Frontiers in Robotics and AI最新文献

This paper examines the evolving landscape of mobile robotics, focusing on challenges faced by roboticists working in industry when integrating robots into human-populated environments. Through interviews with sixteen industry professionals specializing in social mobile robotics, we examined two primary research questions: (1) What approaches to person detection and representation are used in industry? and (2) How does the relationship between industry and academia impact the research process? Our findings reveal diverse approaches to human detection, ranging from basic obstacle avoidance to advanced systems that differentiate among classes of humans. We suggest that robotic system design overall and human detection in particular are influenced by whether researchers use a framework of safety or sociality, how they approach building complex systems, and how they develop metrics for success. Additionally, we highlight the gaps and synergies between industry and academic research, particularly regarding commercial readiness and the incorporation of human-robot interaction (HRI) principles into robotic development. This study underscores the importance of addressing the complexities of social navigation in real-world settings and suggests that strengthening avenues of communication between industry and academia will help to shape a sustainable role for robots in the physical and social world.

Introduction: This study introduces a custom-built uncrewed aerial vehicle (UAV) designed for precision agriculture, emphasizing modularity, adaptability, and affordability. Unlike commercial UAVs restricted by proprietary systems, this platform offers full customization and advanced autonomy capabilities.

Methods: The UAV integrates a Cube Blue flight controller for low-level control with a Raspberry Pi 4 companion computer that runs a Model Predictive Control (MPC) algorithm for high-level trajectory optimization. Instead of conventional PID controllers, this work adopts an optimal control strategy using MPC. The system also incorporates Kalman filtering to enable adaptive mission planning and real-time coordination with a moving uncrewed ground vehicle (UGV). Testing was performed in both simulation and outdoor field environments, covering static and dynamic waypoint tracking as well as complex trajectories.

Results: The UAV performed figure-eight, curved, and wind-disturbed trajectories with root mean square error values consistently between 8 and 20 cm during autonomous operations, with slightly higher errors in more complex trajectories. The system successfully followed a moving UGV along nonlinear, curved paths.

Discussion: These results demonstrate that the proposed UAV platform is capable of precise autonomous navigation and real-time coordination, confirming its suitability for real-world agricultural applications and offering a flexible alternative to commercial UAV systems.

Introduction: As robotic technologies become increasingly integrated into care settings, it is critical to assess their impact within the complexity of real-world contexts. This exploratory study examines the introduction of a robot cat for children with Autism Spectrum Disorder (ASD) in a specialist dental care unit. Children with ASD often face challenges in dental care, including anxiety, sensory sensitivities, and difficulty with collaboration. The study investigates if a robot cat can provide psychosocial support to the patients.

Methods: Ten patients, aged 5-10, participated in the 12-months study, each undergoing one baseline session without the robot and 3-5 subsequent visits with the robot, yielding 37 sessions of video data.

Results: Reflexive thematic analysis revealed three key themes: the robot cat can enhance training and treatment, robot cats can serve as a beneficial but a non-essential tool, and robot cats can sometimes hinder progress in training and treatment. These findings highlight significant individual variation in how the robot was experienced, shaped by context, timing, and emotional state. The robot's role was not universally positive or passive; its effectiveness depended on how it was integrated into personalised care strategies by the dental hygienist, guardians, and the patients themselves.

Discussion: This study underscores the importance of tailoring technological interventions in care, advocating for cautious, context-sensitive use rather than one-size-fits-all solutions. Future work should further explore adaptive, individualised deployment.

For autonomous mobile robots to operate effectively in human environments, navigation must extend beyond obstacle avoidance to incorporate social awareness. Safe and fluid interaction in shared spaces requires the ability to interpret human motion and adapt to social norms-an area that is being reshaped by advances in learning-based methods. This review examines recent progress in learning-based social navigation methods that deal with the complexities of human-robot coexistence. We introduce a taxonomy of navigation methods and analyze core system components, including realistic training environments and objectives that promote socially compliant behavior. We conduct a comprehensive benchmark of existing frameworks in challenging crowd scenarios, showing their advantages and shortcomings, while providing critical insights into the architectural choices that impact performance. We find that many learning-based approaches outperform model-based methods in realistic coordination scenarios such as navigating doorways. A key highlight is the end-to-end models, which achieve strong performance by directly planning from raw sensor input, enabling more efficient and adaptive navigation. This review also maps current trends and outlines ongoing challenges, offering a strategic roadmap for future research. We emphasize the need for models that accurately anticipate human movement, training environments that realistically simulate crowded spaces, and evaluation methods that capture real-world complexity. Advancing these areas will help overcome current limitations and move social navigation systems closer to safe, reliable deployment in everyday environments. Additional resources are available at: https://socialnavigation.github.io.

Robotic grippers are widely utilized in industrial manufacturing, but object slippage during assembly poses challenges, including potential damage, delays, and increased costs. Therefore, early slip detection is crucial for efficient manufacturing operations. Piezoelectric tactile sensors using polyvinylidene fluoride (PVDF) have been developed to detect vibrations. Nevertheless, the development of such sensors with a simple structure and lower fabrication cost, continues to be a challenging task. The analysis on the effect of the thicknesses of soft body layers that attached to sensing elements on the slip sensor's performance has yet been discussed. In this project, a simple-structured and low-cost design of a flexible piezoelectric tactile sensor based on PVDF to estimate slip using robotic gripper is presented. The effect of different thicknesses of soft body layer made of silicone rubber and the sensor's performance in detecting slip is discussed. A PVDF-based sensor is attached to soft body layer that is incorporated into a robotic gripper. Experimental results demonstrate that sensor sensitivity increases with lower soft body layer thickness. Additionally, the sensor's signal amplitude increases with object load, indicating slip intensity. This advancement addresses challenges in fabricating simple structures and cost-effective piezoelectric sensors which enhance robotic gripper functionality in industrial applications.

Parkinson's Disease is a progressively advancing neurological condition. Its severity is evaluated by utilizing the Hoehn and Yahr staging scale. Such assessments may be inconsistent, are more time-consuming, and expensive for patients. To address these shortcomings, this article introduces a machine learning-based gait classification system to assist doctors in identifying the stages of Parkinson's disease. This study utilizes two open-access benchmark datasets from PhysioNet and Figshare to assess ground reaction force collected from patients diagnosed with Parkinson's Disease. This study presents experiments conducted using machine learning algorithms namely Decision Tree, Random Forest, Extreme Gradient Boost, and Light Gradient Boosting Machine classification algorithms to predict severity of Parkinson's Disease. Among all the four algorithms, Light Gradient Boosting Machine classification algorithm have proven its supremacy. It gave an accuracy of 98.25%, Precision of 98.35%, Recall of 98.25%, and F1 Score of 98% for dataset 1. The performance of the algorithm slightly declines on dataset 2. It reports accuracy of 85%, Precision of 95%, Recall of 85% and F1 Score of 89% for dataset 2. Furthermore, this study used Explainable Artificial Intelligence to display the LightGBM classifier's classification pathways for Parkinson's disease severity prediction using Hoehn and Yahr staging on the scale from 0 to 5. This is helps the health experts in decision making. This work provides automated assistance to doctors for the rapid screening of Parkinson's disease patients based on disease severity. This work leaves a scope for integrating wearable sensors and developing real-time monitoring system for screening of Parkinson's Disease patients.