Frontiers in Robotics and AI最新文献

As aquaculture expands to meet global food demand, it remains dependent on manual, costly, infrequent, and high-risk operations due to reliance on high-end Remotely Operated Vehicles (ROVs). Scalable and autonomous systems are needed to enable safer and more efficient practices. This paper proposes a cost-effective autonomous inspection framework for the monitoring of mooring systems, a critical component ensuring structural integrity and regulatory compliance for both the aquaculture and floating offshore wind (FOW) sectors. The core contribution of this paper is a modular and scalable vision-based inspection pipeline built on the open-source Robot Operating System 2 (ROS 2) and implemented on a low-cost Blueye X3 underwater drone. The system integrates real-time image enhancement, YOLOv5-based object detection, and 4-DOF visual servoing for autonomous tracking of mooring lines. Additionally, the pipeline supports 3D reconstruction of the observed structure using tools such as ORB-SLAM3 and Meshroom, enabling future capabilities in change detection and defect identification. Validation results from simulation, dock and sea trials showed that the underwater drone can effective inspect of mooring system critical components with real-time processing on edge hardware. A cost estimation for the proposed approach showed a substantial reduction as compared with traditional ROV-based inspections. By increasing the Level of Autonomy (LoA) of off-the-shelf drones, this work provides (1) safer operations by replacing crew-dependent and costly operations that require a ROV and a mothership, (2) scalable monitoring and (3) regulatory-ready documentation. This offers a practical, cross-industry solution for sustainable offshore infrastructure management.

Introduction: This paper presents a participatory design approach for developing assistive robots, addressing the critical gap between designing robotic applications and real-world user needs. Traditional design methodologies often fail to capture authentic requirements due to users' limited familiarity with robotic technologies and the disconnection between design activities and actual deployment contexts.

Methods: We propose a methodology centred on iterative in-situ co-design, where stakeholders collaborate with researchers using functional low-fidelity prototypes within the actual environment of use. Our approach comprises three phases: observation and inspiration, in-situ co-design through prototyping, which is the core of the methodology, and longitudinal evaluation. We implemented this methodology over 10 months at an intermediate healthcare centre. The process involved healthcare staff in defining functionality, designing interactions, and refining system behaviour through hands-on experience with teleoperated prototypes.

Results: The resulting autonomous patrolling robot operated continuously across a two-month deployment. The evaluation through questionnaires on usability, usage and understanding of the robotic system, along with open-ended questions revealed diverse user adoption patterns, with five distinct personas emerging: enthusiastic high-adopter, disillusioned high-adopter, unconvinced mid-adopter, satisfied mid-adopter and non-adopter, which are discussed in detail.

Discussion: During the final evaluation deployment, user feedback still identified both new needs and practical improvements, as co-design iterations have the potential to continue indefinitely. Moreover, despite some performance issues, the robot's presence seemed to generate a placebo effect on both staff and patients, while it appears that staff's behaviours were also influenced by the regular observation of the researchers. The obtained results prove valuable insights into long-term human-robot interaction dynamics, highlighting the importance of context-based requirements gathering.

Social robots have the potential to support the health activities of older adults. However, they need to be designed for their specific needs; be accepted by and useful to them; and be integrated into their healthcare ecosystem and care network. We explored the research literature to determine the evidence base to guide design considerations necessary for socially assistive robots (SARs) for older adults in the context of healthcare. We identified various elements of the user-centered design of SARs to meet the needs of older adults within the constraints of a home environment. We emphasized the potential benefits of SARs in empowering older adults and supporting their autonomy for health applications. We identified research gaps and provided a road map for future development and deployment to enhance SAR functionality within digital health systems.

With ageing populations and decreasing numbers of care personnel, care technologies such as socially assistive robots offer innovative solutions for healthcare workers and older adults, supporting ageing in place. Among others, SARs are used for both daytime structure support and social companionship, particularly benefiting people with dementia by providing structure in earlier stages of the disease and comfort in later stages. This research introduces the concept of Huggable Integrated SARs (HI-SAR): a novel subtype of SARs combining a soft, comforting, huggable form with integrated socially assistive functionalities, such as verbal prompts for daytime structure, interactive companionship, and activity monitoring via sensor data, enabling the possibility of more context-aware interaction. While HI-SARs have shown promise in Asian care contexts, real-world application and potential in diverse long-term care contexts remain limited and underexplored. This research investigates the potential of HI-SARs in Dutch healthcare settings (eldercare, disability care, and rehabilitation) through three studies conducted between September 2023 and December 2024. Study I examined HI-SAR functions and integration in Dutch care practice via focus groups with professionals, innovation managers, and older adults (N = 36). Study II explored user preferences through sessions with clients with intellectual disabilities and professionals (N = 32). Study III involved two case studies in care settings with clients and caregivers (N = 4). Results indicate that HI-SARs were generally well-received by professionals and older adults, who appreciated their support for daily routines and social engagement, particularly for clients with cognitive disabilities such as dementia. However, concerns were raised about hygiene, the functioning of activity monitoring, and limited interactivity. Based on these findings, we recommend four design and implementation strategies to improve the effectiveness of HI-SARs: (1) integrating personalisation options such as customizable voices to increase user acceptance; (2) optimising activity monitoring by simplifying data output and using sensor input more proactively to trigger interactions; (3) considering persons with cognitive impairments as a first target user group; and (4) encouraging individual use to enhance hygiene and tailor experiences to client needs. Overall, this research demonstrates the potential of HI-SARs in diverse long-term care settings, although further research is needed to explore their applicability, usability, and long-term impact.

Introduction: The challenges encountered in the design of multi-robot teams (MRT) highlight the need for different levels of human involvement, creating human-in-the-loop multi-robot teams. By integrating human cognitive abilities with the functionalities of the robots in the MRT, we can enhance overall system performance. Designing such a human-in-the-loop MRT requires several decisions based on the specific context of application. Before implementing these systems in real-world scenarios, it is essential to model and simulate the various components of the MRT to evaluate their impact on performance and the different roles a human operator might play.

Methods: We developed a simulation framework for a human-in-the-loop MRT using the Java Agent DEvelopment framework (JADE) and investigated the effects of different numbers of robots in the MRT, MRT architectures, and levels of human involvement (human collaboration and human intervention) on performance metrics.

Results: Results show that task execution outcomes and request completion times (RCT) improve with an increasing number of robots in the MRT. Human collaboration reduced the RCT, while human intervention increased the RCT, regardless of the number of robots in the MRT. The effect of system architecture was only significant when the number of robots in the MRT was low.

Discussion: This study demonstrates that both the number of robots in a multi-robot team (MRT) and the inclusion of a human in the loop significantly influence system performance. The findings also highlight the value of simulation as a cost- and time-efficiency strategy to evaluate MRT configurations prior to real-world implementation.

Falls are a major risk factor among the elderly, often resulting in injuries that compromise independence and quality of life. Conventional walking aids lack active stabilization capabilities and are therefore limited in effectively preventing balance-related accidents. This paper presents the design and control of a smart robotic assistant aimed at reducing fall risk in elderly users by providing real-time balance support. The proposed system uses a wearable inertial measurement unit to detect postural imbalances in the sagittal (front-back) and frontal (side-to-side) planes. When instability is detected, the robotic arm generates compensatory forces or torques through linear or rotational actuators to help the user regain a stable posture. Using a cascaded control architecture, the outer loop is designed to maintain the user's upright posture, while the inner loop ensures fast and accurate actuator performance. To enable effective and reliable control in the real system, actuator dynamics are characterized through an optimization-based system identification approach, resulting in transfer function models with over 98% accuracy. Based on these models, PID controllers are optimally tuned using an optimization algorithm to ensure fast and accurate corrective action. The system effectively returns the user to a stable position within 2.3 ± 0.3 s for linear actuation (with a response time of 120 ± 10 ms) and 2.2 ± 0.2 s for rotary actuation (with a response time of 140 ± 15 ms), providing safe posture return during imbalance events. To further enhance safety, an automatic braking mechanism immobilizes the walking aid during corrective maneuvers. Experimental validation demonstrates the system's effectiveness in detecting and correcting postural imbalances in both the sagittal and frontal planes under dynamic conditions. These results highlight the potential for enhancing mobility, safety, and therapeutic support for older adults, contributing to the advancement of assistive fall-prevention technologies.

Background: As global populations age, healthcare and social systems face mounting pressure to provide effective support for older adults. Social robots have emerged as promising tools to enhance companionship, cognitive engagement, and daily assistance. However, fear of robots among older adults remains a critical barrier to adoption.

Objective: This scoping review examined how fear manifests in human-robot interaction (HRI), what factors contribute to these reactions, and how they influence technology acceptance.

Methods: A systematic search of six major databases (PubMed, Scopus, IEEE Xplore, ACM Digital Library, PsycINFO, and Web of Science) identified studies published between January 2014 and March 2025. Following PRISMA-ScR guidelines, 49 studies were included, encompassing 6,670 older participants across 16 countries.

Results: Thematic synthesis revealed seven main fear categories: privacy and autonomy concerns, trust and reliability issues, emotional and ethical discomfort, usability challenges, fear of dependence, unfamiliarity with technology, and the Uncanny Valley effect. Fear levels were shaped by robot design, cultural background, prior technology experience, and contextual factors such as care settings. Mitigation strategies, including co-design with older adults, gradual exposure, transparent system behavior, and emotionally congruent interaction, were associated with improved acceptance.

Conclusions: This review uniquely maps fear typologies to robot functions and intervention strategies, offering a framework to guide emotionally adaptive and culturally sensitive robot design. Addressing emotional barriers is essential for the ethical and effective integration of social robots into eldercare. Future research should prioritize longitudinal, cross-cultural studies and standardized fear measurement tools to advance evidence-based HRI implementation.

Play is a fundamental activity through which humans and animals acquire skills and competencies. Robots are increasingly capable of engaging in playful interactions with humans, offering new opportunities for learning, development, and social connection. Unlike traditional toys, robots possess autonomy and expressive capabilities, enabling them to propose actions, respond meaningfully, and exhibit intentions and emotions. This transforms the nature of play, making it more interactive and adaptive. For individuals with cognitive or physical impairments, robots can serve as predictable and engaging companions that attract attention, foster motivation, and facilitate social interaction in group settings. In this paper, we present a comprehensive framework to support the design of play-oriented robots and activities. Drawing on more than 20 years of research and development, we provide examples of low-cost robotic systems tailored for diverse user needs, including both typically developing individuals and those with disabilities. Through selected case studies, we demonstrate the effectiveness and versatility of our approach in supporting the design and analysis of playful experiences that are inclusive, goal-oriented, and developmentally beneficial.

Mobile robots have emerged as a reliable solution for dynamic navigation in real-world applications. Effective deployment in high-density crowds and emergency scenarios requires not only accurate path planning but also rapid adaptation to changing environments. However, autonomous navigation in such environments remains a significant challenge, particularly in time-sensitive applications such as emergency response. Existing path planning and reinforcement learning approaches often lack adaptability to uncertainties and time-varying obstacles, thereby making them less suitable for unstructured real-world scenarios. To address these limitations, a Deep Reinforcement Learning (DRL) framework for dynamic crowd navigation using three algorithms, Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Deep Q-Network (DQN), is proposed. A context-aware state representation that combines Light Detection and Ranging (LiDAR)-based obstacle perception, goal orientation, and robot kinematics to enhance situational awareness is developed. The proposed framework is implemented in a ROS2 Gazebo simulation environment using the TurtleBot3 platform and tested in challenging scenarios to identify the most effective algorithm. Extensive simulation analysis demonstrates that TD3 outperforms the other approaches in terms of success rate, path efficiency, and collision avoidance. This study contributes a reproducible, constraint-aware DRL navigation architecture suitable for real-time, emergency-oriented mobile robot applications.

Human-autonomy teaming is an increasingly integral component of operational environments, including crewed and remotely operated space missions, military settings, and public safety. The performance of such teams relies on proper trust in the autonomous system, thus creating an urgent need to capture the dynamic nature of trust and devise objective, non-disruptive means of precisely modeling trust. This paper describes the use of bio-signals and embedded measures to create a model capable of inferring and predicting trust. Data (2304 observations) was collected via human subject testing (n = 12, 7M/5F) during which participants interacted with a simulated autonomous system in an operationally relevant, human-on-the-loop, remote monitoring task and reported their subjective trust via visual analog scales. Electrocardiogram, respiration, electrodermal activity, electroencephalogram, functional near-infrared spectroscopy, eye-tracking, and button click data were collected during each trial. Operator background information were collected prior to the experiment. Features were extracted and algorithmically down-selected, then ordinary least squares regression was used to fit the model, and predictive capabilities were assessed on unseen trials. Model predictions achieved a high level of accuracy with a Q² of 0.64 and captured rapid changes in trust during an operationally relevant human-autonomy teaming task. The model advances the field of non-disruptive means of inferring trust by incorporating a broad suite of physiological signals into a model that is predictive, while many current models are purely descriptive. Future work should assess model performance on unseen participants.