Search Results (49)

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

The rapid growth of robotic warehouses and smart logistics has increased the demand for efficient scheduling of multi-tier shuttle systems (MTSSs). MTSS scheduling is a complex robotic task allocation problem, where throughput, energy efficiency, and service quality must be jointly optimized under operational constraints. To address this challenge, this study proposes a hybrid optimization framework that integrates the Minimum-Cost Maximum-Flow (MCMF) algorithm with the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The MTSS is modeled as a cyber–physical robotic system that explicitly incorporates task flow, energy flow, and information flow. The lower-layer MCMF ensures efficient and feasible task–robot assignments under state-of-charge (SOC) and deadline constraints, while the upper-layer NSGA-II adaptively tunes cost-function weights to explore Pareto-optimal trade-offs among makespan, energy consumption, and waiting time. Simulation results show that the hybrid framework outperforms baseline heuristics and static optimization methods and reduces makespan by up to 5%, the energy consumption by 2.8%, and the SOC violations by over 90% while generating diverse Pareto fronts that enable flexible throughput-oriented, service-oriented, or energy-conservative scheduling strategies. The proposed framework thus provides a practical and scalable solution for energy-aware robotic scheduling in automated warehouses, thus bridging the gap between exact assignment methods and adaptive multi-objective optimization approaches. Full article

With the rapid advancement of artificial intelligence, the deployment of social robots has significantly broadened, extending into diverse fields such as education, medical services, and business. Despite this expansive growth, there remains a notable scarcity of empirical research addressing the underlying psychological mechanisms that influence human–robot interactions. To address this critical research gap, the present study proposes and empirically tests a theoretical model designed to elucidate how users’ multi-dimensional perceived values of social robots influence their attitudinal responses and outcomes. Based on questionnaire data from 569 social robot users, the study reveals that users’ perceived utilitarian value, emotional value, and hedonic value all exert significant positive effects on active involvement, thereby fostering their identification and reinforcing attitudinal loyalty. Among these dimensions, emotional value emerged as the strongest predictor, underscoring the pivotal role of emotional orientation in cultivating lasting human–robot relationships. Furthermore, the findings highlight the critical mediating function of active involvement in linking perceived value to users’ psychological sense of belonging, thereby elucidating the mechanism through which perceived value enhances engagement and promotes sustained long-term interaction. These findings extend the conceptual boundaries of human–machine interaction, offer a theoretical foundation for future explorations of user psychological mechanisms, and inform strategic design approaches centered on emotional interaction and user-oriented experiences, providing practical guidance for optimizing social robot design in applications. Full article

A decentralized artificial intelligence (DAI) system is a human-oriented artificial intelligence (AI) system, which performs self-learning and shares its knowledge with other DAI systems like humans. A DAI device is an individual device (e.g., a mobile phone, a personal computer, a robot, a car, etc.) running a DAI system. A DAI device acquires validated knowledge data and raw data from a blockchain system as a trust anchor and improves its knowledge level by self-learning using the validated data. A DAI device using the proposed system reduces unreliable tasks, including the generation of unreliable products (e.g., deepfakes, fake news, and hallucinations), but the proposed system also prevents these malicious DAI devices from acquiring the validated data. This paper proposes a new architecture for a blockchain-based data management system for DAI devices, together with the service scenario and data flow, security threats, and security requirements. It also describes the key features and expected effects of the proposed system. This paper discusses the considerations for developing or operating the proposed system and concludes with future works. Full article

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

In modern manufacturing, achieving high-speed laser cutting requires advanced robotic trajectory planning for smoothness and collision avoidance. Poorly optimized motion can cause frequent velocity changes, leading to mechanical vibrations that shorten machine service life. This study presents an innovative trajectory optimization approach for laser cutting machines equipped with a redundant standoff axis. A B-spline-based analytical model formulates rotational axes trajectories as quadratic programming problems to minimize jerk (the rate of acceleration change) under machining accuracy and kinematic constraints. Additionally, an M path, represented by the wrist center’s trajectory, refines translational axes by adjusting the standoff axis through a similar optimization model, thereby reducing mechanical stress. Collision avoidance is ensured through a concurrent iterative optimization process, considering the feasible domains of representative 3D geometric tool orientations. Simulation experiments on a complex B-pillar workpiece demonstrate the framework’s effectiveness, clearly indicating significant reductions in jerk and improved trajectory smoothness for both rotational and translational axes compared with conventional methods and a prior approach. This work advances high-speed machining capabilities by offering a novel, robust solution that leverages redundant structures to further improve trajectory smoothness and reliability in demanding industrial applications. Full article

The ongoing adoption of Robot Operating Systems (ROSs) not only for research-oriented projects but also for industrial applications demands a more thorough assessment of its security than in the past. This paper highlights that a key ROS component—the ROS Master—is indeed vulnerable to a novel kind of Slow Denial of Service (slow DoS) attack, the root reason of this vulnerability being an extremely high idle connection timeout. The effects of vulnerability exploitation have been evaluated in detail by means of a realistic test bed, showing how it leads to a systemwide and potentially dangerous disruption of ROS system operations. Moreover, it has been shown how some basic forms of built-in protection of the Linux kernel can be easily circumvented, and are therefore ineffective against this kind of threat. Full article

Navigation skills are essential for most social and service robotics applications. The robots that are currently in practical use in various complex human environments are generally very limited in their autonomous navigational abilities; while they can reach the proximity of objects, they are not efficient in approaching them closely. The new solution described in this paper presents a system to solve this context-specific navigation problem. The system handles locations with differing contexts based on the use of LiDAR and URF sensors, allowing for the avoidance of people and obstacles with a wide margin, as well as for approaching target objects closely. To quantify the efficiency of our solution we compared it with the ROS contextless standard navigation (move_base) in two different robot platforms and environments, both with real-world tests and simulations. The metrics selected were (1) the time the robot needs to reach an object, (2) the Euclidean distance, and (3) the orientation between the final position of the robot and the defined goal position. We show that our context-specific solution is superior to the standard navigation both in time and Euclidean distance. Full article

Service robots that coexist with humans in everyday life have become more common, and they have provided customer service in physical shops around the world in recent years. However, their potential in effective sales strategies has not been fully realized due to their low social presence. This study aims to clarify what kind of robot behavior enhances the social presence of service robots and how it affects human–robot interaction and purchasing behavior. We conducted two experiments with a sales robot, Pepper, at a retail shop in Kyoto. In Experiment 1, we showed that the robot’s social presence increased and that customers looked at the robot longer when the robot understood human gaze information and was capable of shared attention. In Experiment 2, we showed that the probability of customers picking up products increased when the robot suggested products based on the humans’ degree of attention from gaze and posture information. These results indicate that the robot’s ability to understand and make utterances about a customer’s orientation and attention effectively enhances human–robot communication and purchasing motivation. Full article

Analysis of the energy consumption of industrial robots during the implementation of a technological task can increase efficiency and minimize production costs, as well as extend the service life of robots, taking into account the rational use of energy sources. An important challenge in designing sustainable production processes is the selection of energy-saving technological equipment supporting the work of robots. Additionally, optimization of robot program codes, including parameters and robot movement paths, determines the energy efficiency of the robotic station. Welding is one of the most popular areas of process robotization due to its ability to increase production efficiency and improve the quality of part assembly compared to manual work. In these studies, an exemplary process of robotic welding of aluminum alloy parts was designed, analyzed, and optimized with an orientation towards sustainable development guidelines. This work also presents a review of current publications discussing the authors’ contribution to achieving energy efficiency in robotic welding processes. The purpose of this analysis is also to indicate directions for the development of future research work in order to minimize energy consumption in production processes with an appropriate level of product quality and increase production efficiency. Full article

The use of smart indoor robotics services is gradually increasing in real-time scenarios. This paper presents a versatile approach to multi-robot backing crash prevention in indoor environments, using hardware schemes to achieve greater competence. Here, sensor fusion was initially used to analyze the state of multi-robots and their orientation within a static or dynamic scenario. The proposed novel hardware scheme-based framework integrates both static and dynamic scenarios for the execution of backing crash prevention. A round-robin (RR) scheduling algorithm was composed for the static scenario. Dynamic backing crash prevention was deployed by embedding a first come, first served (FCFS) scheduling algorithm. The behavioral control mechanism of the distributed multi-robots was integrated with FCFS and adaptive cruise control (ACC) scheduling algorithms. The integration of multiple algorithms is a challenging task for smarter indoor robotics, and the Xilinx-based partial reconfiguration method was deployed to avoid computational issues with multiple algorithms during the run-time. These methods were coded with Verilog HDL and validated using an FPGA (Zynq)-based multi-robot system. Full article

Service robots perform versatile functions in indoor environments. This study focuses on obstacle avoidance using flock-type indoor-based multi-robots. Each robot was developed with rendezvous behavior and distributed intelligence to perform obstacle avoidance. The hardware scheme-based obstacle-avoidance algorithm was developed using a bio-inspired flock approach, which was developed with three stages. Initially, the algorithm estimates polygonal obstacles and their orientations. The second stage involves performing avoidance at different orientations of obstacles using a heuristic based Bug2 algorithm. The final stage involves performing a flock rendezvous with distributed approaches and linear movements using a behavioral control mechanism. VLSI architectures were developed for multi-robot obstacle avoidance algorithms and were coded using Verilog HDL. The novel design of this article integrates the multi-robot’s obstacle approaches with behavioral control and hardware scheme-based partial reconfiguration (PR) flow. The experiments were validated using FPGA-based multi-robots. Full article

Collaborative robots have experienced low acceptance in applications, especially in industry. This fact has attracted the attention of researchers and practitioners, who point to different causes for this limited acceptance. One of the main reasons is the difficulty in converging on suitable methods for modeling collaborative interactions between robots and their surrounding context during the requirements phase. These interactions must be elicited and modeled during the requirements stage to maximize value creation through collaboration. Formal verification is necessary, taking into account the risks of human-robot interaction. However, such modeling is often absent in collaborative robot design, and choosing an appropriate approach remains an open problem. This paper addresses this problem using a model-based requirements cycle where the value creation is detached to provide direct analysis, possible optimization, and formal verification. The general process integrates with the general model-based requirements engineering of the remaining system. This service system approach relies on a goal-oriented requirements approach, and specific algorithms were developed to transfer goal-oriented diagrams into Petri Nets—to provide formal process verification. A case study illustrates the application of the proposed method on a collaborative robot used in a university hospital environment. Full article

Information and Communication Technologies are driving the improvement of industrial processes. According to the Industry 4.0 (I4.0) paradigm, digital systems provide real-time information to humans and machines, increasing flexibility and efficiency in production environments. Based on the I4.0 Design Principles concept, Virtual Assistants can play a vital role in processing production data and offer contextualized and real-time information to the workers in the production environment. This systematic review paper explored Virtual Assistant applications in the context of I4.0, discussing the Technical Assistance Design Principle and identifying the characteristics, services, and limitations regarding Virtual Assistant use in the production environments. The results showed that Virtual Assistants offer Physical and Virtual Assistance. Virtual Assistance provides real-time contextualized information mainly for support, while Physical Assistance is oriented toward task execution. Regarding services, the applications include integration with legacy systems and static information treatment. The limitations of the applications incorporate concerns about information security and adapting to noisy and unstable environments. It is possible to assume that the terminology of Virtual Assistants is not standardized and is mentioned as chatbots, robots, and others. Besides the worthy insights of this research, the small number of resulting papers did not allow for generalizations. Future research should focus on broadening the search scope to provide more-significant conclusions and research possibilities with new AI models and services, including the emergent Industry 5.0 concept. Full article

This paper presents the concept of a parallel medical robotic service system to assist in a surgical procedure involving precise exploratory trepanation holes in a patient’s skull. The target position and orientation of the trepanation tool in the cranial region is determined using a prior intracranial image analysis using an external medical imaging system. A trepanning actuation system is attached to the end-effector of the parallel robot. The end-effector will act as an accurate positioner for the trepanning drill in the medical intervention area. The conceptual design of the mechanical actuation subsystem of a trepanning robot was developed in the SolidWorks 2022 software environment. The virtual model of the kinematic chain of the robot and the assumed design parameters were used to analytically derive the equations describing the inverse kinematics task. An analysis of the forward kinematics task of the parallel manipulator was also carried out using analytical and numerical methods. A workspace analysis was performed using Matlab based on the kinematic model of the parallel robot. This paper significantly advances the field by presenting the conceptual design of the actuation subsystem, deriving the kinematics equations, conducting a thorough workspace analysis, and establishing a foundation for subsequent control-algorithm development. Full article