2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)最新文献_第10页

Simplified Modeling of Hybrid Soft Robots with Constant Stiffness Assumption 具有恒定刚度假设的混合软机器人简化模型

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10355009

Umer Huzaifa, Dimuthu D. K. Arachchige, Muhammad Aneeq uz Zaman, Usman Syed

引用次数: 0

An improved ORB-GMS image feature extraction and matching algorithm* 改进的 ORB-GMS 图像特征提取和匹配算法*

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10355043

Zhiying Tan, Wenbo Fan, Weifeng Kong, Xu Tao, Linsen Xu, Xiaobin Xu

{"title":"An improved ORB-GMS image feature extraction and matching algorithm*","authors":"Zhiying Tan, Wenbo Fan, Weifeng Kong, Xu Tao, Linsen Xu, Xiaobin Xu","doi":"10.1109/ROBIO58561.2023.10355043","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10355043","url":null,"abstract":"Feature point extraction and matching is the key technology in object detection and simultaneous localization and mapping (SLAM). Aiming at the problems such as easy redundancy of feature points extracted by traditional ORB algorithm, low matching accuracy of mainstream robust estimation algorithms and low real-time performance, an improved ORB-GMS image feature extraction and matching algorithm is proposed. Firstly, the algorithm uses the gray value of the image to calculate the adaptive extraction threshold of the feature points. Then the image pyramid is constructed according to the image size. The set number of total feature points to be extracted is evenly distributed to each layer image according to the area ratio; Extract feature points from each layer of the image pyramid, and count the extracted feature points from each layer. If the number of feature points extracted from each layer meets the set number of images from each layer, the extraction ends. Then the quadtree algorithm is used to homogenize the feature points. Finally, the network scoring model is optimized from 8 neighborhood to 4 neighborhood, which reduces the computing time. Experimental results show that the matching accuracy of the proposed algorithm is 14% higher than that of the original algorithm, and the running time is 12% lower.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"78 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":"139187170","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

Path-planning for the Human-arm-like Collaborative Robot with the Capability of Infinite Rotation 具有无限旋转能力的仿人协作机器人的路径规划

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354764

Yusheng Yang, Jiajia Liu, Qiaoni Yang, Hang Shi, Yangmin Xie

{"title":"Path-planning for the Human-arm-like Collaborative Robot with the Capability of Infinite Rotation","authors":"Yusheng Yang, Jiajia Liu, Qiaoni Yang, Hang Shi, Yangmin Xie","doi":"10.1109/ROBIO58561.2023.10354764","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354764","url":null,"abstract":"With the advantages of high safety and scalability, collaborative robots are widely used in the fields of Human-Robot Collaboration and Interaction. However, the joint limits of the robot restrict its flexibility and workspace, especially in a cluttered environment. Inspired by the motion of the human arm, whose elbow joint and shoulder joint can rotate infinitely, a collaborative robot with the capability of infinite rotation of its first and fourth joints is constructed in this paper and named the IR-Robot. With the breakthrough of the joint limit, the corresponding dimension in the robot’s configuration space changes from a bounded dimension to an unbounded dimension. The high-dimensional torus configuration space (HTCS) is presented to describe the bounded-unbounded dimensions hybrid property of the IR-Robot’s configuration space. Additionally, an IR-RRT* algorithm is proposed to conduct path-planning in HTCS. The experimental results in simulation and the real world demonstrate the feasibility and superiority of the IR-Robot in path-following and path-planning tasks.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"55 10","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":"139187190","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

A Sample Repetitive Manipulation Mechanism (SRMM) for Lunar Regolith In-Situ Analysis: Design and Validation 用于月球岩石原位分析的样品重复操纵装置（SRMM）：设计与验证

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354957

Yi Liu, Junyue Tang, Yafang Liu, Gongbo Ma, Feng Sun, Ye Li, Shengyuan Jiang

{"title":"A Sample Repetitive Manipulation Mechanism (SRMM) for Lunar Regolith In-Situ Analysis: Design and Validation","authors":"Yi Liu, Junyue Tang, Yafang Liu, Gongbo Ma, Feng Sun, Ye Li, Shengyuan Jiang","doi":"10.1109/ROBIO58561.2023.10354957","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354957","url":null,"abstract":"To exactly detect the water ice of the South Pole of the moon, a lunar regolith in-situ analysis payload deploying a mass spectrometer is proposed for China future lunar exploration missions. In order to receive the lunar regolith sample from a robotic arm with a soil sampler and transfer it into a furnace for further analysis, a sample manipulation mechanism is required during the above work flow. To solve the problems of adapting the sampler’s docking accuracy, receiving and transferring two different types of lunar soil sample under times of in-situ analysis, etc., a sample repetitive manipulation mechanism (SRMM) is proposed in this paper. By using a floating adjustable docking components and a flexible hopper, two types of encapsulated regolith sample and bulk material sample can be received with minimal sample loss, respectively. In order to receive and transfer two types of samples multiple times, two sample receiving methods have been designed that can be repeatedly transferred. A worm and worm wheel combined with a ball screw is designed in SRMM. To verify the above mechanism design, validation experiments were conducted. It indicates that this novel SRMM can be deployed in the future mission after further environmental tests.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"41 5","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":"139187206","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

Stroke Synchronization of Underwater Modular Robot through Physical Interaction 通过物理交互实现水下模块机器人的行程同步

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354986

Kohei Nishikawa, Y. Origane, Daisuke Kurabayashi

{"title":"Stroke Synchronization of Underwater Modular Robot through Physical Interaction","authors":"Kohei Nishikawa, Y. Origane, Daisuke Kurabayashi","doi":"10.1109/ROBIO58561.2023.10354986","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354986","url":null,"abstract":"Modular robots are expected to be used in extreme environments owing to their adaptability, and various modular robots have been developed. Most studies have focused on the expandability of capabilities or the integration of modules, whereas only a few studies have investigated autonomous decentralized control, in which each module harmonizes its own movements for overall functionality. We developed an underwater modular robot that synchronizes its paddle strokes; the robot is based on the motif of Gonium, a multicellular alga. We built a reduced system model of modules to represent the state of an oscillator by using a phase with attractive interactions with others. Because the model is similar to the Kuramoto model, we applied analysis methods. Real robotic modules were built, and experiments were conducted using a colony of the modules. The experimental results confirmed that the colony exhibited stroke synchronization ability by compensating for individual differences. The stroke synchronization is expected to stabilize the movements of robot colonies and improve their overall propulsion.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"51 8","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":"139187224","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

A soft hydraulic endorectal actuator for prostate radiotherapy 用于前列腺放射治疗的软液压肛门直肠内推杆

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10355037

Aryan Niknam Maleki, Alexander Thompson, M. Runciman, Julia Murray, G. Mylonas

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

DynGraspVS: Servoing Aided Grasping for Dynamic Environments DynGraspVS：为动态环境提供伺服辅助抓取功能

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354813

Gunjan Gupta, Vedansh Mittal, K. M. Krishna

{"title":"DynGraspVS: Servoing Aided Grasping for Dynamic Environments","authors":"Gunjan Gupta, Vedansh Mittal, K. M. Krishna","doi":"10.1109/ROBIO58561.2023.10354813","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354813","url":null,"abstract":"Visual servoing has been gaining popularity in various real-world vision-centric robotic applications. Autonomous robotic grasping often deals with unseen and unstructured environments, and in this task, Visual Servoing has been able to generate improved end-effector control by providing visual feedback. However, existing Servoing-aided grasping methods tend to fail at the task of grasping in dynamic environments i.e. - moving objects.In this paper, we introduce DynGraspVS, a novel Image-based Visual Servoing-aided Grasping approach that models the motion of moving objects in its interaction matrix. Leveraging a single-step rollout strategy, our approach achieves a remarkable increase in success rate, while converging faster and achieving a smoother trajectory, while maintaining precise alignments in six degrees of freedom. By integrating the velocity information into the interaction matrix, our method is able to successfully complete the challenging task of robotic grasping in the case of dynamic objects, while outperforming existing deep Model Predictive Control (MPC) based methods in the PyBullet simulation environment. We test it with a range of objects in the YCB dataset with varying range of shapes, sizes, and material properties. We report various evaluation metrics such as photometric error, success rate, time taken, and trajectory length.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"37 2","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139187294","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

Path Planning for Robotic Arm Based on Reinforcement Learning under the Train 基于列车下强化学习的机械臂路径规划

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354783

Guanhao Xie, Duo Zhao, Qichao Tang, Muhua Zhang, Wenjie Zhao, Yewen Wang

{"title":"Path Planning for Robotic Arm Based on Reinforcement Learning under the Train","authors":"Guanhao Xie, Duo Zhao, Qichao Tang, Muhua Zhang, Wenjie Zhao, Yewen Wang","doi":"10.1109/ROBIO58561.2023.10354783","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354783","url":null,"abstract":"Due to the widespread use of robotic arms, path planning for them has always been a hot research topic. However, traditional path planning algorithms struggle to ensure low disparity in each path, making them unsuitable for operation scenarios with high safety requirements, such as the undercarriage environment of train. A Reinforcement Learning (RL) framework is proposed in this article to address this challenge. The Proximal Policy Optimization (PPO) algorithm has been enhanced, resulting in a variant referred to as Randomized PPO (RPPO), which demonstrates slightly accelerated convergence speed. Additionally, a reward model is proposed to assist the agent in escaping local optima. For modeling application environment, lidar is employed for obtaining obstacle point cloud information, which is then transformed into an octree grid map for maneuvering the robotic arm to avoid obstacles. According to the experimental results, the paths planned by our system are superior to those of RRT* in terms of both average length and standard deviation, and RPPO exhibits better convergence speed and path standard deviation compared to PPO.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"34 12","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139187354","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

Deep Reinforcement Learning for a Humanoid Robot Basketball Player 仿人机器人篮球运动员的深度强化学习

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354565

Shuaiqi Zhang, Guodong Zhao, Peng Lin, Mingshuo Liu, Jianhua Dong, Haoyu Zhang

{"title":"Deep Reinforcement Learning for a Humanoid Robot Basketball Player","authors":"Shuaiqi Zhang, Guodong Zhao, Peng Lin, Mingshuo Liu, Jianhua Dong, Haoyu Zhang","doi":"10.1109/ROBIO58561.2023.10354565","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354565","url":null,"abstract":"Currently, the majority of research on humanoid robot basketball shooting focuses on traditional control methods. However, these methods primarily rely on human-robot interaction and fixed shooting patterns to control the robot’s shooting actions, resulting in limited autonomy for the robot. They often require extensive manual design and coding operations, and face challenges in adapting to different shooting scenarios. To address these problems, this paper applies deep reinforcement learning to the basketball shooting task for a humanoid robot. The task environment is based on the basketball shooting competition defined in the FIRA HuroCup. This paper uses the Double DQN algorithm to train the humanoid robot to master end-to-end basketball shooting skills, specifically: The robot takes RGB images captured by its own head camera as input, then decides to take one of three discrete actions, including turning left, turning right, and shooting. In the experimental section, we validate the effectiveness of our approach and conduct an analysis and discussion on the setup of important parameters that influence the experimental results.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"45 1","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":"139187376","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