2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)最新文献_第10页

Additively Manufactured Primitive Plastic Phantom for Calibration of Low-Resolution Computed Tomography Cone Beam Scanner for Additive Creation of 3D Copies using Inverse Radon Transform 用于校正低分辨率计算机断层扫描锥形束扫描仪的增材制造原始塑料模体，用于使用逆氡变换进行3D副本的增材创建

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10011777

Valentin Ameres, Meriem Chetmi, Lucas Artmann, Tim C. Lueth

{"title":"Additively Manufactured Primitive Plastic Phantom for Calibration of Low-Resolution Computed Tomography Cone Beam Scanner for Additive Creation of 3D Copies using Inverse Radon Transform","authors":"Valentin Ameres, Meriem Chetmi, Lucas Artmann, Tim C. Lueth","doi":"10.1109/ROBIO55434.2022.10011777","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011777","url":null,"abstract":"Computed Tomography (CT) and 3D reconstruction contribute significantly to reverse engineering as well as to additive manufacturing. Utilizing CT scans, surface information as well as inner details of objects of interest can be recorded non-destructively. In this work, a low-resolution computed tomography cone beam (CBCT) scanner was used to scan, reconstruct and print plastic components in order to create 3D copies. Software based calibration using an additively manufactured two layer plastic phantom containing steel ball bearings was used to detect and correct geometrical alignment errors and improve reconstruction quality. A phantom was designed to be printed additively and assembled without the help of further tools, with an axial connection to the CBCT. Corrections were applied to the two-dimensional 300x300 pixel X-ray projections before reconstruction. A reconstructed volume of 212x212x212 voxels was achieved using either the inverse-Radon-Transformation-based Feldkamp Davis Krauss (FDK) or Simultaneous Algebraic Reconstruction Technique (SART) algorithm. In an experiment, a plastic phantom was fabricated and used for misalignment correction. Two reconstructions of uncorrected and corrected projections of a 30 mm plastic cube with center bore were subsequently compared to each other in terms of density. The cube reconstructed from corrected projections had higher voxel density values and sharper slices, showing the successful fabrication and use of the plastic phantom.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130600310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning 基于模拟到真实迁移学习的机器人精确抓取

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10011794

Lei Zhang, Kaixin Bai, Zhaopeng Chen, Yunlei Shi, Jianwei Zhang

{"title":"Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning","authors":"Lei Zhang, Kaixin Bai, Zhaopeng Chen, Yunlei Shi, Jianwei Zhang","doi":"10.1109/ROBIO55434.2022.10011794","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011794","url":null,"abstract":"Precise robotic grasping of several novel objects is a huge challenge in manufacturing, automation, and logistics. Most of the current methods for model-free grasping are disadvantaged by the sparse data in grasping datasets and by errors in sensor data and contact models. This study combines data generation and sim - to- real transfer learning in a grasping framework that reduces the sim-to-real gap and enables precise and reliable model-free grasping. A large-scale robotic grasping dataset with dense grasp labels is generated using domain randomization methods and a novel data augmentation method for deep learning-based robotic grasping to solve data sparse problem. We present an end-to-end robotic grasping network with a grasp optimizer. The grasp policies are trained with sim-to-real transfer learning. The presented results suggest that our grasping framework reduces the uncertainties in grasping datasets, sensor data, and contact models. In physical robotic experiments, our grasping framework grasped single known objects and novel complex-shaped household objects with a success rate of 90.91%. In a complex scenario with multi-objects robotic grasping, the success rate was 85.71%. The proposed grasping framework outperformed two state-of-the-art methods in both known and unknown object robotic grasping.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121652778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recognition of Degradation Scenarios for LiDAR SLAM Applications 激光雷达SLAM应用中退化场景的识别

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10011727

Chenglin Yang, Zihao Chai, Xiaoxiao Yang, Hanyang Zhuang, Ming Yang

{"title":"Recognition of Degradation Scenarios for LiDAR SLAM Applications","authors":"Chenglin Yang, Zihao Chai, Xiaoxiao Yang, Hanyang Zhuang, Ming Yang","doi":"10.1109/ROBIO55434.2022.10011727","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011727","url":null,"abstract":"The SLAM system, which uses 3D LiDAR as the only sensor, is prone to degradation when facing a scenario with sparse structure and fewer constraints. It cannot solve the robot pose based on limited LiDAR constraint information, which leads to the localization failure and mapping failure of the SLAM system. Due to the limitations of LiDAR, it is difficult to only rely on the point cloud data provided by LiDAR to solve the problem of localization and mapping of degraded scenarios. Currently, the mainstream is to provide additional information through multi-sensor fusion and other schemes to restrict and correct the system's attitude. In the multi-source fusion system, it is still essential to determine the information reliability of each sensor source in different directions. Hence, the recognition of the degradation scenario has significant research value. In this paper, three schemes, geometric information, constraint distur-bance, and residual disturbance, are designed to quantitatively identify the degradation state of the system and estimate the degradation direction. Through experimental verification, the proposed schemes have a favorable recognition effect in the degradation scenario of the simulation environment and real environment.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121142959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Experimental Study of Keypoint Descriptor Fusion 关键点描述子融合的实验研究

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10011825

Yaling Pan, Li He, Y. Guan, Hong Zhang

引用次数: 0

Target prediction and temporal localization of grasping action for vision-assisted prosthetic hand 视觉辅助假手抓取动作的目标预测与时间定位

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10011751

Xu Shi, Wei Xu, Weichao Guo, X. Sheng

{"title":"Target prediction and temporal localization of grasping action for vision-assisted prosthetic hand","authors":"Xu Shi, Wei Xu, Weichao Guo, X. Sheng","doi":"10.1109/ROBIO55434.2022.10011751","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011751","url":null,"abstract":"With the development of shared control technology for humanoid prosthetic hands, more and more research is focused on vision-based machine decision making. In this paper, we propose a miniaturized eye-in-hand target object prediction and action decision-making framework for the humanoid hand “approach-grasp” sequence. Our prediction system can simultaneously predict the target object and detect temporal localization of the grasp action. The system is divided into three main modules: feature logging, target filtering and grasp triggering. In this paper, the optimal configuration of the hyper-parameters designed in each module is performed experimentally. We also propose a prediction quality assessment method for “approach-grasp” behavior, including instance level, sequence level and action decision level. With the optimal hyper-parameter configuration, the predicting system perform averagely to 0.854 at instance prediction accuracy (IP), 0.643 at grasp action prediction accuracy (GP). It also has good predictive stability for most classes of objects with number of predicting changes (NPC) below 6.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122313511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Predictive Method for Site Selection in Aquaculture with a Robotic Platform 基于机器人平台的水产养殖选址预测方法

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10011913

Tong Shen, Tianqi Zhang, Kai Yuan, Kaiwen Xue, Huihuan Qian

引用次数: 0

Human-Aided Online Terrain Classification for Bipedal Robots Using Augmented Reality 基于增强现实的双足机器人人工辅助在线地形分类

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10011705

Zahraa Awad, Celine Chibani, Noel Maalouf, Imad H. Elhajjl

{"title":"Human-Aided Online Terrain Classification for Bipedal Robots Using Augmented Reality","authors":"Zahraa Awad, Celine Chibani, Noel Maalouf, Imad H. Elhajjl","doi":"10.1109/ROBIO55434.2022.10011705","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011705","url":null,"abstract":"This paper presents an online training system, enhanced with augmented reality, for improving real-time terrain classification by humanoid robots. The real-time terrain type prediction model relies on data acquired from four different sensors (force, position, current, and inertial) of the NAO humanoid robot. We compare the performance of Stochastic Gradient Descent, Passive Aggressive classifier, and Support Vector Machine in predicting the terrain type being traversed. Then, the models are trained online by manually inputting the correct terrain type being traversed to improve the accuracy of the predictions over time. An Augmented Reality (AR) user interface is designed to display the robot diagnostics and terrain type being predicted and obtain the user feedback to correct the terrain type when needed. This allows the user to improve the classification results and enhance the data collection process in the easiest way possible. The experimental results show that the Passive Aggressive classifier is the most successful among the three online classifiers with an accuracy of 81.4%.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126816055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MR-GMMExplore: Multi-Robot Exploration System in Unknown Environments based on Gaussian Mixture Model MR-GMMExplore:基于高斯混合模型的未知环境多机器人探索系统

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10011789

Yichun Wu, Qiuyi Gu, Jincheng Yu, Guangjun Ge, Jian Wang, Q. Liao, Chun Zhang, Yu Wang

{"title":"MR-GMMExplore: Multi-Robot Exploration System in Unknown Environments based on Gaussian Mixture Model","authors":"Yichun Wu, Qiuyi Gu, Jincheng Yu, Guangjun Ge, Jian Wang, Q. Liao, Chun Zhang, Yu Wang","doi":"10.1109/ROBIO55434.2022.10011789","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011789","url":null,"abstract":"Collaborative exploration in an unknown environ-ment is an essential task for mobile robotic systems. Without external positioning, multi-robot mapping methods have relied on the transfer of place descriptors and sensor data for relative pose estimation, which is not feasible in communication-limited environments. In addition, existing frontier-based exploration strategies are mostly designed for occupancy grid maps, thus failing to use surface information of obstacles in complex three-dimensional scenes. To address these limitations, we utilize Gaussian Mixture Model (GMM) as the map form for both mapping and exploration. We extend our previous mapping work to exploration setting by introducing MR-GMMExplore, a Multi-Robot GMM-based Exploration system in which robots transfer GMM submaps to reduce data transmission and perform exploration directly using the generated GMM map. Specifically, we propose a GMM spatial information extraction strategy that efficiently extracts obstacle probability information from GMM submaps. Then we present a goal selection method that allows robots to explore different areas, and a GMM-based local planner that realizes local planning using GMM maps instead of converting them into grid maps. Simulation results show that the transmission of GMM submaps reduces approximately 96% communication load compared with point clouds and our mean-based extraction strategy is 4 times faster than the traversal-based one. We also conduct comparative experiments to demonstrate the effectiveness of our approach in reducing backtracking paths and enhancing cooperation. MR-GMMExplore is published as an open-source ROS package at https://github.com/efc-robot/gmm_explore.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121504453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SmallRhex: A Fast and Highly-Mobile Hexapod Robot SmallRhex:一个快速和高度移动的六足机器人

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10012013

Wenhui Wang, Wujie Shi, Zerui Li, Weiheng Zhuang, Zheng Zhu, Zhenzhong Jia

{"title":"SmallRhex: A Fast and Highly-Mobile Hexapod Robot","authors":"Wenhui Wang, Wujie Shi, Zerui Li, Weiheng Zhuang, Zheng Zhu, Zhenzhong Jia","doi":"10.1109/ROBIO55434.2022.10012013","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10012013","url":null,"abstract":"Relying on the unique C-leg structure, RHex robots have good mobility and traffic ability when having relatively simple structures. Based on the existing RHex robots, taking into account the performance and cost, this design develops a small hexapod robot, named smallRhex, with low cost but strong performance. This paper mainly introduces the mechanical structure, robot gaits, simulation, and physical performance tests of smallRhex robots. The hardware is mainly based on the raspberry pie microcomputer and Robomaster motor and accessories. The high power density meets the dual requirements of performance and cost. Then cooperates with 3D printing and sheet metal and machined parts processing to complete the design of the mechanical structure and assembly of the robot. At the control level, raspberry pie directly controls the movement of 6 motors. The gait design includes basic motion gait, which includes straight walking and turning, and other gaits of complex motion: stair climbing, jumping, and high obstacle climbing. They are simulated in Webots. Finally, the performance test and the gait test of the robot are carried out. Then the gait design is further optimized, and the basic design of smallRhex robot is completed.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127576239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prospect of Robot Assisted Maxilla-Mandibula-Complex Reposition in Orthognathic Surgery 机器人辅助上颌复合体复位在正颌外科中的应用前景

2022 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2022-12-05 DOI: 10.1109/ROBIO55434.2022.10011845

Jie Liang, Qianqian Li, Xing Wang, Xiaojing Liu

{"title":"Prospect of Robot Assisted Maxilla-Mandibula-Complex Reposition in Orthognathic Surgery","authors":"Jie Liang, Qianqian Li, Xing Wang, Xiaojing Liu","doi":"10.1109/ROBIO55434.2022.10011845","DOIUrl":"https://doi.org/10.1109/ROBIO55434.2022.10011845","url":null,"abstract":"This paper investigates the feasibility and accuracy of robot assisted maxilla-mandibula-complex (MMC) reposition in orthognathic surgery. A robot system was established by an optical motion capture system and a universal robotic arm. Computer assisted surgical simulation (CASS), image guiding and robotic control software modules were developed according to the specific requirements for orthognathic surgery. The operation work flow includes data acquisition, virtual simulation, registration, osteotomy, robotic assisting bone segments reposition and fixation. The reposition and holding accuracy was tested on skull models. Optical scanner was used to acquire the intraoperative skull morphologies before and after the fixation. A postoperative CT scan was conducted when the fixation is completed. The virtual skull, intraoperative scan data and postoperative CT scan image were superimposed and compared. Error was defined as root mean square (RMS) of MMC on different images. The positioning accuracy was calculated by RMS between surface scan data before fixation and the virtual design skull. The holding accuracy was calculated by RMS between the surface scan before and after fixation. A validation test was conducted on five skull models. The mean accuracy of robotic assisting MMC reposition was 0.75 ±0.69 mm while the mean holding accuracy during the fixation procedure was 1.56±1.2mm. The accuracy of robot assisted MMC reposition was clinical feasible. The holding accuracy during fixation procedure is less satisfactory than that of repositioning. Further investigation is needed to improve the holding solidity of the robotic manipulator.","PeriodicalId":151112,"journal":{"name":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128933186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0