IEEE Robotics and Automation Letters最新文献_第10页

Efficient Guidance Control of Large-Scale Drone Swarms Using Implicitly Informed Agents 基于隐式智能体的大规模无人机群制导控制

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-01 DOI: 10.1109/LRA.2025.3604701

Guangpeng Hu;Zhiwei Zhang;Zhaohui Song;Lifu Zhang;Sirun Xu;Hongjun Chu

{"title":"Efficient Guidance Control of Large-Scale Drone Swarms Using Implicitly Informed Agents","authors":"Guangpeng Hu;Zhiwei Zhang;Zhaohui Song;Lifu Zhang;Sirun Xu;Hongjun Chu","doi":"10.1109/LRA.2025.3604701","DOIUrl":"https://doi.org/10.1109/LRA.2025.3604701","url":null,"abstract":"Guidance control is crucial for the effective operation of distributed aerial swarm systems. Despite significant advancements, developing effective guidance control techniques for large-scale drone swarms remains a considerable challenge, especially when dealing with scenarios involving implicitly informed agents. Traditional methods often lead to swarm fragmentation in non-uniform guidance scenarios. Inspired by the intelligent swarming behavior of starlings, we propose a novel resilient guidance control model for drone swarms. This model employs a stochastic transition strategy for interaction modes based on a Markov decision process, establishing a swarm resilience mechanism that dynamically couples swarm cohesion with individual integration behaviors. This mechanism enables informed agents to guide individuals beyond their immediate topological interaction range effectively. Furthermore, the guidance control problem is formulated as a multi-objective optimization problem, which balances system flexibility and motion consistency through an adaptive optimization algorithm. Extensive simulations demonstrate that the model can guide a 1000-drone swarm to execute complex trajectories with a 99.95% success rate, even with only 5% informed agents. Real-world experiments using Crazyflie micro quadrotors further validate the model's practicality. This letter introduces a novel approach to large-scale drone swarm guidance and offers valuable insights for designing next-generation intelligent swarm systems.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10618-10625"},"PeriodicalIF":5.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed NMPC for Cooperative Aerial Manipulation of Cable-Suspended Loads 悬索载荷协同空中操纵的分布式NMPC

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-01 DOI: 10.1109/LRA.2025.3604703

Nicola De Carli;Riccardo Belletti;Emanuele Buzzurro;Andrea Testa;Giuseppe Notarstefano;Marco Tognon

引用次数: 0

Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underpowered Systems Operating in Uncertain Ocean Currents 自主农场海藻生长最大化：在不确定洋流中运行的动力不足系统的动态规划方法

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-01 DOI: 10.1109/LRA.2025.3604727

Matthias Killer;Marius Wiggert;Hanna Krasowski;Manan Doshi;Pierre F.J. Lermusiaux;Claire J. Tomlin

{"title":"Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underpowered Systems Operating in Uncertain Ocean Currents","authors":"Matthias Killer;Marius Wiggert;Hanna Krasowski;Manan Doshi;Pierre F.J. Lermusiaux;Claire J. Tomlin","doi":"10.1109/LRA.2025.3604727","DOIUrl":"https://doi.org/10.1109/LRA.2025.3604727","url":null,"abstract":"Seaweed biomass presents a substantial opportunity for climate mitigation, yet to realize its potential, farming must be expanded to the vast open oceans. However, in the open ocean neither anchored farming nor floating farms with powerful engines are economically viable. Thus, a potential solution are farms that operate by <italic>going with the flow</i>, utilizing minimal propulsion to strategically leverage beneficial ocean currents. In this work, we focus on low-power autonomous seaweed farms and design controllers that maximize seaweed growth by taking advantage of ocean currents. We first introduce a Dynamic Programming (DP) formulation to solve for the growth-optimal value function when the true currents are known. However, in reality only short-term imperfect forecasts with increasing uncertainty are available. Hence, we present three additional extensions. Firstly, we use frequent replanning to mitigate forecast errors. Second, to optimize for long-term growth, we extend the value function beyond the forecast horizon by estimating the expected future growth based on seasonal average currents. Lastly, we introduce a discounted finite-time DP formulation to account for the increasing uncertainty in future ocean current estimates. We empirically evaluate our approach with 30-day simulations of farms in realistic ocean conditions. Our method achieves 95.8% of the best possible growth using only 5-day forecasts. This demonstrates that low-power propulsion is a promising method to operate autonomous seaweed farms in real-world conditions.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10745-10752"},"PeriodicalIF":5.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensorless Force Imbalance Prediction Based on Visual Information for Robotic Ultrasound Scanning 基于视觉信息的机器人超声扫描无传感器力不平衡预测

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-01 DOI: 10.1109/LRA.2025.3604730

Qian Liu;Eunbin Choi;Anika Tabassum Sejuty;Suhyun Park

{"title":"Sensorless Force Imbalance Prediction Based on Visual Information for Robotic Ultrasound Scanning","authors":"Qian Liu;Eunbin Choi;Anika Tabassum Sejuty;Suhyun Park","doi":"10.1109/LRA.2025.3604730","DOIUrl":"https://doi.org/10.1109/LRA.2025.3604730","url":null,"abstract":"During robotic ultrasound examinations, maintaining pressure and angle control over the ultrasound probe is crucial for obtaining consistent images for an accurate diagnosis. Although force and torque sensors are commonly used for contact force monitoring, their accuracy can be influenced by sensor placement and system complexity. To address these issues, we propose a sensorless approach to estimate the contact force difference between the two sides of an ultrasound probe. Our proposed method utilizes a deep learning-based approach, specifically, a convolutional neural network–long short-term memory (CNN–LSTM) approach, that leverages sequential ultrasound images to estimate force differentials. Experiments were conducted using three tissue-mimicking phantoms and an in vivo human arm to train and evaluate the proposed approach. By varying the applied force difference on the phantoms and human arm, we achieved root mean squared error values of 0.501 and 0.553 N, respectively, in the contact force difference prediction. For performance assessment, we compared our proposed approach with a confidence map and various CNN–LSTM-based methods and demonstrated that our approach outperforms other approaches in terms of accuracy. The results indicate that our proposed method is effective for probe imbalance prediction without relying on physical sensors at inference time and can be deployed to control probes during robotic ultrasound examinations. Therefore, our sensorless approach offers a promising solution for more consistent and reliable robotic ultrasound scanning.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10594-10601"},"PeriodicalIF":5.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain-Inspired Visual Topometric Localization via Roadnetwork-Constraint Hidden Markov Model 基于路网约束隐马尔可夫模型的脑启发视觉拓扑定位

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-01 DOI: 10.1109/LRA.2025.3604706

Jinyu Li;Taiping Zeng;Bailu Si

{"title":"Brain-Inspired Visual Topometric Localization via Roadnetwork-Constraint Hidden Markov Model","authors":"Jinyu Li;Taiping Zeng;Bailu Si","doi":"10.1109/LRA.2025.3604706","DOIUrl":"https://doi.org/10.1109/LRA.2025.3604706","url":null,"abstract":"Accurate localization in GPS-denied environments remains a critical challenge for autonomous robot navigation. Animals exhibit remarkable navigational abilities in complex, dynamic environments by relying on mental cognitive maps. Inspired by neural representations such as head direction cells and grid cells, numerous robotic cognitive mapping systems can efficiently cover large areas; however, they often lack the precise metric information required for accurate localization. To address this challenge, we propose a neurodynamically driven monocular visual topometric localization approach based on road network constraints. We introduce the Roadnetwork-Constraint Hidden Markov Model (RC-HMM) to enhance the semi-metric map by incorporating road network constraints, forming a coherent topometric map that maintains vertex relationships and improves localization accuracy. Experimental results in the CARLA Town07 environment demonstrate the remarkable efficiency of our topometric cognitive map. Compared to the semi-metric map, our approach achieves a 95% reduction in Absolute Pose Error (APE) and an 81% reduction in Relative Pose Error (RPE). Compared to binocular ORB-SLAM3, our monocular approach reduces CPU usage by 96.7% and map storage by 77.7%, with an APE of 3.6 m and RPE of 1.4 m — closely matching ORB-SLAM3’s 3.86 m APE and 0.96 m RPE. Furthermore, by leveraging neurodynamics of grid cells and head direction cells, our monocular topometric localization robustly delivers the localization accuracy of 3.86 meters, comparable to binocular ORB-SLAM3. This approach integrates road network metrics into topological maps, enhancing brain-inspired navigation with topometric maps in complex environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 11","pages":"11062-11069"},"PeriodicalIF":5.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Actuation in Magnetic Soft Actuators: An Inverse Design Framework Incorporating Internal Magnetic Coupling 增强磁性软执行器的致动性：一种结合内部磁耦合的逆设计框架

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-01 DOI: 10.1109/LRA.2025.3604707

Zhaoda Du;Arjun Sharma;Sahibzada Shahroze Umar;Xiaolong Liu

{"title":"Enhancing Actuation in Magnetic Soft Actuators: An Inverse Design Framework Incorporating Internal Magnetic Coupling","authors":"Zhaoda Du;Arjun Sharma;Sahibzada Shahroze Umar;Xiaolong Liu","doi":"10.1109/LRA.2025.3604707","DOIUrl":"https://doi.org/10.1109/LRA.2025.3604707","url":null,"abstract":"This letter investigates the role of internal magnetic interactions in magnetic soft actuators and introduces a novel framework for their design and optimization. Magnetic soft actuators, composed of soft composites embedded with magnetic particles, enable untethered deformation and actuation under external magnetic fields and hold great potential for robotic applications such as minimally invasive surgery and intervention. Existing design approaches primarily address external field effects and mechanical resistance, often neglecting the contributions of internal magnetic interactions, which are critical to actuator performance. To address this gap, we develop an inverse design methodology that integrates material properties, actuator geometry, and internal magnetic dynamics. By analyzing the interplay between particle volumetric fractions, magnetic moment strengths, and intrinsic properties such as saturation magnetization and coercivity, we propose that internal magnetic coupling can significantly enhance deformation. Rotating-square magnetic metastructures are employed to evaluate actuation behaviors, comparing performance with and without internal coupling. Our findings demonstrate that incorporating internal magnetic interactions into the design process can lead to up to a 36–49% increase in deformation. In addition, the inverse design framework accurately predicted actuator performance, with experimental measurements of relaxed and actuated state deformations matching simulation results within 10%. This study contributes to the field by 1) quantifying the role of internal magnetic interactions in enhancing actuator performance, 2) developing a fabrication process that reliably produces a magnetic soft composite with targeted remanence and Young's modulus, and 3) introducing an optimization framework for predictive actuator design.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10846-10853"},"PeriodicalIF":5.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Miniature Multihole Airflow Sensor for Lightweight Aircraft Over Wide Speed and Angular Range 轻型飞机大速度和大角度范围微型多孔气流传感器

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-01 DOI: 10.1109/LRA.2025.3604704

Lukas Stuber;Simon Luis Jeger;Raphael Zufferey;Dario Floreano

{"title":"Miniature Multihole Airflow Sensor for Lightweight Aircraft Over Wide Speed and Angular Range","authors":"Lukas Stuber;Simon Luis Jeger;Raphael Zufferey;Dario Floreano","doi":"10.1109/LRA.2025.3604704","DOIUrl":"https://doi.org/10.1109/LRA.2025.3604704","url":null,"abstract":"An aircraft's airspeed, angle of attack, and angle of side slip are crucial to its safety, especially when flying close to the stall regime. Various solutions exist, including pitot tubes, angular vanes, and multihole pressure probes. However, current sensors are either too heavy (<inline-formula><tex-math>$>$</tex-math></inline-formula> 30g) or require large airspeeds (<inline-formula><tex-math>$>$</tex-math></inline-formula>20 m/s), making them unsuitable for small uncrewed aerial vehicles. We propose a novel multihole pressure probe, integrating sensing electronics in a single-component structure, resulting in a mechanically robust and lightweight sensor (9 g), which we released to the public domain. Since there is no consensus on two critical design parameters, tip shape (conical vs spherical) and hole spacing (distance between holes), we provide a study on measurement accuracy and noise generation using wind tunnel experiments. The sensor is calibrated using a multivariate polynomial regression model over an airspeed range of 3-27 m/s and an angle of attack/sideslip range of <inline-formula><tex-math>$pm 35^circ$</tex-math></inline-formula>, achieving a mean absolute error of 0.44 m/s and 0.16<inline-formula><tex-math>$^circ$</tex-math></inline-formula>. Finally, we validated the sensor in outdoor flights near the stall regime. Our probe enabled accurate estimations of airspeed, angle of attack and sideslip during different acrobatic manoeuvres. Due to its size and weight, this sensor will enable safe flight for lightweight, uncrewed aerial vehicles flying at low speeds close to the stall regime.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10722-10728"},"PeriodicalIF":5.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RUSH: Rapid UAV Spatial Hierarchical Exploration via Regional Viewpoint Generation for Large-Scale Environments RUSH：大尺度环境下基于区域视点生成的快速无人机空间分层探索

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-09-01 DOI: 10.1109/LRA.2025.3604699

Ningbo Bu;Gen Xu;Hao Zheng;Xuehang Wei;Wenshi Chen;Li Lv;Xiaolu Zhang;Jiangjian Xiao;Zhiqiang Li

{"title":"RUSH: Rapid UAV Spatial Hierarchical Exploration via Regional Viewpoint Generation for Large-Scale Environments","authors":"Ningbo Bu;Gen Xu;Hao Zheng;Xuehang Wei;Wenshi Chen;Li Lv;Xiaolu Zhang;Jiangjian Xiao;Zhiqiang Li","doi":"10.1109/LRA.2025.3604699","DOIUrl":"https://doi.org/10.1109/LRA.2025.3604699","url":null,"abstract":"Exploring large-scale environments quickly and autonomously using uncrewed aerial vehicles (UAVs) remains a challenge. Two major issues, long-distance back-tracking and the UAV's low-velocity flight, significantly hinder exploration efficiency. To tackle this problem, we propose a spatial hierarchical exploration method combining rapid regional viewpoint generation. This involves dividing the exploration space into subregions using an online hgrid spatial decomposition, and determining the order of exploration for these subregions by solving a non-closed traveling salesman problem. Optimal viewpoints are chosen from these subregions based on a global loss function related to frontiers. By dividing the space into subregions and optimizing the path globally, we can reduce the UAV's back-tracking distance. Additionally, the selected optimal viewpoints allow UAVs to make smaller turns, avoid obstacles, and achieve better coverage, which helps decrease the occurrence of low-velocity movements and back-tracking. We also incorporate a velocity loss constrain to improve local trajectories, ensuring high-velocity flight. Our proposed method has been analyzed and validated through simulations and real-world tests, showing improved exploration efficiency compared to several leading methods, particularly in large-scale environments.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10698-10705"},"PeriodicalIF":5.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Robotics and Automation Society Publication Information IEEE机器人与自动化协会出版信息

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-08-22 DOI: 10.1109/LRA.2025.3598965

引用次数: 0

IEEE Robotics and Automation Society Information IEEE机器人与自动化学会信息

IF 5.3 2区计算机科学

IEEE Robotics and Automation Letters Pub Date : 2025-08-22 DOI: 10.1109/LRA.2025.3598967

引用次数: 0