Robotica最新文献_第9页

ROB volume 42 issue 3 Cover and Back matter ROB 第 42 卷第 3 期封面和封底

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-02-13 DOI: 10.1017/s0263574724000213

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ROB volume 42 issue 3 Cover and Back matter ROB 第 42 卷第 3 期封面和封底

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-02-13 DOI: 10.1017/s0263574724000213

引用次数: 0

Reinforcement learning-based motion control for snake robots in complex environments 基于强化学习的蛇形机器人在复杂环境中的运动控制

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-02-12 DOI: 10.1017/s0263574723001613

Dong Zhang, Renjie Ju, Zhengcai Cao

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IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles IFE-网络：用于自动驾驶水下航行器弱特征目标识别的改进型特征增强网络

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-02-08 DOI: 10.1017/s0263574724000195

Lei Cai, Bingyuan Zhang, Yuejun Li, Haojie Chai

{"title":"IFE-net: improved feature enhancement network for weak feature target recognition in autonomous underwater vehicles","authors":"Lei Cai, Bingyuan Zhang, Yuejun Li, Haojie Chai","doi":"10.1017/s0263574724000195","DOIUrl":"https://doi.org/10.1017/s0263574724000195","url":null,"abstract":"The recognizing underwater targets is a crucial component of autonomous underwater vehicle patrols and detection efforts. In the process of visual image recognition in real underwater environment, the spatial and semantic features of the target often appear to different degrees of loss, and the scarcity of specific types of underwater samples leads to unbalanced data on categories. This kind of problem makes the target features appear weak and seriously affects the accuracy of underwater target recognition. Traditional deep learning methods based on data and feature enhancement cannot achieve ideal recognition effect. Based on the above difficulties, this paper proposes an improved feature enhancement network for weak feature target recognition. Firstly, a multi-scale spatial and semantic feature enhancement module is constructed to extract the feature information of the extraction target accurately. Secondly, this paper solves the influence of target feature distortion on classification through multi-scale feature comparison of positive and negative samples. Finally, the Rank & Sort Loss function was used to train the depth target detection to solve the problem of recognition accuracy under highly unbalanced sample data. Experimental results show that the recognition accuracy of the proposed method is 2.28% and 3.84% higher than that of the existing algorithms in the recognition of underwater fuzzy and distorted target images, which demonstrates the effectiveness and superiority of the proposed method.","PeriodicalId":49593,"journal":{"name":"Robotica","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139761234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Design and analysis of a wall-climbing robot with passive compliant mechanisms to adapt variable curvatures walls 设计和分析带被动顺应机构的爬墙机器人，以适应不同曲率的墙壁

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-02-05 DOI: 10.1017/s026357472300173x

Yifeng Song, Zhenyu Yang, Yong Chang, Hui Yuan, Song Lin

引用次数: 0

Panoramic visual system for spherical mobile robots 球形移动机器人的全景视觉系统

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-02-05 DOI: 10.1017/s0263574724000043

Muhammad Affan Arif, Aibin Zhu, Han Mao, Yao Tu

{"title":"Panoramic visual system for spherical mobile robots","authors":"Muhammad Affan Arif, Aibin Zhu, Han Mao, Yao Tu","doi":"10.1017/s0263574724000043","DOIUrl":"https://doi.org/10.1017/s0263574724000043","url":null,"abstract":"Aimed at the challenges of wide-angle mobile robot visual perception for diverse field applications, we present the spherical robot visual system that uses a 360° field of view (FOV) for realizing real-time object detection. The spherical robot image acquisition system model is developed with optimal parameters, including camera spacing, camera axis angle, and the distance of the target image plane. Two 180<img data-mimesubtype=\"png\" data-type=\"\" src=\"https://static.cambridge.org/binary/version/id/urn:cambridge.org:id:binary:20240203031027772-0638:S0263574724000043:S0263574724000043_inline1.png\">$^{circ}$</img>-wide panoramic FOVs, front and rear view, are formed using four on-board cameras. The speed of the SURF algorithm is increased for feature extraction and matching. For seamless fusion of the images, an improved fade-in and fade-out algorithm is used, which not only improves the seam quality but also improves object detection performance. The speed of the dynamic image stitching is significantly enhanced by using a cache-based sequential image fusion method. On top of the acquired panoramic wide FOVs, the YOLO algorithm is used for real-time object detection. The panoramic visual system for the spherical robot is then tested in real time, which outputs panoramic views of the scene at an average frame rate of 21.69 fps and panoramic views with object detection at an average of 15.39 fps.","PeriodicalId":49593,"journal":{"name":"Robotica","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139690174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Design and control of a compliant robotic actuator with parallel spring-damping transmission 设计和控制带平行弹簧阻尼传动的顺应式机器人致动器

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-02-05 DOI: 10.1017/s0263574724000110

Peikang Yuan, Jianbin Liu, David T. Branson, Zhibin Song, Shuai Wu, Jian S. Dai, Rongjie Kang

引用次数: 0

Path planning for robots in preform weaving based on learning from demonstration 基于示范学习的预成型编织机器人路径规划

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-02-01 DOI: 10.1017/s0263574724000146

Zhuo Meng, Shuo Li, Yujing Zhang, Yize Sun

{"title":"Path planning for robots in preform weaving based on learning from demonstration","authors":"Zhuo Meng, Shuo Li, Yujing Zhang, Yize Sun","doi":"10.1017/s0263574724000146","DOIUrl":"https://doi.org/10.1017/s0263574724000146","url":null,"abstract":"A collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task. Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage. The results of different yarn storage examples show that the ELM-IRRT method can plan the yarn storage path within 2s–5s when the position of the mechanism changes in narrow spaces, avoiding tedious manual operations that program the robot movements, which is feasible and effective.","PeriodicalId":49593,"journal":{"name":"Robotica","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139657078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Editorial to the special issue on “Modeling and control of innovative robots” 为 "创新机器人的建模与控制 "特刊撰写社论

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-01-30 DOI: 10.1017/s0263574724000067

Andreas Müller, Mathias Brandstötter, L. Romdhane, Alessandro Gasparetto

引用次数: 0

SLAMB&MAI: a comprehensive methodology for SLAM benchmark and map accuracy improvement SLAMB&MAI：用于 SLAM 基准和地图精度改进的综合方法

IF 2.7 4区计算机科学

Robotica Pub Date : 2024-01-30 DOI: 10.1017/s0263574724000079

Shengshu Liu, Erhui Sun, Xin Dong

{"title":"SLAMB&MAI: a comprehensive methodology for SLAM benchmark and map accuracy improvement","authors":"Shengshu Liu, Erhui Sun, Xin Dong","doi":"10.1017/s0263574724000079","DOIUrl":"https://doi.org/10.1017/s0263574724000079","url":null,"abstract":"SLAM Benchmark plays a pivotal role in the field by providing a common ground for performance evaluation. In this paper, a novel methodology of simultaneous localization and mapping benchmark and map accuracy improvement (SLAMB&MAI) is introduced. It can objectively evaluate errors of localization and mapping, and further improve map accuracy by utilizing evaluation results as feedback. The proposed benchmark transforms all elements into a global frame and measures the errors between them. The comprehensiveness consists in the benchmark of both localization and mapping, and the objectivity consists in the consideration of the correlation between localization and mapping by the preservation of the original pose relations between all reference frames. The map accuracy improvement is realized by first obtaining the optimization that minimizes the errors between the estimated trajectory and ground truth trajectory and then applying it to the estimated map. The experimental results showed that the map accuracy can be improved by an average of 15%. The optimization that yields minimal localization errors is obtained by the proposed Centre Point Registration-Iterative Closest Point (CPR-ICP). This proposed Iterative Closest Point (ICP) variant pre-aligns two point clouds by their centroids and least square planes and then uses traditional ICP to minimize the error between them. The experimental results showed that CPR-ICP outperformed traditional ICP, especially in cases involving large-scale environments. To the extent of our knowledge, this is the first work that can not only objectively benchmark both localization and mapping but also revise the estimated map and increase its accuracy, which provides insights into the acquisition of ground truth map and robot navigation.","PeriodicalId":49593,"journal":{"name":"Robotica","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139579207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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