2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)最新文献_第7页

DenseXFormer: An Effective Occluded Human Instance Segmentation Network based on Density Map for Nursing Robot DenseXFormer：基于密度图的护理机器人有效遮挡人体实例分割网络

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354873

Sihao Qi, Jiexin Xie, Haitao Yan, Shijie Guo

{"title":"DenseXFormer: An Effective Occluded Human Instance Segmentation Network based on Density Map for Nursing Robot","authors":"Sihao Qi, Jiexin Xie, Haitao Yan, Shijie Guo","doi":"10.1109/ROBIO58561.2023.10354873","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354873","url":null,"abstract":"Human instance segmentation in occlusion scenarios remains a challenging task, especially in nursing scenarios, which hinders the development of nursing robots. Existing approaches are unable to focus the network’s attention on the occluded areas, which leads to unsatisfactory results. To address this issue, this paper proposes a novel and effective network based on density map in the instance segmentation task. Density map-based neural networks perform well in cases where human bodies occlude each other and can be trained without additional annotation information. Firstly, a density map generator (DMG) is introduced to generate accurate density information from feature maps computed by the backbone. Secondly, using density map enhances features in the density fusion module (DFM), which focuses the network on high-density areas as well as occluded areas. Additionally, to remedy the lack of occlusion-based dataset of nursing instance segmentation, a new dataset NSR-dataset is proposed. A large amount experiments on the public datasets (NSR and COCO-PersonOcc) show that the proposed method can be a powerful instrument for human instance segmentation. The improvements of efficiency with respect to accuracy are both prominent. The dataset can be got at https://github.com/Monkey0806/NSR-dataset.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"44 4","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139187378","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

Real-Time RGB-D Pedestrian Tracking for Mobile Robot 移动机器人的实时 RGB-D 行人跟踪

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354856

Wenhao Liu, Wanlei Li, Tao Wang, Jun He, Yunjiang Lou

{"title":"Real-Time RGB-D Pedestrian Tracking for Mobile Robot","authors":"Wenhao Liu, Wanlei Li, Tao Wang, Jun He, Yunjiang Lou","doi":"10.1109/ROBIO58561.2023.10354856","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354856","url":null,"abstract":"Pedestrian tracking is an important research direction in the field of mobile robotics. In order to complete tasks more efficiently and without hindering the original intention of pedestrians, mobile robots need to track pedestrians accurately in real time. In this paper, we propose a real-time RGB-D pedestrian tracking framework. First, we propose a pedestrian segmentation detection algorithm to detect pedestrians and obtain their two-dimensional positions. Second, due to limited computational resources and the rarity of missed detection for pedestrians, we use an nearest neighbor tracker for pedestrian tracking. To address the issue of inaccurate pedestrian localization, we use our detection algorithm to obtain the center of pedestrians from RGB images. By combining them with point clouds, the 2D coordinates of pedestrians are obtained. Our method enables accurate pedestrian tracking in the world coordinate, by adaptively fusing RGB images with their corresponding depth-based point clouds. Besides, our light-weight detection and tracking algorithm guarantee the real-time pedestrian tracking for realistic mobile robot applications. To validate the effectiveness and real-time performance of tracking algorithm, we conduct experiments using multiple pedestrian datasets of approximately half a minute in length, captured from two different perspectives. To validate the practicality and accuracy of the tracking algorithm in real-world scenarios, we extend our tracking algorithm to apply it to trajectory prediction.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"43 3","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139187405","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

Feature Fusion Module Based on Gate Mechanism for Object Detection 基于门机制的物体检测特征融合模块

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354575

Zepeng Sun, Dongyin Jin, Jian Deng, Mengyang Zhang, Zhenzhou Shao

引用次数: 0

Fog-based Distributed Camera Network system for Surveillance Applications 用于监控应用的基于雾的分布式摄像机网络系统

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10355008

Mvs Sakethram, Ps Saikrishna

引用次数: 0

Shape Analysis and Control of a Continuum Objects* 连续物体的形状分析和控制 *

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354616

Yuqiao Dai, Peng Li, Shilin Zhang, Yunhui Liu

引用次数: 0

Research on Horizontal Following Control of a Suspended Robot for Self-Momentum Targets 悬挂式机器人对自重目标的水平跟随控制研究

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354971

Dan Xiong, Yiyong Huang, Yanjie Yang, Hongwei Liu, Zhijie Jiang, Wei Han

{"title":"Research on Horizontal Following Control of a Suspended Robot for Self-Momentum Targets","authors":"Dan Xiong, Yiyong Huang, Yanjie Yang, Hongwei Liu, Zhijie Jiang, Wei Han","doi":"10.1109/ROBIO58561.2023.10354971","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354971","url":null,"abstract":"Micro/low gravity is one of the most prominent features of the outer space environment, and it significantly alters the force state and dynamics of spacecraft or astronauts compared to the Earth’s gravitational environment. It is crucial to simulate the micro/low gravity environment on the ground for astronaut training or spacecraft testing. The suspension method utilizes a pulley and sling mechanism to create a micro-low gravity environment. This method counteracts the gravitational force exerted by the object based on rope tension. The simulation effect greatly depends on the accuracy of the horizontal following system, which serves as the central subsystem of the suspension device. In this paper, we propose a dual-arm following system to solve the issue of horizontal following for self-momentum targets. In addition, we conduct research on adaptive inhibition technology for flexible rope swing, and coupling control between a robotic arm and a crane. Physical experiments are conducted on the robotic system to verify the effectiveness of the proposed approach.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"88 12","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139186736","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

Visual Servoing Using Cosine Similarity Metric 使用余弦相似度量进行视觉伺服

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354973

Wenbo Ning, Yecan Yin, Xiangfei Li, Huan Zhao, Yunfeng Fu, Han Ding

引用次数: 0

The Enhanced Network Swin-T by CNN on Flow Pattern Recognition for Two-phase Image Dataset with Low Similarity 低相似度两相图像数据集的增强型网络 Swin-T

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354651

Jinsong. Zhang, Deling. Wang, Huadan. Hao, Liangwen. Yan

引用次数: 0

Inertia Estimation of Quadruped Robot under Load and Its Walking Control Strategy in Urban Complex Terrain 负载下四足机器人的惯性估计及其在城市复杂地形中的行走控制策略

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354861

Qiang Fu, Muxuan Han, Yunjiang Lou, Ke Li, Zhiyuan Yu

{"title":"Inertia Estimation of Quadruped Robot under Load and Its Walking Control Strategy in Urban Complex Terrain","authors":"Qiang Fu, Muxuan Han, Yunjiang Lou, Ke Li, Zhiyuan Yu","doi":"10.1109/ROBIO58561.2023.10354861","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354861","url":null,"abstract":"When the quadruped robot is engaged in logistics transportation tasks, it encounters a challenge where the distribution of the center of mass (CoM) of the loaded items is not only random but also subject to time variations. Consequently, the robot becomes susceptible to non-zero resultant torques, which inevitably impact its body posture during the walking process. This paper proposes a method to estimate the CoM inertia using four one-dimensional force sensors and a walking control strategy for complex urban terrain. The inertia tensor and CoM of the load are first estimated, then the robot’s dynamics are compensated, and foothold adjustments are made for underactuated orientations to compensate for the extra moment generated by the CoM offset. For uneven terrain, the terrain estimator and event-based gait are used to adjust the robot’s gait to reduce the impact of terrain changes on the robot. The effectiveness of the proposed method and the feasibility of load walking in urban terrain are verified through comparative experiments, complex terrain load walking experiments in Webots, and real prototype experiments.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"69 11","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139187104","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

Uncertainty in Bayesian Reinforcement Learning for Robot Manipulation Tasks with Sparse Rewards 针对奖励稀疏的机器人操纵任务的贝叶斯强化学习中的不确定性

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354785

Li Zheng, Yanghong Li, Yahao Wang, Guangrui Bai, Haiyang He, Erbao Dong

{"title":"Uncertainty in Bayesian Reinforcement Learning for Robot Manipulation Tasks with Sparse Rewards","authors":"Li Zheng, Yanghong Li, Yahao Wang, Guangrui Bai, Haiyang He, Erbao Dong","doi":"10.1109/ROBIO58561.2023.10354785","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354785","url":null,"abstract":"This paper aims to explore the application of Bayesian deep reinforcement learning (BDRL) in robot manipulation tasks with sparse rewards, focusing on addressing the uncertainty in complex and sparsely rewarded environments. Conventional deep reinforcement learning (DRL) algorithms still face significant challenges in the context of robot manipulation tasks. To address this issue, this paper proposes a general algorithm framework called BDRL that combines reinforcement learning algorithms with Bayesian networks to quantify the model uncertainty, aleatoric uncertainty in neural networks, and uncertainty in the reward function. The effectiveness and generality of the proposed algorithm are validated through simulation experiments on multiple sets of different sparsely rewarded tasks, employing various advanced DRL algorithms. The research results demonstrate that the DRL algorithm based on the Bayesian network mechanism significantly improves the convergence speed of the algorithms in sparse reward tasks by accurately estimating the model uncertainty.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"48 6","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139187288","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