Frontiers in Robotics and AI最新文献

Introduction: Continuum robots are studied and applied in neurosurgery due to their high flexibility and adaptability. The basic performance of continuum is mainly evaluated by stiffness, but there is no systematic and universal evaluation system.

Methods: In this paper, a general experimental platform for continuum robots is designed, based on which the fundamental performance of the notched continuum robot used in neurosurgery is evaluated. The continuum stiffness evaluation method based on energy method and Castigliano's second theorem is proposed. By solving the internal force and energy of the notched continuum in sections, the stiffness model of single-segment and double-segment series continuum is established. The relationship between the stiffness of the continuum and the bending angle is obtained.

Results: The simulation and experimental results show that under the condition of small deformation angle, the spatial stiffness model obtained by strain energy basically conforms to the actual model, which verifies the correctness and rationality of the stiffness calculation method proposed in this paper.

Discussion: This paper is of significant importance to promote the performance evaluation and optimization of continuum.

In the realm of real-time environmental monitoring and hazard detection, multi-robot systems present a promising solution for exploring and mapping dynamic fields, particularly in scenarios where human intervention poses safety risks. This research introduces a strategy for path planning and control of a group of mobile sensing robots to efficiently explore and reconstruct a dynamic field consisting of multiple non-overlapping diffusion sources. Our approach integrates a reinforcement learning-based path planning algorithm to guide the multi-robot formation in identifying diffusion sources, with a clustering-based method for destination selection once a new source is detected, to enhance coverage and accelerate exploration in unknown environments. Simulation results and real-world laboratory experiments demonstrate the effectiveness of our approach in exploring and reconstructing dynamic fields. This study advances the field of multi-robot systems in environmental monitoring and has practical implications for rescue missions and field explorations.

Background: The ergonomics of flexible endoscopes require improvement as the current design carries a high risk of musculoskeletal injury for endoscopists. Robotic systems offer a solution by separating the endoscope from the control handle, allowing a focus on ergonomics and usability. Despite the increasing interest in this field, little attention has been paid towards developing ergonomic human input devices. This study addresses two key questions: How can handheld control devices for flexible robotic endoscopy be designed to prioritize ergonomics and usability? And, how effective are these new devices in a simulated clinical environment?

Methods: Addressing this gap, the study proposes two handheld input device models for controlling a flexible endoscope in four degrees of freedom (DOFs) and an endoscopic instrument in three DOFs. A two-stage evaluation was conducted with six endoscopists evaluating the physical ergonomics and a final clinical user evaluation with seven endoscopists using a virtual colonoscopy simulator with proportional velocity and position mapping.

Results and discussion: Both models demonstrated clinical suitability, with the first model scoring 4.8 and the second model scoring 5.2 out of 6 in the final evaluation. In sum, the study presents two designs of ergonomic control devices for robotic colonoscopy, which have the potential to reduce endoscopy-related injuries. Furthermore, the proposed colonoscopy simulator is useful to evaluate the benefits of different mapping modes. This could help to optimize the design and control mechanism of future control devices.

The exceptional performance of general-purpose large models has driven various industries to focus on developing domain-specific models. However, large models are not only time-consuming and labor-intensive during the training phase but also have very high hardware requirements during the inference phase, such as large memory and high computational power. These requirements pose considerable challenges for the practical deployment of large models. As these challenges intensify, model compression has become a vital research focus to address these limitations. This paper presents a comprehensive review of the evolution of model compression techniques, from their inception to future directions. To meet the urgent demand for efficient deployment, we delve into several compression methods-such as quantization, pruning, low-rank decomposition, and knowledge distillation-emphasizing their fundamental principles, recent advancements, and innovative strategies. By offering insights into the latest developments and their implications for practical applications, this review serves as a valuable technical resource for researchers and practitioners, providing a range of strategies for model deployment and laying the groundwork for future advancements in model compression.

A significant number of individuals have been affected by pandemic diseases, such as COVID-19 and seasonal influenza. Nucleic acid testing is a common method for identifying infected patients. However, manual sampling methods require the involvement of numerous healthcare professionals. To address this challenge, we propose a novel transoral swab sampling robot designed to autonomously perform nucleic acid sampling using a visual-tactile fusion approach. The robot comprises a series-parallel hybrid flexible mechanism for precise distal posture adjustment and a visual-tactile perception module for navigation within the subject's oral cavity. The series-parallel hybrid mechanism, driven by flexible shafts, enables omnidirectional bending through coordinated movement of the two segments of the bendable joint. The visual-tactile perception module incorporates a camera to capture oral images of the subject and recognize the nucleic acid sampling point using a deep learning method. Additionally, a force sensor positioned at the distal end of the robot provides feedback on contact force as the swab is inserted into the subject's oral cavity. The sampling robot is capable of autonomously performing transoral swab sampling while navigating using the visual-tactile perception algorithm. Preliminary experimental trials indicate that the designed robot system is feasible, safe, and accurate for sample collection from subjects.

Assistive mobile robots can play an important role in supporting individuals with disabilities. While the field of robot control interfaces for individuals with disabilities is growing, there is little work done on such systems for children end users specifically. Accordingly, we pursued the design of an adapted robot control interface for use in child pediatric occupational therapy (OT). Our target end user, a nine-year-old child with cerebral palsy, leveraged the interface to perform instrumental activities of daily living (e.g., play) with a modern mobile manipulator. We used an iterative design process to adjust and improve the interface via input from the participant's caregivers and occupational therapist, as well as objective participant performance data. Furthermore, we tested the participant's ability to utilize our interface by creating two testing cases: a control case (in which our participant performed standard ALD/IADL tasks) and an experimental case (in which our participant performed ADL/IADL practice activities more tailored toward the child). Key insights during the process included the need for sensitivity to taking up space on the child user's existing power wheelchair, the advantages of integrating technologies familiar to the child (e.g., gaming controls, iPads) in our system design, and the potential value of integrating playful mischief (including playful interactions between the child, their caregivers, and their clinicians) as a part of the playbook for pediatric OT. This work can serve to inform and augment new OT strategies for the marginalized population of young children with disabilities.

Due to the increasing employment of people with disabilities, support for the elderly, and childcare needs, enhancing the independence and freedom across generations and spaces is necessary. This study aimed to develop a human-collaborative robot using multimodal vital information as input with cybernics space, which is fused "human" and "Cyber/Physical Space," and confirm its feasibility experimentally. The robot allows the user to operate it via gaze and bio-electrical signals (BES), reflecting the user's intentions, and seamlessly transition among three modes (i.e., assistant, jockey, and ghost). In the assistant mode, the user collaborates with the robot in the physical space using a system that includes a head-mounted display (HMD) for gaze measurement, BES measurement unit, personal mobility system, and an arm-hand system. The HMD can be flipped up and down for hands-free control. The BES measurement unit captures leaked weak signals from the skin surface, indicating the user's voluntary movement intentions, which are processed by the main unit to generate control commands for the various actuators. The personal mobility system features omni-wheels for tight turning, and the arm-hand system can handle payloads up to 500 g. In the jockey mode, the user remotely operates a small mobile base with a display and camera, moving it through the physical space. In the ghost mode, the user navigates and inputs commands in a virtual space using a smart key and remote-control device integrated with IoT and wireless communication. The switching of each control mode is estimated using the BES from the user's upper arm, gaze direction, and position, thereby enabling movement, mobility, and manipulation without physical body movement. In basic experiments involving able-bodied participants, the macro averages of recall, precision, and F score were 1.00, 0.90, and 0.94, respectively, in the assistant mode. The macro averages of recall, precision, and F score were 0.85, 0.92, and 0.88, respectively, in the ghost mode. Therefore, the human-collaborative robot utilizing multimodal vital information has feasibility for supporting daily life tasks, contributing to a safer and more secure society by addressing various daily life challenges.

Research on integrating emerging technologies, such as robots, into K-12 education has been growing because of their benefits in creating engaging learning environments and preparing children for appropriate human-robot interactions in the future. However, most studies have focused on the impact of robots in formal educational settings, leaving their effectiveness in informal settings, such as afterschool programs, unclear. The present study developed a 9-week afterschool program in an elementary school to promote STEAM (STEM + Art) education for elementary school students. The program incorporated four modules (Acting, Dancing, Music & Sounds, and Drawing), each with specific learning objectives and concluding with a theater play at the end. This program facilitated hands-on activities with social robots to create engaging learning experiences for children. A total of 38 students, aged 6-10 years, participated in the afterschool program. Among these students, 21 took part in research activities, which included answering questions about their perceptions of robots compared to other entities (i.e., babies and beetles), learning interest and curiosity, and their opinions about robots. In addition, four teachers and staff participated in interviews, sharing their reflections on children's learning experiences with robots and their perceptions of the program. Our results showed that 1) children perceived robots as having limited affective and social capabilities but gained a more realistic understanding of their physiological senses and agentic capabilities; 2) children were enthusiastic about interacting with robots and learning about robot-related technologies, and 3) teachers recognized the importance of embodied learning and the benefits of using robots in the afterschool program; however, they also expressed concerns that robots could be potential distractions and negatively impact students' interpersonal relationships with peers in educational settings. These findings suggest how robots can shape children's perceptions of robots and their learning experiences in informal education, providing design guidelines for future educational programs that incorporate social robots for young learners.