Search Results (136)

This paper presents a novel approach to determine dynamic safety and comfort zones in human–robot interactions (HRIs), with a focus on service robots operating in dynamic environments with people. The proposed proxemic model leverages the golden ratio-based comfort zone distribution and ISO safety standards to define adaptive proxemic boundaries for robots around humans. Unlike traditional fixed-threshold approaches, this novel method proposes a gradual and context-sensitive modulation of robot behaviour based on human position, orientation, and relative velocity. The system was implemented on an NVIDIA Jetson Xavier NX platform using a ZED 2i stereo depth camera Stereolabs, New York, USA and tested on two mobile robotic platforms: Go1 Unitree, Hangzhou, China (quadruped) and Scout Mini Agilex, Dongguan, China (wheeled). The initial verification of proposed proxemic model through experimental comfort validation was conducted using two simple interaction scenarios, and subjective feedback was collected from participants using a modified Godspeed Questionnaire Series. The results show that the participants felt comfortable during the experiments with robots. This acceptance of the proposed methodology plays an initial role in supporting further research of the methodology. The proposed solution also facilitates integration into existing navigation frameworks and opens pathways towards socially aware robotic systems. Full article

Decontamination robots are becoming more common in environments where reducing human exposure to hazardous substances is essential, including healthcare settings, laboratories, and industrial cleanrooms. Designing terrain-capable decontamination robots quickly is challenging due to varying operational surfaces and mobility limitations. To tackle this issue, a structured recommendation framework is proposed to automate selecting optimal locomotion types and track configurations, significantly cutting down design time. The proposed system features a two-stage evaluation process: first, it creates an annotated compatibility score matrix by validating locomotion types against a robust dataset based on factors like friction coefficient, roughness, payload capacity, and slope gradient; second, it employs a weighted scoring model to rank wheel/track types based on their appropriateness for the identified environmental conditions. User needs are processed dynamically using a large language model, enabling flexible and scalable management of various deployment scenarios. A prototype decontamination robot was developed following the proposed algorithm’s guidance. This framework speeds up the configuration process and establishes a foundation for more intelligent, terrain-aware robot design workflows that can be applied to industrial, healthcare, and service robotics sectors. Full article

With the widespread adoption of the Robot Operating System (ROS), technologies for autonomous navigation in mobile robots have advanced considerably. ROS provides a modular navigation stack that integrates essential components, such as SLAM, localisation, global path planning, and obstacle avoidance, forming the foundation for applications including service robotics and autonomous driving. Nonetheless, achieving safe and reliable navigation in complex and dynamic environments remains a formidable challenge, due to the need for real-time perception of moving obstacles, sensor fusion requirements, and the demand for robust and efficient algorithms. This study presents a systematic examination of the ROS-based navigation stack and obstacle-avoidance mechanisms. The architecture and implementation principles of the core modules are analysed, along with a comparison of the features and application suitability of common local planners such as the Dynamic Window Approach (DWA) and Timed Elastic Band (TEB). The key technical challenges in autonomous navigation are summarised, and recent advancements are reviewed to outline emerging trends in ROS-based systems, including integration with deep learning, multi-robot coordination, and real-time optimisation. The findings contribute to a deeper theoretical understanding of robotic navigation and offer practical guidance for the design and development of autonomous systems. Full article

The edge computing paradigm has gained prominence in both academic and industry circles in recent years. When edge computing facilities and services are implemented in robotics, they become a key enabler in the deployment of artificial intelligence applications to robots. Time-sensitive robotics applications benefit from the reduced latency, mobility, and location awareness provided by the edge computing paradigm, which enables real-time data processing and intelligence at the network’s edge. While the advantages of integrating edge computing into robotics are numerous, there has been no recent survey that comprehensively examines these benefits. This paper aims to bridge that gap by highlighting important work in the domain of edge robotics, examining recent advancements, and offering deeper insight into the challenges and motivations behind both current and emerging solutions. In particular, this article provides a comprehensive evaluation of recent developments in edge robotics, with an emphasis on fundamental applications, providing in-depth analysis of the key motivations, challenges, and future directions in this rapidly evolving domain. It also explores the importance of edge computing in real-world robotics scenarios where rapid response times are critical. Finally, the paper outlines various open research challenges in the field of edge robotics. Full article

This paper presents a simulation approach for mobile robots designed to operate within networks intended for emergency response scenarios. The simulation component is part of a broader and more complex system architecture focused on enhancing communication efficiency and operational coordination within robotic networks. This study leverages virtualization and robotic simulation technologies to develop a controlled environment in which the behavior and coordination of mobile robots can be analyzed and validated under simulated emergency conditions. To achieve this, a virtual machine was configured to host a ROS2 and Gazebo-based simulation environment. Custom packages were developed to enable the dynamic instantiation of mobile robots and the integration of essential sensing and control functionalities. The simulation process was carried out in two stages: initially, a single mobile robot was deployed and evaluated; subsequently, the configuration was extended to support a second robot, enabling multi-agent interaction within the simulated environment using flat surfaces. The proposed architecture demonstrates the potential for scalable deployment and simulation of mobile robotic instances. As a future direction, the authors aim to extend the system by optimizing data extraction from the simulation environment and implementing ROS2 microservices to facilitate secure and efficient communication with a centralized server deployed within a Kubernetes cluster. This integration will enable real-time coordination and data exchange between simulated agents and backend services, forming the foundation for a robust, distributed robotic system tailored to emergency operations. Full article

This study proposes a D-optimality-based viewpoint selection method to improve visual assistance for a manipulator by optimizing camera placement. The approach maximizes the information gained from visual observations, reducing uncertainty in object recognition and localization. A mathematical framework utilizing D-optimality criteria is developed to determine the most informative camera viewpoint in real time. The proposed method is integrated into a robotic system where a mobile robot adjusts its viewpoint to support the manipulator in grasping and placing tasks. Experimental evaluations demonstrate that D-optimality-based viewpoint selection improves recognition accuracy and task efficiency. The results suggest that optimal viewpoint planning can enhance perception robustness, leading to better manipulation performance. Although tested in structured environments, the approach has the potential to be extended to dynamic or unstructured settings. This research contributes to the integration of viewpoint optimization in vision-based robotic cooperation, with promising applications in industrial automation, service robotics, and human–robot collaboration. Full article

Human action recognition (HAR) is essential for understanding and classifying human movements. It is widely used in real-life applications such as human–computer interaction and assistive robotics. However, recognizing patterns across different temporal scales remains challenging. Traditional methods struggle with complex timing patterns, intra-class variability, and inter-class similarities, leading to misclassifications. In this paper, we propose a deep learning framework for efficient and robust HAR. It integrates residual networks (ResNet-18) for spatial feature extraction and Bi-LSTM for temporal feature extraction. A multi-head attention mechanism enhances the prioritization of crucial motion details. Additionally, we introduce a motion-based frame selection strategy utilizing optical flow to reduce redundancy and enhance efficiency. This ensures accurate, real-time recognition of both simple and complex actions. We evaluate the framework on the UCF-101 dataset, achieving a 96.60% accuracy, demonstrating competitive performance against state-of-the-art approaches. Moreover, the framework operates at 222 frames per second (FPS), achieving an optimal balance between recognition performance and computational efficiency. The proposed framework was also deployed and tested on a mobile service robot, TIAGo, validating its real-time applicability in real-world scenarios. It effectively models human actions while minimizing frame dependency, making it well-suited for real-time applications. Full article

In recent years, advancements in artificial intelligence (AI) have become an essential study for machine learning. The use of AI with the Robot Operating System (ROS) enables mobile robots to learn and move autonomously. Mobile robots can now be widely used in industrial and service sectors. Generally, robots have been operated on fixed paths requiring set points to function. This study utilizes Deep Q-Network (DQN) incorporating filtering to train and reward AprilTag images, paths, and obstacle avoidance. Training is conducted in a Gazebo simulation environment, and the collected data is verified on physical mobile robots. The DQN network excels in computing complex functions; AprilTag provides X, Y, Z, Pitch, Yaw, and Roll data. By employing DQN methods, recognition and path accuracy are simultaneously enhanced. The constructed DQN network can endow mobile robots with autonomous learning capabilities. Full article

Navigation services and high-precision positioning play a significant role in emerging fields such as self-driving and mobile robots. The performance of precise point positioning (PPP) may be seriously affected by signal interference and struggles to achieve continuous and accurate positioning in complex environments. LiDAR/inertial navigation can use spatial structure information to realize pose estimation but cannot solve the problem of cumulative error. This study proposes a PPP/inertial/LiDAR combined localization algorithm based on factor graph optimization. Firstly, the algorithm performed the spatial alignment by adding the initial yaw factor. Then, the PPP factor and anchor factor were constructed using PPP information. Finally, the global localization is estimated accurately and robustly based on the factor graph. The vehicle experiment shows that the proposed algorithm in this study can achieve meter-level accuracy in complex environments and can greatly enhance the accuracy, continuity, and reliability of attitude estimation. Full article

Spinopelvic dissociation is a highly unstable orthopedic injury with a growing incidence worldwide. Operative treatment classically involves an open lumbopelvic fusion and sacroiliac stabilization, which carries high perioperative morbidity and mortality in a frail patient population. Advancements in spinal navigation, robotics, and minimally invasive surgery (MIS) techniques now allow these fracture patterns to be treated entirely percutaneously through small incisions. These incisions are just large enough to accommodate pedicle screw guides and enable the placement of lumbopelvic instrumentation, with rods being passed subfascially across pedicle screws and extending caudally to iliac fixation. This contrasts with the open midline approach, which requires more extensive soft tissue dissection and results in increased blood loss compared to percutaneous techniques. Modern imaging techniques, including CT navigation and robotics, facilitate the precise placement of sacral S2AI screw instrumentation in both open and percutaneous methods, all while safely avoiding previously placed trans-sacral fixation and other existing hardware, such as acetabular screws. Trans-sacral screws are typically percutaneously inserted first by the orthopedic trauma service, utilizing inlet, outlet, and lateral sacral fluoroscopic guidance to navigate the limited available corridor. With the advent of MIS techniques, trauma patients can now benefit from faster postoperative rehabilitation, minimal blood loss, decreased pain, and quicker mobilization. This article will review current concepts on spinopelvic anatomy, fracture patterns, indications for treatment, and current concepts for minimally invasive percutaneous lumbopelvic fixation, and it will present illustrative examples. Full article

Background: With urban logistics facing challenges such as high delivery volumes and driver shortages, autonomous driving emerges as a promising solution. However, the integration of autonomous vans and robots into existing fulfillment processes and platforms remains largely unexplored. Method: This paper addresses this gap by developing and piloting a comprehensive blueprint architecture tailored for autonomous mobility in urban last-mile delivery. The proposed framework integrates autonomous vehicle operations, data processing, and stakeholder collaboration. Results: Through initial implementation and piloting, we demonstrate the practical applicability and advantages of this architecture. Conclusions: This study contributes to the understanding of essential data, services, and tools, providing a valuable guideline for Logistics Service Providers aiming to implement autonomous last-mile delivery solutions. Full article

Objectives: To summarize the impact of various telerehabilitation interventions on motor function, balance, gait, activities of daily living (ADLs), and quality of life (QoL) among patients with stroke and to determine the existing telerehabilitation interventions for delivering physiotherapy sessions in clinical practice. Methods: Six electronic databases were searched to identify relevant quantitative systematic reviews (SRs). Due to substantial heterogeneity, the data were analysed narratively. Results: A total of 28 systematic reviews (n = 245 primary studies) were included that examined various telerehabilitation interventions after stroke. Motor function was the most studied outcome domain across the reviews (20 SRs), followed by ADL (18 SRs), and balance (14 SRs) domains. For primary outcomes, our findings highlight moderate- to high-quality evidence showing either a significant effect or no significant difference between telerehabilitation and other interventions. There was insufficient evidence to draw a conclusion regarding feasibility outcomes, including participant satisfaction, adherence to treatment, and cost. Most reviews under this umbrella included patients with stroke in the subacute or chronic phase (12 SRs). Simple and complex telerehabilitation interventions such as telephone calls, videoconferencing, smartphone- or tablet-based mobile health applications, messaging, virtual reality, robot-assisted devices, and 3D animation videos, either alone or in combination with other interventions, were included across reviews. Conclusions: Various telerehabilitation interventions have shown either a significant effect or no significant difference compared to other interventions in improving upper and lower limb motor function, balance, gait, ADLs, and QoL, regardless of whether simple or complex approaches were used. Further research is needed to support the delivery of rehabilitation services through telerehabilitation intervention following a stroke. Full article

The representation and utilization of environmental information by service robots has become increasingly challenging. In order to solve the problems that the service robot platform has, such as high timeliness requirements for indoor environment recognition tasks and the small scale of indoor scene data, a method and model for rapid classification of household environment domain knowledge is proposed, which can achieve high recognition accuracy by using a small-scale indoor scene and tool dataset. This paper uses a knowledge graph to associate data for home service robots. The application requirements of knowledge graphs for home service robots are analyzed to establish a rule base for the system. A domain ontology of the home environment is constructed for use in the knowledge graph system, and the interior functional areas and functional tools are classified. This designed knowledge graph contributes to the state of the art by improving the accuracy and efficiency of service decision making. The lightweight network MobileNetV3 is used to pre-train the model, and a lightweight convolution method with good feature extraction performance is selected. This proposal adopts a combination of MobileNetV3 and transfer learning, integrating large-scale pre-training with fine-tuning for the home environment to address the challenge of limited data for home robots. The results show that the proposed model achieves higher recognition accuracy and recognition speed than other common methods, meeting the work requirements of service robots. With the Scene15 dataset, the proposed scheme has the highest recognition accuracy of 0.8815 and the fastest recognition speed of 63.11 microseconds per sheet. Full article

Mobile robot navigation is a critical aspect of robotics, with applications spanning from service robots to industrial automation. However, navigating in complex and dynamic environments poses many challenges, such as avoiding obstacles, making decisions in real-time, and adapting to new situations. Reinforcement Learning (RL) has emerged as a promising approach to enable robots to learn navigation policies from their interactions with the environment. However, application of RL methods to real-world tasks such as mobile robot navigation, and evaluating their performance under various training–testing settings has not been sufficiently researched. In this paper, we have designed an evaluation framework that investigates the RL algorithm’s generalization capability in regard to unseen scenarios in terms of learning convergence and success rates by transferring learned policies in simulation to physical environments. To achieve this, we designed a simulated environment in Gazebo for training the robot over a high number of episodes. The training environment closely mimics the typical indoor scenarios that a mobile robot can encounter, replicating real-world challenges. For evaluation, we designed physical environments with and without unforeseen indoor scenarios. This evaluation framework outputs statistical metrics, which we then use to conduct an extensive study on a deep RL method, namely the proximal policy optimization (PPO). The results provide valuable insights into the strengths and limitations of the method for mobile robot navigation. Our experiments demonstrate that the trained model from simulations can be deployed to the previously unseen physical world with a success rate of over $88\%$. The insights gained from our study can assist practitioners and researchers in selecting suitable RL approaches and training–testing settings for their specific robotic navigation tasks. Full article

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