Context Of Robotics Research Articles

SincNet-Based Speaker Identification for Robotic Environments with Varying Human–Robot Interaction Distance

As human–robot interaction (HRI) becomes increasingly significant, various studies have focused on speaker recognition. However, few studies have explored this topic in the specific environment of home service robots. Notably, most existing research relies on databases composed of English-language data, while studies utilizing Korean speech data are exceedingly scarce. This gap underscores the need for research on speaker recognition in robotic environments, specifically using Korean data. In response, this paper conducts experiments using a speaker recognition database tailored to the Korean language and set in a robotic context. The database includes noise generated by robot movement as well as common environmental noise, accounting for variable distances between humans and robots, which are partitioned accordingly. The deep learning model employed is SincNet, with experiments conducted under two settings for the SincNet filter parameters: one with learnable parameters and the other with fixed values. After training the model with data collected at varying distances, performance was tested across these distances. Experimental results indicate that SincNet with learnable parameters achieved a peak accuracy of 99%.

Robotic Laser Tissue Soldering for Atraumatic Soft Tissue Fusion Guided by Fluorescent Nanothermometry.

Minimally invasive surgical techniques, including endoscopic and robotic procedures, continue to revolutionize patient care, for their ability to minimize surgical trauma, thus promoting faster recovery and reduced hospital stays. Yet, the suturing of soft tissues ensuring damage-free tissue bonding during these procedures remains challenging due to missing haptics and the fulcrum effect. Laser tissue soldering has potential in overcoming these issues, offering atraumatic seamless tissue fusion. To ensure the precision and safety of laser tissue soldering, the study introduces feedback-controlled fluorescent nanothermometry-guided laser tissue soldering using nanoparticle-protein solders within endoscopic and robotic contexts. Temperature-sensitive fluorescent nanoparticles embedded in the solder provide surgeons with immediate feedback on tissue temperatures during laser application, all while within the confines of minimally invasive (robotic) surgical setups. By integrating fluorescent nanothermometry-guided laser tissue surgery into endoscopic and robotic surgery, the study paves the way for a new approach for safe and atraumatic soft tissue joining, especially in regions where traditional suturing is unfeasible.

Integration of Artificial Vision and Image Processing into a Pick and Place Collaborative Robotic System

In the field of robotics, pick and place applications are becoming increasingly popular due to their ability to automate repetitive tasks that can create temporary or permanent injuries. To enhance the efficiency of these applications, object recognition using a fixed camera or one mounted on a robotic hand has been employed. This paper explores the possibilities of implementing a low-cost camera into a collaborative robotic system. A software architecture has been developed, including modules for perception, pick and place, and part transfer. A comprehensive overview of various intuitive drag-and-drop image processing technologies and their suitability for object recognition in a robotic context is provided. The challenges related to lighting and the effect of shadows in object recognition are discussed. A critical assessment is made of the architecture development platform as well as the study and the results are performed, and the effectiveness of the proposed solution based on the Niop architecture is verified.

Telepresence Robots in the Context of Dementia Caregiving: Caregivers’ and Care Recipients’ Perspectives

As a result of a rapidly aging population and the increasing prevalence of dementia among older adults, technological solutions are increasingly being considered to facilitate caregiving. This research investigates the perspectives of 20 caregiving dyads on VGo, a telepresence social robot with features designed to support caregiving. Care recipients (CRs), aged 65 and older, diagnosed with Alzheimer’s disease and related dementias, along with their primary caregivers (CGs), evaluated the robot through an online interview study. The interviews integrated informative videos showcasing VGo’s features and functions. Insights from the interviews revealed diverse expectations, interests, and reservations. The majority of CGs and their CRs perceived the robot’s features as beneficial. In particular, the voice command capability was appreciated as an alternative to using smartphones and as a way to manage home appliances. The community feature, however, did not align well with many participants’ lifestyles, and participants had a number of suggestions to enhance the robot’s notification function. Based on the interview results, the study offers a set of design recommendations for telepresence social robots in home caregiving contexts. This investigation highlights the promise of social robots in caregiving contexts and underscores the need for further improvements to ensure they fit users’ needs.

Understanding the Interaction between Delivery Robots and Other Road and Sidewalk Users: A Study of User-generated Online Videos

The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2,067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces.

Customer Participation in the Value Co-creation of Hotel Service Robot Interaction

Service robots are becoming increasingly popular in hotels, mainly providing services such as welcome, check-in, and meal delivery. This study examines the relationships among customer participation, perceived value, and behavioral intention in a hotel service robot context. We collected 321 valid samples and performed the Partial Least Squares (PLS) path analysis. The results show that customer participation is positively related to utilitarian and hedonic values, which further lead to service robot satisfaction. Service robot satisfaction is also related to hotel satisfaction, and both service robot satisfaction and hotel satisfaction have positive effects on behavioral intention. This study helps to deepen the understanding of the influence of customer participation in value co-creation on behavioral intention in human-robot interaction context.

Designing speculative nonhuman-robot scenarios: An artistic approach

In the rapidly evolving field of robotics, a new paradigm is emerging that goes beyond anthropocentric design and embraces the innate ingenuity of the natural world. This article seeks to unveil ecocentric and more-than-human approaches, along with innovative materials, in creating nonhuman robotic scenarios within an artistic framework. Building on the foundation laid by anthropomorphic concepts depicted in science fiction, this article seeks to show cutting-edge experiments and material applications that imagine potential more-than-human scenarios in a robotic context. By highlighting examples from robotics, soft robotics, plant neurobiology, and material research, the article aims to propose an alternative concept to anthropomorphism. It endeavors to offer an overview of cutting-edge scientific efforts, experimental designs, and material explorations, all of which converge toward the realisation of robotic scenarios inspired by the animal and natural kingdom.

Advancing human-robot collaboration: Predicting operator trajectories through AI and infrared imaging

In industrial applications demanding close interaction between humans and robots, accurate perception of the robot's surroundings is vital to adjust its behavior appropriately. This need becomes especially pertinent when manipulators operate autonomously and execute both separate and common tasks with human operators. To ensure operator safety in such Human-Robot Collaboration (HRC) environments, the present paper proposes an innovative approach that leverages advanced collaborative robotic systems capable of sensing the operator's position to effectively prevent or mitigate potential contact. Our solution combines infrared-thermal imaging technology with a workspace monitoring system, utilizing thermal and depth cameras, to track the operator's current location and predict their future trajectory using a multi-layer Long Short-Term Memory (LSTM) neural network. We also introduce a technique for simulating both human and robot movements to generate a synthetic dataset. This data is shared with the motion planning system to generate trajectories that do not intersect with the operator's future paths, minimizing downtime due to safety stoppages. The solution is integrated and evaluated within an industrial high payload collaborative robot context, and the results have shown improvements in cycle time efficiency and operator approval. This approach offers a promising direction for enhancing safety and productivity in HRC workspaces.

Influence of different position modal parameters on milling chatter stability of orthopedic surgery robots

This research is dedicated to exploring the dynamics of milling chatter stability in orthopedic surgery robots, focusing on the impact of position modal parameters on chatter stability. Initially, we develop a dynamic milling force model for the robotic milling process that integrates both modal coupling and regenerative effects. We then employ the zero-order frequency domain method to derive a chatter stability domain model, visually represented through stability lobe diagrams (SLDs). Through conducting hammer test experiments, we ascertain the robot's modal parameters at varying positions, enabling the precise generation of SLDs. This study also includes experimental validation of the chatter SLD analysis method, laying the groundwork for further examination of chatter stability across different positional modal parameters. Finally, our analysis of the variations in modal parameters on the stability of robot milling chatter yields a theoretical framework for optimizing cutting parameters and developing control strategies within the context of orthopedic surgery robots.

Simulation Analysis and Comparative Study of MOSGT with Other Techniques

This paper presents a comparative study of the Multi-Objective Symmetric Game Theory (MOSGT) for task offloading in cloud robots with existing techniques such as Genetic Algorithm 4 Collaborative Computation Offloading (GA4CCO) and Approximation Collaborative Computation Offloading (ACCO). The study employs a simulation analysis to evaluate the performance of the proposed MOSGT model in a cloud robotics context. The results of the simulation study reveal that the MOSGT model reduces system cost by approximately 35% compared to GA4CCO and by 20% compared to ACCO. Furthermore, the MOSGT model exhibits a reduced rate of 30% less than GA4CCO and 25% minimal than ACCO in terms of iteration. These findings underscore the potential of MOSGT as a promising solution for efficient task offloading in cloud robots, offering significant improvements in computational complexity and service quality in edge computing.

Integrating augmented reality, gesture recognition, and NLP for enhancing underwater human-robot interaction

This paper presents an in-depth exploration into the integration of augmented reality (AR), gesture recognition, and natural language processing (NLP) to enhance human-robot interaction (HRI) within the context of underwater robotics. It highlights the significant potential these technologies hold in addressing the unique challenges faced in underwater environments, such as limited visibility, complex navigation, and the need for precise, intuitive communication between divers and robots. By reviewing current technological advancements and applications, the study underscores the critical role of AR in providing real-time visual feedback, gesture recognition in enabling more natural control mechanisms, and NLP in facilitating voice-driven commands and interactions. The research further discusses the development of a conceptual framework for an AR-based intuitive interface that synergizes gesture recognition and NLP, aiming to revolutionize underwater HRI by making it more efficient, safe, and user-friendly. Through this investigation, the paper seeks to contribute to the advancement of underwater robotics, proposing innovative solutions that could significantly improve human-robot collaboration in challenging aquatic missions.

How Service Robots Facilitate User Self-Disclosure: The Roles of Personality, Animacy, and Automated Social Presence

This study investigates whether an extroverted personality of service robots can facilitate user self-disclosure and enhance human–robot intimacy. An experiment of 2 2 between-subjects factorial design was conducted to examine the effects of robot personality (extroverted and introverted) and human personality (extroverted and introverted) on user self-disclosure and intimacy. Sixty-four participants from a university campus were included in this study. We simulated a financial investment scenario in which the participants were required to disclose personal information to a robot acting as an investment advisor. Functional near-infrared spectroscopy (fNIRS) was used to capture participants’ prefrontal cortex (PFC) activity. The results revealed a positive effect of robot extroversion on user self-disclosure and intimacy, and this impact was stronger among extroverted participants than among introverted ones. Additionally, perceived robot extroversion enabled users to regard the robot as more animate, promoting automated social presence and ultimately enhancing self-disclosure and intimacy. According to the fNIRS results, the participants who interacted with an extroverted robot had stronger activation in the medial PFC than those who interacted with an introverted robot. The self-reported and fNIRS results were consistently aligned, demonstrating the positive role of extroversion in service robots. These findings provide theoretical and practical insights into the design of service robots in contexts requiring user disclosure.

Learning passive policies with virtual energy tanks in robotics

AbstractWithin a robotic context, the techniques of passivity‐based control and reinforcement learning are merged with the goal of eliminating some of their reciprocal weaknesses, as well as inducing novel promising features in the resulting framework. The contribution is framed in a scenario where passivity‐based control is implemented by means of virtual energy tanks, a control technique developed to achieve closed‐loop passivity for any arbitrary control input. Albeit the latter result is heavily used, it is discussed why its practical application at its current stage remains rather limited, which makes contact with the highly debated claim that passivity‐based techniques are associated with a loss of performance. The use of reinforcement learning allows to learn a control policy that can be passivized using the energy tank architecture, combining the versatility of learning approaches and the system theoretic properties which can be inferred due to the energy tanks. Simulations show the validity of the approach, as well as novel interesting research directions in energy‐aware robotics.

An Innovative Collision-Free Image-Based Visual Servoing Method for Mobile Robot Navigation Based on the Path Planning in the Image Plan.

In this article, we present an innovative approach to 2D visual servoing (IBVS), aiming to guide an object to its destination while avoiding collisions with obstacles and keeping the target within the camera's field of view. A single monocular sensor's sole visual data serves as the basis for our method. The fundamental idea is to manage and control the dynamics associated with any trajectory generated in the image plane. We show that the differential flatness of the system's dynamics can be used to limit arbitrary paths based on the number of points on the object that need to be reached in the image plane. This creates a link between the current configuration and the desired configuration. The number of required points depends on the number of control inputs of the robot used and determines the dimension of the flat output of the system. For a two-wheeled mobile robot, for instance, the coordinates of a single point on the object in the image plane are sufficient, whereas, for a quadcopter with four rotating motors, the trajectory needs to be defined by the coordinates of two points in the image plane. By guaranteeing precise tracking of the chosen trajectory in the image plane, we ensure that problems of collision with obstacles and leaving the camera's field of view are avoided. Our approach is based on the principle of the inverse problem, meaning that when any point on the object is selected in the image plane, it will not be occluded by obstacles or leave the camera's field of view during movement. It is true that proposing any trajectory in the image plane can lead to non-intuitive movements (back and forth) in the Cartesian plane. In the case of backward motion, the robot may collide with obstacles as it navigates without direct vision. Therefore, it is essential to perform optimal trajectory planning that avoids backward movements. To assess the effectiveness of our method, our study focuses exclusively on the challenge of implementing the generated trajectory in the image plane within the specific context of a two-wheeled mobile robot. We use numerical simulations to illustrate the performance of the control strategy we have developed.

“It Brings the Good Vibes”: Exploring Biomorphic Aesthetics in the Design of Soft Personal Robots

The flexible bodies of soft robots provide exciting new possibilities for interaction with humans. In this paper, we propose a novel design paradigm, Soft Biomorphism, for soft robots centered on the idea of amplifying their inherent biomorphic aesthetic qualities and activating these as affordances for human interaction. Following this approach, we developed a set of biomorphic soft robotic prototypes and conducted two studies to understand the effects of biomorphic design aesthetics on people’s impressions of these prototypes. Based on qualitative data collected through five workshop sessions, the first exploratory study (n = 10) sought to investigate the envisioned uses and types of interactions that prototypes elicited within the context of personal robots. We found that various uses were considered and that most participants associated the biomorphic aesthetic design with soft robots contributing to emotional and physical well-being. Building on these results, we conducted a second study (n = 32) to investigate if soft robots with enhanced biomorphic qualities are perceived as more appealing and appropriate for physical human–robot interaction aimed at supporting well-being. We did not find any statistically significant preference for biomorphic soft robots. However, we found statistically significant differences in appeal ratings post-interaction for some prototypes, suggesting that physical interaction with soft robots can impact the perceived appeal. Based on our findings, we highlight key issues to bear in mind when considering biomorphic aesthetics in soft personal robot designs and provide tentative design recommendations to combine biomorphic and geometric elements and align visual appearance, tactility, and movement in future robot designs.

Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots.

With the development of machine perception and multimodal information decision-making techniques, autonomous driving technology has become a crucial area of advancement in the transportation industry. The optimization of vehicle navigation, path planning, and obstacle avoidance tasks is of paramount importance. In this study, we explore the use of attention mechanisms in a end-to-end architecture for optimizing obstacle avoidance and path planning in autonomous driving vehicles. We position our research within the broader context of robotics, emphasizing the fusion of information and decision-making capabilities. The introduction of attention mechanisms enables vehicles to perceive the environment more accurately by focusing on important information and making informed decisions in complex scenarios. By inputting multimodal information, such as images and LiDAR data, into the attention mechanism module, the system can automatically learn and weigh crucial environmental features, thereby placing greater emphasis on key information during obstacle avoidance decisions. Additionally, we leverage the end-to-end architecture and draw from classical theories and algorithms in the field of robotics to enhance the perception and decision-making abilities of autonomous driving vehicles. Furthermore, we address the optimization of path planning using attention mechanisms. We transform the vehicle's navigation task into a sequential decision-making problem and employ LSTM (Long Short-Term Memory) models to handle dynamic navigation in varying environments. By applying attention mechanisms to weigh key points along the navigation path, the vehicle can flexibly select the optimal route and dynamically adjust it based on real-time conditions. Finally, we conducted extensive experimental evaluations and software experiments on the proposed end-to-end architecture on real road datasets. The method effectively avoids obstacles, adheres to traffic rules, and achieves stable, safe, and efficient autonomous driving in diverse road scenarios. This research provides an effective solution for optimizing obstacle avoidance and path planning in the field of autonomous driving. Moreover, it contributes to the advancement and practical applications of multimodal information fusion in navigation, localization, and human-robot interaction.

Potential Facilitators of and Barriers to Implementing the MINI Robot in Community-Based Meeting Centers for People With Dementia and Their Carers in the Netherlands and Spain: Explorative Qualitative Study.

Social robots, as a form of digital health technologies, are used to support emotional, cognitive, and physical care and have shown promising outcomes in enhancing social well-being in people with dementia (PwD) by boosting emotions, social interactions, and activity participation. The goal is to investigate the attitude of stakeholders and potential facilitators and the barriers to implementing the social robot MINI in community-based meeting centers (MCs) for PwD and carers in the Netherlands and Spain. Based on the British Medical Research Council guidance for process evaluation of the implementation of complex interventions and the model for tracing the facilitators of and barriers to the adaptive implementation of innovations in dementia care, an explorative qualitative study was conducted. Following the introduction of the MINI robot, 11 stakeholders were interviewed in 3 MCs in the Netherlands and 1 in Spain, as well as stakeholders in health and welfare organizations in both countries. In addition, 12 adults with dementia participated in focus groups. The data were thematically analyzed and narratively described. Overall, the stakeholder opinion and interest in the MINI robot were positive. The most important (expected) facilitating factors mentioned by stakeholders appeared to be human resources, funding, the impact of the MINI robot on the users and programs of the MCs, characteristics of the innovation, and collaboration with other care and welfare organizations. However, the (expected) barriers mentioned concerned the physical context and functionalities of the MINI robot, the user context, and MC activity policies. The findings will inform professional stakeholders, such as MC directors and managers, as well as care and welfare organizations, on the practicality of using the MINI robot in MCs. Furthermore, our research will aid MINI robot developers in tailoring its features to PwD's preferences and demands and MC policies, which will contribute to the MINI robot's effective adoption and deployment.

Concealing Robots in Environments: Enhancing Navigation and Privacy through Stealth Integration

With the continuous advancement of robotics technology, the integration of robots into diverse human environments has become increasingly prevalent. However, the presence of robots in public spaces can often elicit discomfort or unease among individuals. To address this concern, the concept of concealing robots in various settings has emerged as an innovative approach to improve robot navigation and interaction while minimizing intrusion on human privacy. This paper explores the motivations, challenges, and potential benefits of hiding robots in different environments, particularly within the context of swarm robotics where multiple interconnected robots form a cohesive swarm. Equipped with onboard processing, communication, and sensing capabilities, these robots can autonomously interact with each other and adapt to the environment.The paper investigates the problem of maximizing the number of hidden orthogonal swarm robots, considering scenarios in which robots need to navigate and operate within polygonal environments. Specifically, it presents a 4-approximation algorithm for computing a maximum hidden robot set in such environments. The algorithm offers a practical solution for determining an efficient arrangement of robots that minimizes their visibility while ensuring effective swarm operation.By concealing robots in diverse environments, several benefits can be achieved. First, it helps to alleviate discomfort or unease among individuals, allowing for smoother integration of robots into public spaces. Additionally, concealing robots enhances their navigation capabilities by leveraging stealth techniques, allowing them to move seamlessly and unobtrusively within the environment. This approach also promotes improved human-robot interaction, as the reduced visibility of the robots can alleviate concerns and foster a more natural and comfortable interaction between humans and robots. The paper sheds light on the current state of the field, discussing the motivations behind concealing robots in different settings and highlighting the challenges that need to be addressed. Furthermore, it presents insights into future directions, including the development of more advanced stealth technologies, ethical frameworks for integrating hidden robots, and the potential impact on urban planning and infrastructure. In conclusion, hiding robots in diverse environments offers a promising approach to enhancing robot navigation, interaction, and privacy. The research presented in this paper contributes to the understanding of this emerging field and provides a foundation for further exploration and development of hiding strategies for swarm robotics in various settings.

A low-cost table-top robot platform for measurement science education in robotics and artificial intelligence

Robotics and artificial intelligence represent highly interdisciplinary fields, which – in particular on the bachelor level - makes providing a strong fundamental background in the education challenging. With respect to lab exercises in measurement, one approach to provide interdisciplinary hands-on experience is to embed experiments for measurement science in a robotic context. We present a low-cost robot platform that can be used to address several measurement science and sensor topics, and also other aspects such as machine learning, actuators and mechanics. The 3D printed chassis can be equipped with different sensors for environment perception but also be adapted to different embedded PC platforms. In order to also introduce concepts of robot simulation and realization approaches in a hands-on fashion, the table-top robot is also available as a digital twin in the simulation environments Gazebo and CoppeliaSim®, where, e.g., limitations of simulations and required adaption of models to consider non ideal effects of sensors can be studied.

Design and Fabrication of Bio-Inspired Robotic Systems for Developed Mobility and Functionality in Unstructured Environments

In turn, bio-inspired robotic systems have significantly contributed to the development of well-abled robots capable of moving and manipulating with ease in unknown environments. Imitating biological creatures in their mechanisms of adaptation and effectiveness of design gives these robots enhanced qualities of mobility, functionality, and resilience. In fact, major advances build on flexible joints, controlled underactuation, adaptive control, and sensory arrays of high quality, enabling these robots to traverse rough environments and perform high-precision operations inside the human body. For instance, snake-like robots should be able to move through narrow gaps and debris in some kind of search and rescue task much like a real snake, robotic fish can move effectively across underwater environments while monitoring water quality and marine life. These advancements in technology have broadened the sphere of application and effectiveness of robot applications, proving to be valuable tools in many areas, including disaster response, environmental monitoring, and space exploration. In this regard, one also realizes their significance due to the context of bio-inspired robots regarding the capability of data collection and analyses. The usual methods of data collection are generally limited in reach, recurrence, and resolution. Bio-inspired robots compensate for these inadequacies by continuously gathering various real-time data from environments not easily reachable for humans. More precise and detailed analysis of data resolution collected through these robots supports advanced techniques such as AI and machine learning. This is a very crucial capacity that is central to applications such as following wildlife, assessing the impacts of climate change, and monitoring pollution levels. The capacity of these robots to provide timely and accurate data ensures efforts in environmental management and conservation. The importance of bioinspired robotics, therefore, comes in the solutions associated with unstructured environments. Through the gains of nature, which have been exploited by evolution, these robots present new solutions that overcome the inability of classical robotic systems. The persistence in the performance and development of this field of science will provide ever-greater achievements, with very probably a very important role in the exploration, understanding, and interaction with the natural world played by bioinspired robotics.