Frontiers in Robotics and AI最新文献

{"title":"Multi-layer robotic controller for enhancing the safety of mobile robot navigation in human-centered indoor environments.","authors":"Karameldeen Omer, Andrea Monteriù","doi":"10.3389/frobt.2025.1629931","DOIUrl":"10.3389/frobt.2025.1629931","url":null,"abstract":"<p><p>This research proposes a multi-layer navigation system for indoor mobile robots when they share space with vulnerable individuals. The primary objectives are increasing or maintaining safety measures and curtailing operational costs, emphasizing reducing reliance on intricate sensor technologies and computational resources. The developed system employs a three-tiered control approach, with each layer playing a pivotal role in the navigation process. The \"online\" control layer integrates a human-in-the-loop strategy, where the human operator detects missing obstacles or approaching danger through a user interface and sends a trigger to the robot's controller. This trigger enables the system to estimate the coordinates of the danger and update the robot's navigation path in real time, minimizing reliance on complex sensor systems. The \"semi-online\" control layer generates dynamic virtual barriers to restrict the robot's navigation in specific areas during specific times. This ensures the robot avoids hazardous zones that could pose temporary risks to the human or robot. For example, areas with temporary obstructions or potential danger, such as kids' play zones or during cleaning, are temporarily restricted from the robot's path, ensuring safe navigation without relying solely on real-time sensor data. The \"offline\" control layer centers around the use of semantic information to control the robot's behavior according to user-defined space management and safety requirements. By leveraging Building Information Models (BIM) as digital twins, this layer combines semantic and geometric data to comprehensively understand the environment. It enables the robot to navigate according to precise user requirements, utilizing the semantic context for path planning and behavior control. This layer obviates the need for a real-time sensor mapping process, making the system more efficient and adaptable to user needs. This research represents a significant step forward in enhancing the navigational capabilities of robots within human-centric indoor environments, with a core focus on safety, adaptability, and cost-effectiveness.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1629931"},"PeriodicalIF":3.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12350386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875990","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":"Autonomous navigation of quadrupeds using coverage path planning with morphological skeleton maps.","authors":"Alexander James Becoy, Kseniia Khomenko, Luka Peternel, Raj Thilak Rajan","doi":"10.3389/frobt.2025.1601862","DOIUrl":"10.3389/frobt.2025.1601862","url":null,"abstract":"<p><p>This article proposes a novel method of coverage path planning for the purpose of scanning an unstructured environment autonomously. The method uses the morphological skeleton of a prior 2D navigation map via SLAM to generate a sequence of points of interest (POIs). This sequence is then ordered to create an optimal path based on the robot's current position. To control the high-level operation, a finite state machine (FSM) is used to switch between two modes: navigating toward a POI using Nav2 and scanning the local surroundings. We validate the method in a leveled, indoor, obstacle-free, non-convex environment, evaluating time efficiency and reachability over five trials. The map reader and path planner can quickly process maps of widths and heights ranging between [196,225] <math><mrow><mi>p</mi> <mi>i</mi> <mi>x</mi> <mi>e</mi> <mi>l</mi> <mi>s</mi></mrow> </math> and [185,231] <math><mrow><mi>p</mi> <mi>i</mi> <mi>x</mi> <mi>e</mi> <mi>l</mi> <mi>s</mi></mrow> </math> in <math><mrow><mn>2.52</mn> <mtext> </mtext> <mi>m</mi> <mi>s</mi></mrow> </math> and <math><mrow><mn>1.7</mn> <mtext> </mtext> <mi>m</mi> <mi>s</mi></mrow> </math> , respectively. Their computation time increases with <math><mrow><mn>22.0</mn> <mtext> </mtext> <mi>n</mi> <mi>s</mi> <mo>/</mo> <mi>p</mi> <mi>i</mi> <mi>x</mi> <mi>e</mi> <mi>l</mi></mrow> </math> and 8.17 μs/pixel, respectively. The robot managed to reach 86.5% of all waypoints across the five runs. The proposed method suffers from drift occurring in the 2D navigation map.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1601862"},"PeriodicalIF":3.0,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875987","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-robot collaborative manipulation framework for dynamic and obstacle-dense environments: integration of deep learning for real-time task execution.","authors":"Afnan Ahmed Adil, Saber Sakhrieh, Jinane Mounsef, Noel Maalouf","doi":"10.3389/frobt.2025.1585544","DOIUrl":"10.3389/frobt.2025.1585544","url":null,"abstract":"<p><p>This paper presents a multi-robot collaborative manipulation framework, implemented in the Gazebo simulation environment, designed to enable the execution of autonomous tasks by mobile manipulators in dynamic environments and dense obstacles. The system consists of multiple mobile robot platforms, each equipped with a robotic manipulator, a simulated RGB-D camera, and a 2D LiDAR sensor on the mobile base, facilitating task coordination, object detection, and advanced collision avoidance within a simulated warehouse setting. A leader-follower architecture governs collaboration, allowing for the dynamic formation of teams to tackle tasks requiring combined effort, such as transporting heavy objects. Task allocation and control are achieved through a centralized control structure architecture in which the leader robot coordinates subordinate units based on high-level task assignments. The framework incorporates deep learning-based object detection (YOLOv2) to identify target objects using a simulated RGB-D camera mounted on the manipulator's end-effector. Path planning is achieved through a sampling-based algorithm that is integrated with the LiDAR data to facilitate precise obstacle avoidance and localization. It also provides real-time path rerouting for safe navigation when dynamically moving obstacles, such as humans or other entities, intersect planned paths. This functionality ensures uninterrupted task execution and enhances safety in human-robot shared spaces. High-level task scheduling and control transitions are managed using MATLAB and Stateflow logic, while ROS facilitates data communication between MATLAB, Simulink, and Gazebo. This multirobot architecture is adaptable, allowing configuration of team size for collaborative tasks based on load requirements and environmental complexity. By integrating computer vision and deep learning for visual processing, and YOLOv2 for object detection, the system efficiently identifies, picks, and transports objects to designated locations, demonstrating the scalability of multi-robot framework for future applications in logistics automation, collaborative manufacturing, and dynamic human-robot interaction scenarios.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1585544"},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12343253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849390","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 relevance model of human sparse communication in cooperation.","authors":"Kaiwen Jiang, Boxuan Jiang, Anahita Sadaghdar, Rebekah Limb, Tao Gao","doi":"10.3389/frobt.2025.1512099","DOIUrl":"10.3389/frobt.2025.1512099","url":null,"abstract":"<p><p>Human real-time communication creates a limitation on the flow of information, which requires the transfer of carefully chosen and condensed data in various situations. We introduce a model that explains how humans choose information for communication by utilizing the concept of \"relevance\" derived from decision-making theory and Theory of Mind (ToM). We evaluated the model by conducting experiments where human participants and an artificial intelligence (AI) agent assist each other to avoid multiple traps in a simulated navigation task. The relevance model accurately depicts how humans choose which trap to communicate. It also outperforms GPT-4, which participates in the same task by responding to prompts that describe the game settings and rules. Furthermore, we demonstrated that when humans received assisting information from an AI agent, they achieved a much higher performance and gave higher ratings to the AI when it utilized the relevance model compared to a heuristic model. Together, these findings provide compelling evidence that a relevance model rooted in decision theory and ToM can effectively capture the sparse and spontaneous nature of human communication.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1512099"},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12344457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849391","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":"Robot System Assistant (RoSA): evaluation of touch and speech input modalities for on-site HRI and telerobotics.","authors":"Dominykas Strazdas, Matthias Busch, Rijin Shaji, Ingo Siegert, Ayoub Al-Hamadi","doi":"10.3389/frobt.2025.1561188","DOIUrl":"10.3389/frobt.2025.1561188","url":null,"abstract":"<p><p>Future work scenarios envision increased collaboration between humans and robots, emphasizing the need for versatile interaction modalities. Robotic systems can support various use cases, including on-site operations and telerobotics. This study investigates a hybrid interaction model in which a single user engages with the same robot both on-site and remotely. Specifically, the Robot System Assistant (RoSA) framework is evaluated to assess the effectiveness of touch and speech input modalities in these contexts. The participants interact with two robots, <i>Rosa</i> and <i>Ari</i>, utilizing both input modalities. The results reveal that touch input excels in precision and task efficiency, while speech input is preferred for its intuitive and natural interaction flow. These findings contribute to understanding the complementary roles of touch and speech in hybrid systems and their potential for future telerobotic applications.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1561188"},"PeriodicalIF":3.0,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12343565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849392","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":"Parametric modeling of deformable linear objects for robotic outfitting and maintenance of space systems.","authors":"Amy Quartaro, Joshua Moser, John Cooper, Erik Komendera","doi":"10.3389/frobt.2025.1565837","DOIUrl":"10.3389/frobt.2025.1565837","url":null,"abstract":"<p><p>Outfitting and maintenance are important to an in-space architecture consisting of long duration missions. During such missions, crew is not continuously present; robotic agents become essential to the construction, maintenance, and servicing of complicated space assets, requiring some degree of autonomy to plan and execute tasks. There has been significant research into manipulation planning for rigid elements for in-space assembly and servicing, but flexible electrical cables, which fall under the domain of Deformable Linear Objects (DLOs), have not received such attention despite being critical components of powered space systems. Cables often have a non-zero bend equilibrium configuration, which the majority of DLO research does not consider. This article implements a model-based optimization approach to estimate cable configuration, where a design parameter of the model's discretization level enables trading model accuracy vs computational complexity. Observed 2D cable configurations are used to improve the model via parameter estimation. The parameter estimation is validated through comparing predicted configurations based on estimated parameters to that of a real cable. The incorporation of parameter estimation to the cable model is shown to reduce prediction errors by an order of magnitude. The results of this work demonstrate some of the challenges present with robotic cable manipulation, exploring the complexities of outfitting and maintenance operations of in-space facilities, and puts forth a method for reducing the size of the state space of a cable payload while accounting for non-zero equilibrium configurations.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1565837"},"PeriodicalIF":3.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12340519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838248","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":"Responsible and adaptive robots in care home settings: an implementation framework analysis of a workshop with public and professionals.","authors":"Andriana Boudouraki, Maria Waheed, Rafael Mestre, Aleksandra Landowska, Athina Georgara, Jayati Deshmukh, Lokesh Singh, Ayodeji O Abioye, Nguyen Tan Viet Tuyen, Yi Dong, Shuang Ao, Dominic Price, Joel Fischer, Aislinn Gomez Bergin","doi":"10.3389/frobt.2025.1610329","DOIUrl":"10.3389/frobt.2025.1610329","url":null,"abstract":"<p><p>As populations grow, research looks to emerging adaptive technologies for the urgent challenge in providing suitable care for older adults. Drawing on implementation science, we conducted a holistic examination looking at broader, contextual factors relating to the acceptability of robotics and sensor technologies in care homes. We held a workshop that brought together members of the public and researchers with experience in care home, to try such technologies and discuss their application in different care home scenarios. Using the NASSS framework, we examine acceptability through the angles of technology, condition, adopters, value proposition, organisation, wider context, and sustainability. While both groups of participants share concerns about the negative impacts of robotics on the quality of care, we also uncovered additional areas of further consideration relating to tensions between stakeholders and constraints around material resources, culture, processes and regulatory considerations.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1610329"},"PeriodicalIF":3.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12331501/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144817927","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":"Incentivising cooperation by judging a group's performance by its weakest member in neuroevolution and reinforcement learning.","authors":"Jory Schossau, Bamshad Shirmohammadi, Arend Hintze","doi":"10.3389/frobt.2025.1599676","DOIUrl":"10.3389/frobt.2025.1599676","url":null,"abstract":"<p><strong>Introduction: </strong>Autonomous agents increasingly interact within social domains such as customer service, transportation, and healthcare, often acting collectively on behalf of humans. In many of these scenarios, individually greedy strategies can diminish overall performance, exemplified by phenomena such as stop-and-go traffic congestion or network service disruptions due to competing interests. Thus, there is a growing need to develop decision-making strategies for autonomous agents that balance individual efficiency with group equitability.</p><p><strong>Methods: </strong>We propose a straightforward approach for rewarding groups of autonomous agents within evolutionary and reinforcement learning frameworks based explicitly on the performance of the weakest member of the group. Rather than optimizing each agent's individual rewards independently, we align incentives by using a \"weakest-link\" metric, thereby encouraging collective strategies that support equitable outcomes.</p><p><strong>Results: </strong>Our results demonstrate that this weakest-member reward system effectively promotes equitable behavior among autonomous agents. Agents evolve or learn to balance collective benefit with individual performance, resulting in fairer outcomes for the entire group. Notably, the introduced approach improves overall efficiency, as equitably-minded agents collectively achieve greater stability and higher individual outcomes than agents pursuing purely selfish strategies.</p><p><strong>Discussion: </strong>This methodology aligns closely with biological mechanisms observed in nature, specifically group-level selection and inclusive fitness theory. By tying the evolutionary and learning objectives to the group's weakest member, we mimic natural processes that favor cooperative and equitable behaviors. Our findings highlight the importance of incentive structures that consider the collective well-being to optimize both group fairness and individual agent success. Future research should explore how this reward framework generalizes across broader domains and more complex agent interactions.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1599676"},"PeriodicalIF":3.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12331510/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144817926","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":"Editorial: Merging symbolic and data-driven AI for robot autonomy.","authors":"Daniele Meli, Mohan Sridharan, Simona Perri, Nikos Katzouris","doi":"10.3389/frobt.2025.1662674","DOIUrl":"https://doi.org/10.3389/frobt.2025.1662674","url":null,"abstract":"","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1662674"},"PeriodicalIF":3.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12325050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144795850","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 hybrid elastic-hyperelastic approach for simulating soft tactile sensors.","authors":"Berith Atemoztli De la Cruz Sánchez, Jean-Philippe Roberge","doi":"10.3389/frobt.2025.1639524","DOIUrl":"10.3389/frobt.2025.1639524","url":null,"abstract":"<p><p>Efficient robotic grasping increasingly relies on artificial intelligence (AI) and tactile sensing technologies, which necessitate the acquisition of substantial data-a task that can often prove challenging. Consequently, the alternative of generating tactile data through precise and efficient simulations is becoming increasingly appealing. A significant challenge for simulating tactile sensors is balancing the trade-off between accuracy and processing time in simulation algorithms and models. To address this, we propose a hybrid approach that combines elastic and hyperelastic finite element simulations, complemented by convolutional neural networks (CNNs), to generate synthetic tactile maps of a soft capacitive tactile sensor. By leveraging a dataset of 53,400 real-world tactile maps, this methodology enables effective training, validation, and testing of each pipeline. This approach combines a fast elastic model for simple contact patches with a more detailed but slower hyperelastic model when greater precision is required. Our method automatically assesses contact patch complexity based on parameters associated with the object's mesh to determine the most appropriate modeling technique by still ensuring accurate deformation simulation. Tested on a dataset of 12 unseen objects, our approach achieves up to 97% Structural Similarity Index Measure (SSIM) for the hyperelastic model and 90% for the elastic model. This hybrid strategy enables an adaptive balance between simulation speed and accuracy, making it suitable for generating synthetic tactile data across tasks with varying precision demands and object geometrical complexities.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1639524"},"PeriodicalIF":3.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12321859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144790335","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}