Frontiers in Robotics and AI最新文献

{"title":"Geometric line-of-sight guidance law with exponential switching sliding mode control for marine vehicles' path following.","authors":"Chengren Yuan, Changgeng Shuai, Zhanshuo Zhang, Buyun Li, Yuqiang Cheng, Jianguo Ma","doi":"10.3389/frobt.2025.1598982","DOIUrl":"10.3389/frobt.2025.1598982","url":null,"abstract":"<p><p>Marine vehicle guidance and control technology serves as the core support for advancing marine development and enabling scientific exploration. Its accuracy, autonomy, and environmental adaptability directly determine a vehicle's mission effectiveness in complex marine environments. This paper explores path following for marine vehicles in the horizontal plane. To tackle the limitation of a fixed look-ahead distance, we develop a novel geometric line-of-sight guidance law. It adapts to diverse compound paths by dynamically adjusting according to cross-track errors and local path curvature. Then, to enhance control performance, we present an improved exponential switching law for sliding mode control, enabling rapid convergence, disturbance rejection, and chatter reduction. Additionally, integral sliding mode control is integrated to stabilize yaw angular velocity, ensuring the system's global asymptotic stability. Through a series of numerical simulations, the effectiveness, robustness, and adaptability of our proposed methods are verified.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1598982"},"PeriodicalIF":2.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translating human information into robot tasks: action sequence recognition and robot control based on human motions.","authors":"Taichi Obinata, Kazutomo Baba, Akira Uehara, Hiroaki Kawamoto, Yoshiyuki Sankai","doi":"10.3389/frobt.2025.1462833","DOIUrl":"10.3389/frobt.2025.1462833","url":null,"abstract":"<p><p>Long-term use and highly reliable batteries are essential for wearable cyborgs including Hybrid Assistive Limb and wearable vital sensing devices. Consequently, there is ongoing research and development aimed at creating safer next-generation batteries. Researchers, leveraging advanced specialized knowledge and skills, bring products to completion through trial-and-error processes that involve modifying materials, shapes, work protocols, and procedures. When robots can undertake the tedious, repetitive, and attention-demanding tasks currently performed by researchers within facility environments, it will reduce the workload on researchers and ensure reproducibility. In this study, aiming to reduce the workload on researchers and ensure reproducibility in trial-and-error tasks, we proposed and developed a system that collects human motion data, recognizes action sequences, and transfers both physical information (including skeletal coordinates) and task information to a robot. This enables the robot to perform sequential tasks that are traditionally performed by humans. The proposed system employs a non-contact method to acquire three-dimensional skeletal information over time, allowing for quantitative analysis without interfering with sequential tasks. In addition, we developed an action sequence recognition model based on skeletal information and object detection results, which operated independent of background information. This model can adapt to changes in work processes and environments. By translating the human information including the physical and semantic information of a sequential task performed by humans into a robot, the robot can perform the same task. An experiment was conducted to verify this capability using the proposed system. The proposed action sequence recognition method demonstrated high accuracy in recognizing human-performed tasks with an average Edit score of 95.39 and an average F1@10 score of 0.951. In two out of the four trials, the robot adapted to changes in work processes without misrecognizing action sequences and seamlessly executed the sequential task performed by the human. In conclusion, we confirmed the feasibility of using the proposed system.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1462833"},"PeriodicalIF":2.9,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144585206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation theory of mind for heterogeneous human-robot teams.","authors":"Monica Nicolescu, Janelle Blankenburg, Bashira Akter Anima, Mariya Zagainova, Pourya Hoseini, Mircea Nicolescu, David Feil-Seifer","doi":"10.3389/frobt.2025.1533054","DOIUrl":"https://doi.org/10.3389/frobt.2025.1533054","url":null,"abstract":"<p><p>This paper focuses on the problem of collaborative task execution by teams comprising of people and multiple heterogeneous robots. In particular, the problem is motivated by the need for the team members to dynamically coordinate their execution, in order to avoid overlapping actions (i.e. multiple team members working on the same part of the task) and to ensure a correct execution of the task. This paper expands on our own prior work on collaborative task execution by single human-robot and single robot-robot teams, by taking an approach inspired by simulation Theory of Mind (ToM) to develop a real-time distributed architecture that enables collaborative execution of tasks with hierarchical representations and multiple types of execution constraints by teams of people and multiple robots with variable heterogeneity. First, the architecture presents a novel approach for concurrent coordination of task execution with both human and robot teammates. Second, a novel pipeline is developed in order to handle automatic grasping of objects with unknown initial locations. Furthermore, the architecture relies on a novel continuous-valued metric which accounts for a robot's capability to perform tasks during the dynamic, on-line task allocation process. To assess the proposed approach, the architecture is validated with: 1) a heterogeneous team of two humanoid robots and 2) a heterogeneous team of one human and two humanoid robots, performing a household task in different environmental conditions. The results support the proposed approach, as different environmental conditions result in different and continuously changing values for the robots' task execution abilities. Thus, the proposed architecture enables adaptive, real-time collaborative task execution through dynamic task allocation by a heterogeneous human-robot team, for tasks with hierarchical representations and multiple types of constraints.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1533054"},"PeriodicalIF":2.9,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144545476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards applied swarm robotics: current limitations and enablers.","authors":"Miquel Kegeleirs, Mauro Birattari","doi":"10.3389/frobt.2025.1607978","DOIUrl":"10.3389/frobt.2025.1607978","url":null,"abstract":"<p><p>Swarm robotics addresses the design, deployment, and analysis of large groups of robots that collaborate to perform tasks in a decentralized manner. Research in this field has predominantly relied on simulations or small-scale robots with limited sensing, actuation, and computational capabilities. Consequently, despite significant advancements, swarm robotics has yet to see widespread commercial or industrial application. A major barrier to practical deployment is the lack of affordable, modern, and robust platforms suitable for real-world scenarios. Moreover, a narrow definition of what swarm robotics should be has restricted the scope of potential applications. In this paper, we argue that the development of more advanced robotic platforms-incorporating state-of-the-art technologies such as SLAM, computer vision, and reliable communication systems-and the adoption of a broader interpretation of swarm robotics could significantly expand its range of applicability. This would enable robot swarms to tackle a wider variety of real-world tasks and integrate more effectively with existing systems, ultimately paving the way for successful deployment.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1607978"},"PeriodicalIF":2.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metric scale non-fixed obstacles distance estimation using a 3D map and a monocular camera.","authors":"Daijiro Higashi, Naoki Fukuta, Tsuyoshi Tasaki","doi":"10.3389/frobt.2025.1560342","DOIUrl":"10.3389/frobt.2025.1560342","url":null,"abstract":"<p><p>Obstacle avoidance is important for autonomous driving. Metric scale obstacle detection using a monocular camera for obstacle avoidance has been studied. In this study, metric scale obstacle detection means detecting obstacles and measuring the distance to them with a metric scale. We have already developed PMOD-Net, which realizes metric scale obstacle detection by using a monocular camera and a 3D map for autonomous driving. However, PMOD-Net's distance error of non-fixed obstacles that do not exist on the 3D map is large. Accordingly, this study deals with the problem of improving distance estimation of non-fixed obstacles for obstacle avoidance. To solve the problem, we focused on the fact that PMOD-Net simultaneously performed object detection and distance estimation. We have developed a new loss function called \"DifSeg.\" DifSeg is calculated from the distance estimation results on the non-fixed obstacle region, which is defined based on the object detection results. Therefore, DifSeg makes PMOD-Net focus on non-fixed obstacles during training. We evaluated the effect of DifSeg by using CARLA simulator, KITTI, and an original indoor dataset. The evaluation results showed that the distance estimation accuracy was improved on all datasets. Especially in the case of KITTI, the distance estimation error of our method was 2.42 m, which was 2.14 m less than that of the latest monocular depth estimation method.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1560342"},"PeriodicalIF":2.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12198967/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144508851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-modal sensing system for human-robot interaction through tactile and proximity data.","authors":"Gianluca Laudante, Michele Mirto, Olga Pennacchio, Salvatore Pirozzi","doi":"10.3389/frobt.2025.1581154","DOIUrl":"10.3389/frobt.2025.1581154","url":null,"abstract":"<p><strong>Introduction: </strong>The rapid advancement of collaborative robotics has driven significant interest in Human-Robot Interaction (HRI), particularly in scenarios where robots work alongside humans. This paper considers tasks where a human operator teaches the robot an operation that is then performed autonomously.</p><p><strong>Methods: </strong>A multi-modal approach employing tactile fingers and proximity sensors is proposed, where tactile fingers serve as an interface, while proximity sensors enable end-effector movements through contactless interactions and collision avoidance algorithms. In addition, the system is modular to make it adaptable to different tasks.</p><p><strong>Results: </strong>Demonstrative tests show the effectiveness of the proposed system and algorithms. The results illustrate how the tactile and proximity sensors can be used separately or in a combined way to achieve human-robot collaboration.</p><p><strong>Discussion: </strong>The paper demonstrates the use of the proposed system for tasks involving the manipulation of electrical wires. Further studies will investigate how it behaves with object of different shapes and in more complex tasks.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1581154"},"PeriodicalIF":2.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186202/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decentralized nonlinear model predictive control-based flock navigation with real-time obstacle avoidance in unknown obstructed environments.","authors":"Nuthasith Gerdpratoom, Kaoru Yamamoto","doi":"10.3389/frobt.2025.1540808","DOIUrl":"10.3389/frobt.2025.1540808","url":null,"abstract":"<p><p>This work extends our prior work on the distributed nonlinear model predictive control (NMPC) for navigating a robot fleet following a certain flocking behavior in unknown obstructed environments with a more realistic local obstacle-avoidance strategy. More specifically, we integrate the local obstacle-avoidance constraint using point clouds into the NMPC framework. Here, each agent relies on data from its local sensor to perceive and respond to nearby obstacles. A point cloud processing technique is presented for both two-dimensional and three-dimensional point clouds to minimize the computational burden during the optimization. The process consists of directional filtering and down-sampling that significantly reduce the number of data points. The algorithm's performance is validated through realistic 3D simulations in Gazebo, and its practical feasibility is further explored via hardware-in-the-loop (HIL) simulations on embedded platforms. The results demonstrate that the agents can safely navigate through obstructed environments, and the HIL simulation confirms the feasibility of deploying this scheme on an embedded computer. These results suggest that the proposed NMPC scheme is suitable for real-world robotics deployment in decentralized robotic systems operating in complex environments.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1540808"},"PeriodicalIF":2.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12185270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotic optimization of powdered beverages leveraging computer vision and Bayesian optimization.","authors":"Emilia Szymańska, Josie Hughes","doi":"10.3389/frobt.2025.1603729","DOIUrl":"10.3389/frobt.2025.1603729","url":null,"abstract":"<p><p>The growing demand for innovative research in the food industry is driving the adoption of robots in large-scale experimentation, a shift that offers increased precision, repeatability, and efficiency in product manufacturing and evaluation. This paper addresses this need by introducing a robotic system that extends automation into optimization and closed-loop quality control, using powdered cappuccino preparation as a case study. By leveraging Bayesian Optimization and image analysis, the robot explores the parameter space to identify the ideal conditions for producing cappuccino with high foam quality. A computer vision-based feedback loop further improves the beverage by mimicking human-like corrections in preparation process. Findings demonstrate the effectiveness of robotic automation in achieving high repeatability and enabling extensive exploration of system parameters, paving the way for more advanced and reliable food product development.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1603729"},"PeriodicalIF":2.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12183032/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward an active exoskeleton with full energy autonomy.","authors":"Yakir Knafo, Yinjie Zhou, Avi Manor, Alon Osovizky, Raziel Riemer","doi":"10.3389/frobt.2025.1597271","DOIUrl":"10.3389/frobt.2025.1597271","url":null,"abstract":"<p><p>Exoskeletons aim to enhance human performance and reduce physical fatigue. However, one major challenge for active exoskeletons is the need for a power source. This demand is typically met with batteries, which limit the device's operational time. This study presents a novel solution to this challenge: a design that enables the generation of electricity during motions where the muscles work as brakes and absorb energy, with the energy stored and subsequently returned to assist when the muscles function as motors. To achieve this goal, a knee exoskeleton design with a direct drive and a novel electronic board was designed and manufactured to capture the energy generated by the wearer's movements and convert it into electrical energy. The harvested energy is stored in a power bank, and later, during motion, this energy is used to power the exoskeleton motor. Further, the device has torque control and can change the assistive profile and magnitude as needed for different assistance scenarios. Sit-to-stand (STS) motion was chosen as a test case for the first exoskeleton prototype. It was found that, during lowering (from stand to sit), the exoskeleton provided up to 10 Nm and harvested 9.4 J. During rising (from sit to stand), it provided up to 7.6 Nm and was able to return 6.8 J of the harvested energy. Therefore, the cycle efficiency of the exoskeleton system (return divided by harvesting) is 72.3%. In summary, this study introduces the first active exoskeleton for STS that can generate its own electrical power. The results show that the full development of this technology could reduce exoskeletons' need for external energy sources.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1597271"},"PeriodicalIF":2.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12183371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is AI currently capable of identifying wild oysters? A comparison of human annotators against the AI model, ODYSSEE.","authors":"Brendan Campbell, Alan Williams, Kleio Baxevani, Alyssa Campbell, Rushabh Dhoke, Rileigh E Hudock, Xiaomin Lin, Vivek Mange, Bernhard Neuberger, Arjun Suresh, Alhim Vera, Arthur Trembanis, Herbert G Tanner, Edward Hale","doi":"10.3389/frobt.2025.1587033","DOIUrl":"10.3389/frobt.2025.1587033","url":null,"abstract":"<p><p>Oysters are ecologically and commercially important species that require frequent monitoring to track population demographics (e.g., abundance, growth, mortality). Current methods of monitoring oyster reefs often require destructive sampling methods and extensive manual effort. However, these methods are destructive and are suboptimal for small-scale or sensitive environments. A recent alternative, the ODYSSEE model, was developed to use deep learning techniques to identify live oysters using video or images taken in the field of oyster reefs to assess abundance. The validity of this model in identifying live oysters on a reef was compared to expert and non-expert annotators. In addition, we identified potential sources of prediction error. Although the model can make inferences significantly faster than expert and non-expert annotators (39.6 s, <math><mrow><mn>2.34</mn> <mo>±</mo> <mn>0.61</mn></mrow> </math> h, <math><mrow><mn>4.50</mn> <mo>±</mo> <mn>1.46</mn></mrow> </math> h, respectively), the model overpredicted the number of live oysters, achieving lower accuracy (63%) in identifying live oysters compared to experts (74%) and non-experts (75%) alike. Image quality was an important factor in determining the accuracy of the model and annotator. Better quality images improved human accuracy and worsened model accuracy. Although ODYSSEE was not sufficiently accurate, we anticipate that future training on higher-quality images, utilizing additional live imagery, and incorporating additional annotation training classes will greatly improve the model's predictive power based on the results of this analysis. Future research should address methods that improve the detection of living vs dead oysters.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1587033"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12178886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}