensional fiber-based system. It is expected to meet the application requirements of practical complex scenarios and has important application value in fields such as autonomous robots, energy harvesters, autonomous separators, sensors, mechanical logic devices, and biomimetic design. [ABSTRACT FROM AUTHOR]

Robot vision technology based on binocular vision holds tremendous potential for development in various fields, including 3D scene reconstruction, target detection, and autonomous driving. However, current binocular vision methods used in robotics engineering have limitations such as high costs, complex algorithms, and low reliability of the generated disparity map in different scenes. To overcome these challenges, a cross-domain stereo matching algorithm for binocular vision based on transfer learning was proposed in this paper, named Cross-Domain Adaptation and Transfer Learning Network (Ct-Net), which has shown valuable results in multiple robot scenes. First, this paper introduces a General Feature Extractor to extract rich general feature information for domain adaptive stereo matching tasks. Then, a feature adapter is used to adapt the general features to the stereo matching network. Furthermore, a Domain Adaptive Cost Optimization Module is designed to optimize the matching cost. A disparity score prediction module was also embedded to adaptively adjust the search range of disparity and optimize the cost distribution. The overall framework was trained using a phased strategy, and ablation experiments were conducted to verify the effectiveness of the training strategy. Compared with the prototype PSMNet, on KITTI 2015 benchmark, the 3PE-fg of Ct-Net in all regions and non-occluded regions decreased by 19.3 and 21.1% respectively, meanwhile, on the Middlebury dataset, the proposed algorithm improves the sample error rate at least 28.4%, which is the Staircase sample. The quantitative and qualitative results obtained from Middlebury, Apollo, and other datasets demonstrate that Ct-Net significantly improves the cross-domain performance of stereo matching. Stereo matching experiments in real-world scenes have shown that it can effectively address visual tasks in multiple scenes. [ABSTRACT FROM AUTHOR]

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]