Search Results (122)

As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human–robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject. Full article

Intelligent mobile robots have been gradually used in various fields, including logistics, healthcare, service, and maintenance. Path planning is a crucial aspect of intelligent mobile robot research, which aims to empower robots to create optimal trajectories within complex and dynamic environments autonomously. This study introduces an improved A* algorithm to address the challenges faced by the preliminary A* pathfinding algorithm, which include limited efficiency, inadequate robustness, and excessive node traversal. Firstly, the node storage structure is optimized using a minimum heap to decrease node traversal time. In addition, the heuristic function is improved by adding an adaptive weight function and a turn penalty function. The original 8-neighbor is expanded to a 16-neighbor within the search strategy, followed by the elimination of invalid search neighbor to refine it into a new 8-neighbor according to the principle of symmetry, thereby enhancing the directionality of the A* algorithm and improving search efficiency. Furthermore, a bidirectional search mechanism is implemented to further reduce search time. Finally, trajectory optimization is performed on the planned paths using path node elimination and cubic Bezier curves, which aligns the optimized paths more closely with the kinematic constraints of the robot derivable trajectories. In simulation experiments on grid maps of different sizes, it was demonstrated that the proposed improved A* algorithm outperforms the preliminary A* Algorithm in various metrics, such as search efficiency, node traversal count, path length, and inflection points. The improved algorithm provides substantial value for practical applications by efficiently planning optimal paths in complex environments and ensuring robot drivability. Full article

Industrial development has implicitly led to the development of new systems that increase the ability to provide services and products in real time. Autonomous mobile robots are considered some of the most important tools that can help both industry and society. These robots offer a certain autonomy that makes them indispensable in industrial activities. However, some elements of these robots are not yet very well outlined, such as their construction, their lifetime and energy consumption, and the environmental impact of their activity. Within the context of European regulations (here, we focus on the Green Deal and the growth in greenhouse gas emissions), any industrial activity must be analyzed and optimized so that it is efficient and does not significantly impact the environment. The added value of this paper is its examination of the activities carried out by mobile robots and the impact of their electronic components on the environment. The proposed analysis employs, as a central point, an analysis of mobile robots from the point of view of their electronic components and the impact of their activity on the environment in terms of energy consumption, as evaluated by calculating the emission of greenhouse gases (GHGs). The way in which the activity of a robot impacts the environment was established throughout the economic flow, as well as by providing possible methods of reducing this impact by optimizing the robot’s activity. The environmental impact of a mobile robot, in regard to its electronic components, will also be analyzed when the period of operation is completed. Full article

Mobile service robots experience excessive vibrations when travelling over uneven surfaces in their workspace, increasing the degradation rate of the mechanical components or disrupting the robot’s sensing abilities for proper localization and navigational capabilities. Robot inclusivity principles can determine the suitability of a site for robot performance by considering the ground’s unevenness. This paper proposes a novel framework to autonomously evaluate the Robot Inclusivity Level of buildings based on surface unevenness (RIL-SU) by quantifying the surface unevenness of floor surfaces. The surface unevenness values are converted to RIL-SU using a rule-based approach, and the corresponding RIL-SU is tagged to the map location. A coloured heatmap based on the RIL-SU values is created as a visual representation of the RIL-SU of a given space. This heatmap would be useful for modifying the environment to make it more robot-friendly or restrict the robot’s operation in certain areas to avoid possible robot failures. The experimental results show that the proposed framework can successfully generate a valid RIL-SU heatmap for building environments. Full article

This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models’ ability to generalize. However, the model’s performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incorporates efficient multi-scale attention (EMA) into the traditional YOLOv5-Lite model and replaces the C3 module with a lightweight C3Ghost module to reduce computation and model size during the convolution process. To enhance bounding box localization accuracy, modified precision-defined intersection over union (MPDIoU) is employed to optimize the model, resulting in the ROS–YOLOv5–FleetEMA model. The results indicated that relative to the conventional YOLOv5-Lite model, the ROS–YOLOv5–FleetEMA model enhanced the mean average precision (mAP) by 2.7% post-training, reduced giga floating-point operations per second (GFLOPS) by 13.2%, and decreased the params by 15.1%. In light of these experimental findings, the model was incorporated into ROS, leading to the development of a ROS-based object detection platform that offers rapid and precise object detection capabilities. Full article

New applications such as augmented reality/virtual reality (AR/VR), Internet-of-Things (IOT), autonomous mobile robot (AMR) services, etc., require high reliability and high accuracy real-time positioning and tracking of persons and devices in indoor areas. Among the different visible-light-positioning (VLP) schemes, such as proximity, time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), and received-signal-strength (RSS), the RSS scheme is relatively easy to implement. Among these VLP methods, the RSS method is simple and efficient. As the received optical power has an inverse relationship with the distance between the LED transmitter (Tx) and the photodiode (PD) receiver (Rx), position information can be estimated by studying the received optical power from different Txs. In this work, we propose and experimentally demonstrate a real-time VLP system utilizing long short-term memory neural network (LSTM-NN) with principal component analysis (PCA) to mitigate high positioning error, particularly at the positioning unit cell boundaries. Experimental results show that in a positioning unit cell of 100 × 100 × 250 cm³, the average positioning error is 5.912 cm when using LSTM-NN only. By utilizing the PCA, we can observe that the positioning accuracy can be significantly enhanced to 1.806 cm, particularly at the unit cell boundaries and cell corners, showing a positioning error reduction of 69.45%. In the cumulative distribution function (CDF) measurements, when using only the LSTM-NN model, the positioning error of 95% of the experimental data is >15 cm; while using the LSTM-NN with PCA model, the error is reduced to <5 cm. In addition, we also experimentally demonstrate that the proposed real-time VLP system can also be used to predict the direction and the trajectory of the moving Rx. Full article

A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards. Full article

In recent years, with the rapidly aging population, alleviating the pressure on medical staff has become a critical issue. To improve the work efficiency of medical staff and reduce the risk of infection, we consider the multi-trip autonomous mobile robot (AMR) routing problem in a stochastic environment. Our goal is to minimize the total expected operating cost and maximize the total service quality for patients, ensuring that each route violates the vehicle capacity and the time window with only a minimal probability. The travel time of AMRs is stochastically affected by the surrounding environment; the demand for each ward is unknown until the AMR reaches the ward, and the service time is linearly related to the actual demand. We developed a population-based tabu search algorithm (PTS) that combines the genetic algorithm with the tabu search algorithm to solve this problem. Extensive numerical experiments were conducted on the modified Solomon instances to demonstrate the efficiency of the PTS algorithm and reveal the impacts of the confidence level on the optimal solution, providing insights for decision-makers to devise delivery schemes that balance operating costs with patient satisfaction. Full article

The OHIO (Odin Hospital Indoor cOmpass) project received funding from the European Union’s Horizon 2020 research and innovation action program, via ODIN–Open Call, which is issued and executed under the ODIN project and focuses on enhancing hospital safety, productivity, and quality by introducing digital solutions, such as the Internet of Things (IoT), robotics, and artificial intelligence (AI). OHIO aims to enhance the productivity and quality of medical equipment maintenance activities within the pilot hospital, “Le Scotte” in Siena (Italy), by leveraging internal informational resources. OHIO will also be completely integrated with the ODIN platform, taking advantage of the available services and functionalities. OHIO exploits Bluetooth Low Energy (BLE) tags and antennas together with the resources provided by the ODIN platform to develop a complex ontology-based IoT framework, which acts as a central cockpit for the maintenance of medical equipment through a central management web application and an indoor real-time location system (RTLS) for mobile devices. The application programmable interfaces (APIs) are based on REST architecture for seamless data exchange and integration with the hospital’s existing computer-aided facility management (CAFM) and computerized maintenance management system (CMMS) software. The outcomes of the project are assessed both with quantitative and qualitative methods, by evaluating key performance indicators (KPIs) extracted from the literature and performing a preliminary usability test on both the whole system and the graphic user interfaces (GUIs) of the developed applications. The test implementation demonstrates improvements in maintenance timings, including a reduction in maintenance operation delays, duration of maintenance tasks, and equipment downtime. Usability post-test questionnaires show positive feedback regarding the usability and effectiveness of the applications. The OHIO framework enhanced the effectiveness of medical equipment maintenance by integrating existing software with newly designed, enhanced interfaces. The research also indicates possibilities for scaling up the developed methods and applications to additional large-scale pilot hospitals within the ODIN network. Full article

In the global context, advancements in technology and science have rendered virtual, augmented, and mixed-reality technologies capable of transforming clinical care and medical environments by offering enhanced features and improved healthcare services. This paper aims to present a mixed reality-based system to control a robotic wheelchair for people with limited mobility. The test group comprised 11 healthy subjects (six male, five female, mean age 35.2 ± 11.7 years). A novel platform that integrates a smart wheelchair and an eye-tracking-enabled head-mounted display was proposed to reduce the cognitive requirements needed for wheelchair movement and control. The approach’s effectiveness was demonstrated by evaluating our system in realistic scenarios. The demonstration of the proposed AR head-mounted display user interface for controlling a smart wheelchair and the results provided in this paper could highlight the potential of the HoloLens 2-based innovative solutions and bring focus to emerging research topics, such as remote control, cognitive rehabilitation, the implementation of patient autonomy with severe disabilities, and telemedicine. Full article

Visible light technology presents significant advancement for indoor IoT applications. These systems offer enhanced bit rate transmission, enabling faster and reliable data transfer. Moreover, optical-based visible light systems facilitate improved location services within indoor environments. However, many of these systems still exhibit limited accuracy within several centimeters, even when relying on costly high-resolution cameras. This paper introduces a novel low-cost visible light system for 3D positioning, designed to enhance indoor positioning accuracy using low-resolution images. Initially, we propose a non-integer pixel (NI-P) algorithm to enhance precision without the need for higher-resolution images. This algorithm allows the system to identify the precise light spot coordinates on the low-resolution images, enabling accurate positioning. Subsequently, we present an algorithm leveraging the precise coordinate data from the previous step to determine the 3D position of objects even in front of errors in the measures. Benefiting from high accuracy, reduced cost, and low complexity, the proposed system is suitable for implementation on low-end hardware platforms, thereby increasing the versatility and feasibility of visible light technologies in indoor settings. Experimental results show an average 2D positioning error of 1.08 cm and 3D error within 1.4 cm at 2.3 m separation between the object and camera, achieved with an average positioning time of 20 ms on a low-end embedded device. Consequently, the proposed system offers fast and highly accurate indoor positioning and tracking capabilities, making it suitable for applications like mobile robots, automated guided vehicles, and indoor parking management. Furthermore, it is easy to deploy and does not require re-calibration. Full article

Mobile robots are autonomous devices capable of self-motion, and are utilized in applications ranging from surveillance and logistics to healthcare services and planetary exploration. Precise trajectory tracking is a crucial component in robotic applications. This study introduces the use of improved sliding surfaces and artificial neural networks in controlling mobile robots. An enhanced sliding surface, combined with exponential and hyperbolic tangent approach laws, is employed to mitigate chattering phenomena in sliding mode control. Nonlinear components of the sliding control law are estimated using artificial neural networks. The weights of the neural networks are updated online using a gradient descent algorithm. The stability of the system is demonstrated using Lyapunov theory. Simulation results in MATLAB/Simulink R2024a validate the effectiveness of the proposed method, with rise times of 0.071 s, an overshoot of 0.004%, and steady-state errors approaching zero meters. Settling times were 0.0978 s for the x-axis and 0.0902 s for the y-axis, and chattering exhibited low amplitude and frequency. Full article

Mobile service robots employ vision systems to discern objects in their workspaces for navigation or object detection. The lighting conditions of the surroundings affect a robot’s ability to discern and navigate in its work environment. Robot inclusivity principles can be used to determine the suitability of a site’s lighting condition for robot performance. This paper proposes a novel framework for autonomously auditing the Robot Inclusivity Index of indoor environments based on the lighting condition (RII-lux). The framework considers the factors of light intensity and the presence of glare to define the RII-Lux of a particular location in an environment. The auditing framework is implemented on a robot to autonomously generate a heatmap visually representing the variation in RII-Lux of an environment. The applicability of the proposed framework for generating true-to-life RII-Lux heatmaps has been validated through experimental results. Full article

As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to a new turning point, with expectations that numerous mobile robots will be driving on roads with traffic. To achieve these expectations, autonomous mobile robots should precisely perceive the situation on roads with traffic. In this paper, we revisit and implement a real-time traffic light recognition system with a proposed lightweight state recognition network and ratio-preserving zero padding, which is a two-stage system consisting of a traffic light detection (TLD) module and a traffic light status recognition (TLSR) module. For the TLSR module, this work proposes a lightweight state recognition network with a small number of weight parameters, because the TLD module needs more weight parameters to find the exact location of traffic lights. Then, the proposed effective and lightweight network architecture is constructed by using skip connection, multifeature maps with different sizes, and kernels of appropriately tuned sizes. Therefore, the network has a negligible impact on the overall processing time and minimal weight parameters while maintaining high performance. We also propose to utilize a ratio-preserving zero padding method for data preprocessing for the TLSR module to enhance recognition accuracy. For the TLD module, extensive evaluations with varying input sizes and backbone network types are conducted, and then appropriate values for those factors are determined, which strikes a balance between detection performance and processing time. Finally, we demonstrate that our traffic light recognition system, utilizing the TLD module’s determined parameters, the proposed network architecture for the TLSR module, and the ratio-preserving zero padding method can reliably detect the location and state of traffic lights in real-world videos recorded in Gumi and Deagu, Korea, while maintaining at least 30 frames per second for real-time operation. Full article

Currently, we exist in the 5G division of the wireless technology cycle, where the standardization is complete and deployment is being carried out. However, 5G networks do not have the capacity to deliver an automated and intelligent network that supports connected intelligence. 6G is what enables this, and globally, countries are aiming to lay the foundation for the communication needs of 2030. This brings out a very key question and discussion on how wireless communications will develop in the future, particularly adapting to the range and set of applications and user cases. Industry and academic efforts have started to explore beyond 5G and uncover 6G as 5G becomes more internationally accessible. We forecast that 6G will undergo a transition that is unheard of in the history of wireless cellular systems. 6G exists beyond mobile internet and will be required to support omnipresent AI services from the network’s core to its endpoints. Meanwhile, artificial intelligence (AI) will be crucial for developing and improving 6G designs, protocols, and operations. URLLC plays a crucial role in next-generation communication systems, particularly in 6G, for applications requiring ultra-low latency and reliability. These services support cutting-edge technologies like driverless vehicles, remote robotic surgery, smart factories, and augmented reality applications. URLLCs ensure robust connectivity and real-time responsiveness, enabling time-sensitive and safety-critical services in 6G communication infrastructures. This article illustrates the importance of URLLCs in 6G and their integration with deep learning, the security challenges, and their potential solutions. Further on, it establishes its relationship with key aspects of federated learning and security in the 6G domain. Full article