Robotics and Autonomous Systems最新文献_第9页

Robust super-twisting-based disturbance observer for autonomous underwater vehicles: Design, stability analysis, and real-time experiments 用于自主潜水器的基于超扭曲的鲁棒扰动观测器：设计、稳定性分析和实时实验

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-13 DOI: 10.1016/j.robot.2024.104859

Jesus Guerrero , Ahmed Chemori , Vincent Creuze , Jorge Torres

引用次数: 0

Learning latent causal factors from the intricate sensor feedback of contact-rich robotic assembly tasks 从接触丰富的机器人装配任务的复杂传感器反馈中学习潜在因果因素

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-09 DOI: 10.1016/j.robot.2024.104832

Yurou Chen , Jiyang Yu , Zhenyang Lin , Liancheng Shen , Zhiyong Liu

{"title":"Learning latent causal factors from the intricate sensor feedback of contact-rich robotic assembly tasks","authors":"Yurou Chen , Jiyang Yu , Zhenyang Lin , Liancheng Shen , Zhiyong Liu","doi":"10.1016/j.robot.2024.104832","DOIUrl":"10.1016/j.robot.2024.104832","url":null,"abstract":"<div><div>Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To enhance learning efficiency amidst challenging observations, this paper proposes introducing the concept of latent causal factors that drive sensor observations and hold paramount significance in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high-precision assembly policies from interacting with the environment. The algorithm’s performance is evaluated in simulated and real-world nut insertion environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing industrial challenges.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"183 ","pages":"Article 104832"},"PeriodicalIF":4.3,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658821","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

A sensorless approach for cable failure detection and identification in cable-driven parallel robots 用于电缆驱动并联机器人电缆故障检测和识别的无传感器方法

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-09 DOI: 10.1016/j.robot.2024.104855

Giovanni Boschetti , Riccardo Minto

引用次数: 0

GPS-free autonomous navigation in cluttered tree rows with deep semantic segmentation 利用深度语义分割技术在杂乱树行中实现无 GPS 自主导航

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-11-08 DOI: 10.1016/j.robot.2024.104854

Alessandro Navone, Mauro Martini, Marco Ambrosio, Andrea Ostuni, Simone Angarano, Marcello Chiaberge

{"title":"GPS-free autonomous navigation in cluttered tree rows with deep semantic segmentation","authors":"Alessandro Navone, Mauro Martini, Marco Ambrosio, Andrea Ostuni, Simone Angarano, Marcello Chiaberge","doi":"10.1016/j.robot.2024.104854","DOIUrl":"10.1016/j.robot.2024.104854","url":null,"abstract":"<div><div>Segmentation-based autonomous navigation has recently been presented as an appealing approach to guiding robotic platforms through crop rows without requiring perfect GPS localization. Nevertheless, current techniques are restricted to situations where the distinct separation between the plants and the sky allows for the identification of the row’s center. However, tall, dense vegetation, such as high tree rows and orchards, is the primary cause of GPS signal blockage. In this study, we increase the overall robustness and adaptability of the control algorithm by extending the segmentation-based robotic guiding to those cases where canopies and branches occlude the sky and prevent the utilization of GPS and earlier approaches. An efficient Deep Neural Network architecture has been used to address semantic segmentation, performing the training with synthetic data only. Numerous vineyards and tree fields have undergone extensive testing in both simulation and real world to show the solution’s competitive benefits. The system achieved unseen results in orchards, with a Mean Average Error smaller than 9% of the maximum width of each row, improving state-of-the-art algorithms by disclosing new scenarios such as close canopy crops. The official code can be found at: <span><span>https://github.com/PIC4SeR/SegMinNavigation.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"183 ","pages":"Article 104854"},"PeriodicalIF":4.3,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust trajectory tracking for omnidirectional robots by means of anti-peaking linear active disturbance rejection 利用反发音线性主动干扰抑制技术实现全向机器人的鲁棒轨迹跟踪

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-10-31 DOI: 10.1016/j.robot.2024.104842

Mario Ramírez-Neria , Rafal Madonski , Eduardo Gamaliel Hernández-Martínez , Norma Lozada-Castillo , Guillermo Fernández-Anaya , Alberto Luviano-Juárez

{"title":"Robust trajectory tracking for omnidirectional robots by means of anti-peaking linear active disturbance rejection","authors":"Mario Ramírez-Neria , Rafal Madonski , Eduardo Gamaliel Hernández-Martínez , Norma Lozada-Castillo , Guillermo Fernández-Anaya , Alberto Luviano-Juárez","doi":"10.1016/j.robot.2024.104842","DOIUrl":"10.1016/j.robot.2024.104842","url":null,"abstract":"<div><div>This article presents a Linear Active Disturbance Rejection scheme for the robust trajectory tracking control of an Omnidirectional robot, including an additional saturation element in the control design to improve the transient closed-loop response by including a saturation-input strategy in the Extended State Observer design, mitigating the possible arising peaking phenomenon. In addition, the controller is implemented in the kinematic model of the robotic system, assuming as the available information the position and orientation measurement and concerning the system structure, it is just known the order of the system and the control gain matrix as well. A wide set of laboratory experiments, including a comparison with a standard ADRC (<em>i.e</em>. without the proposed anti-peaking mechanism) and a PI-based control including an anti-peaking proposal, in the presence of different disturbance elements in the terrain of smooth and abrupt nature is carried out to formulate a comprehensive assessment of the proposal which validate the practical advantages of the proposal in robust trajectory tracking of the kind of robots.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"183 ","pages":"Article 104842"},"PeriodicalIF":4.3,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586364","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

YOLO with feature enhancement and its application in intelligent assembly 增强功能的 YOLO 及其在智能装配中的应用

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-10-30 DOI: 10.1016/j.robot.2024.104844

Fenglei Zheng , Aijun Yin , Chuande Zhou

引用次数: 0

Local obstacle avoidance control for multi-axle and multi-steering-mode wheeled robot based on window-zone division strategy 基于窗区划分策略的多轴和多转向模式轮式机器人局部避障控制

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-10-28 DOI: 10.1016/j.robot.2024.104843

Yongqiang Zhu , Junru Zhu , Pingxia Zhang

{"title":"Local obstacle avoidance control for multi-axle and multi-steering-mode wheeled robot based on window-zone division strategy","authors":"Yongqiang Zhu , Junru Zhu , Pingxia Zhang","doi":"10.1016/j.robot.2024.104843","DOIUrl":"10.1016/j.robot.2024.104843","url":null,"abstract":"<div><div>Due to the length of the body, multiple number of wheels and the complexity of controlling, it is difficult for a multi-axle wheeled robot to avoid obstacles autonomously in narrow space. To solve this problem, this article presents window-zone division and gap-seeking strategies for local obstacle avoidance of a multi-axle multi-steering-mode all-wheel-steering wheeled robot. Firstly, according to the influence degree of lidar points on the robot, combining with the human driving characteristics of avoiding obstacles, a window-zone division strategy is proposed. The lidar points are selected and divided according to the degree of emergency. By eliminating irrelevant points, the work of obstacle avoidance calculation is reduced. Thus, this increases the response speed of obstacle avoidance. Based on this, the robot uses a multi-steering-mode to avoid emergency obstacle. Secondly, the gap-seeking theory of normal obstacle avoidance is proposed. It can seek the passable gap among the surrounding lidar points according to the prediction of the robot's driving trajectory corresponding to different steering angles. Thirdly, the on-board control system and the upper computer program of the robot were designed. Thereafter a multi-steering-mode algorithm was designed based on the front and rear wheel steering angles and speed, as well as the travel trajectory forecast-drawing module. Finally, the proposed methods have been implemented on a five-axle all-wheel steering wheeled robot. Some obstacle avoidance experiments are carried out with S-shaped, Z-Shaped, U-Shaped, and Random obstacle distribution. The results show that the proposed strategy can finish all obstacle avoidance successfully.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"183 ","pages":"Article 104843"},"PeriodicalIF":4.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572217","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

Fractional-order sliding mode control of manipulator combined with disturbance and state observer 与干扰和状态观测器相结合的机械手分数阶滑动模式控制

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-10-28 DOI: 10.1016/j.robot.2024.104840

Jinghui Pan

{"title":"Fractional-order sliding mode control of manipulator combined with disturbance and state observer","authors":"Jinghui Pan","doi":"10.1016/j.robot.2024.104840","DOIUrl":"10.1016/j.robot.2024.104840","url":null,"abstract":"<div><div>A fractional-order sliding mode control (FSMC) method for a manipulator based on disturbance and state observers is proposed. First, a state estimator is designed that can estimate the velocity and acceleration, and only joint position feedback and the mathematical model of the manipulator are needed. The state estimator converges in finite time. Then, the disturbance observer is designed. By designing the nominal system model of the manipulator, a disturbance observation error is introduced into the closed-loop control so that the performance of the manipulator can track the nominal system. Finally, a sliding mode controller (SMC) based on the fractional differential operator theory is also designed. The value of the sliding mode variable in the derivation of the controller is composed of the fractional derivative of the trajectory tracking error of the manipulator, whereas the fractional differentiation operation uses integration in its realization, and the integration is a low-pass filter, thus, high-frequency noise is suppressed. In the experimental section, the method designed is compared with the conventional sliding mode, which further reveals the rapidity and control accuracy of FSMC.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"183 ","pages":"Article 104840"},"PeriodicalIF":4.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572216","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

An explainable deep learning model for automated classification and localization of microrobots by functionality using ultrasound images 利用超声波图像对微型机器人进行功能性自动分类和定位的可解释深度学习模型

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-10-25 DOI: 10.1016/j.robot.2024.104841

Ferhat Sadak

{"title":"An explainable deep learning model for automated classification and localization of microrobots by functionality using ultrasound images","authors":"Ferhat Sadak","doi":"10.1016/j.robot.2024.104841","DOIUrl":"10.1016/j.robot.2024.104841","url":null,"abstract":"<div><div>The rapid advancements of untethered microrobots offer exciting opportunities in fields such as targeted drug delivery and minimally invasive surgical procedures. However, several challenges remain, especially in achieving precise localization and classification of microrobots within living organisms using ultrasound (US) imaging. Current US-based detection algorithms often suffer from inaccurate visual feedback, causing positioning errors. This paper presents a novel explainable deep learning model for the localization and classification of eight different types of microrobots using US images. We introduce the Attention-Fused Bottleneck Module (AFBM), which enhances feature extraction and improves the performance of microrobot classification and localization tasks. Our model consistently outperforms baseline models such as YOLOR, YOLOv5-C3HB, YOLOv5-TBH, YOLOv5 m, and YOLOv7. The proposed model achieved mean Average Precision (mAP) of 0.861 and 0.909 at an IoU threshold of 0.95 which is 2% and 1.5% higher than the YOLOv5 m model in training and testing, respectively. Multi-thresh IoU analysis was performed at IoU thresholds of 0.6, 0.75, and 0.95, and demonstrated that the microrobot localization accuracy of our model is superior. A robustness analysis was performed based on high and low frequencies, gain, and speckle in our test data set, and our model demonstrated higher overall accuracy. UsingScore-CAM in our framework enhances interpretability, allowing for transparent insights into the model’s decision-making process. Our work signifies a notable advancement in microrobot classification and detection, with potential applications in real-world scenarios using the newly available USMicroMagset dataset for benchmarking.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"183 ","pages":"Article 104841"},"PeriodicalIF":4.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572215","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

Visual place recognition for aerial imagery: A survey 航空图像的视觉地点识别：一项调查

IF 4.3 2区计算机科学

Robotics and Autonomous Systems Pub Date : 2024-10-23 DOI: 10.1016/j.robot.2024.104837

Ivan Moskalenko , Anastasiia Kornilova , Gonzalo Ferrer

{"title":"Visual place recognition for aerial imagery: A survey","authors":"Ivan Moskalenko , Anastasiia Kornilova , Gonzalo Ferrer","doi":"10.1016/j.robot.2024.104837","DOIUrl":"10.1016/j.robot.2024.104837","url":null,"abstract":"<div><div>Aerial imagery and its direct application to visual localization is an essential problem for many Robotics and Computer Vision tasks. While Global Navigation Satellite Systems (GNSS) are the standard default solution for solving the aerial localization problem, it is subject to a number of limitations, such as, signal instability or solution unreliability that make this option not so desirable. Consequently, visual geolocalization is emerging as a viable alternative. However, adapting <em>Visual Place Recognition</em> (VPR) task to aerial imagery presents significant challenges, including weather variations and repetitive patterns. Current VPR reviews largely neglect the specific context of aerial data. This paper introduces a methodology tailored for evaluating VPR techniques specifically in the domain of aerial imagery, providing a comprehensive assessment of various methods and their performance. However, we not only compare various VPR methods, but also demonstrate the importance of selecting appropriate zoom and overlap levels when constructing map tiles to achieve maximum efficiency of VPR algorithms in the case of aerial imagery. The code is available on our GitHub repository — <span><span>https://github.com/prime-slam/aero-vloc</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49592,"journal":{"name":"Robotics and Autonomous Systems","volume":"183 ","pages":"Article 104837"},"PeriodicalIF":4.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553696","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