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

Modelling and Compensation for Transmission Error of Timing Belt in Legged Robots 支腿机器人同步带传动误差的建模与补偿

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354989

Jingcheng Jiang, Yifang Zhang, N. Tsagarakis

{"title":"Modelling and Compensation for Transmission Error of Timing Belt in Legged Robots","authors":"Jingcheng Jiang, Yifang Zhang, N. Tsagarakis","doi":"10.1109/ROBIO58561.2023.10354989","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354989","url":null,"abstract":"The timing belt transmission offers numerous advantages for legged robots, including high efficiency, impact absorption and large range of joint motion. However, the transmission error under high load remains challenging to locomotion control and further applications of belt transmission. Traditional linear models cannot effectively model the belt deformation under a wide range of tension variations due to the nonlinearity. In this paper, we propose a model of the compensation for the belt transmission error based on the pretension and torque of the pully. The adopted approach bypasses the complexity of elaborate physical model derivations, yielding a non-linear model for transmission system errors through straightforward fitting. Based on the proposed model, an error compensation control is investigated and tested with an one-DoF leg prototype of legged robot. The alignment between experimental results and theoretical analysis demonstrates the accuracy of the modeling and the effectiveness of the error compensation control method. The proposed model provides a convenient and straightforward solution to effectively compensate for the belt transmission errors in legged robots.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"60 9","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":"139187064","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

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 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

Estimation of Deformation for Self-balancing Lower Limb Exoskeleton Only Using Force/Torque Sensors 仅使用力/扭矩传感器估算自平衡下肢外骨骼的形变

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354999

Ziqiang Chen, Ming Yang, Feng Li, Wentao Li, Jinke Li, Dingkui Tian, Jianquan Sun, Yong He, Xinyu Wu

{"title":"Estimation of Deformation for Self-balancing Lower Limb Exoskeleton Only Using Force/Torque Sensors","authors":"Ziqiang Chen, Ming Yang, Feng Li, Wentao Li, Jinke Li, Dingkui Tian, Jianquan Sun, Yong He, Xinyu Wu","doi":"10.1109/ROBIO58561.2023.10354999","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354999","url":null,"abstract":"This paper presents a general estimation method of deformation for the self-balancing lower limb exoskeleton (SBLLE). In particular, we propose a Bi-LSTM deformation estimator (BLDE) to predict and compensate for the deformation of SBLLE based on the current force and torque data measured by force/torque (F/T) sensors. First, we choose four movements including squatting down and up, waist twisting, left foot lifting, and right foot lifting to mimic the constituent action of walking motion. The deformation data set is obtained through the motion capture analysis system and offline planning trajectories, and the relative F/T data set is obtained by the F/T sensors embedded in the feet of SBLLE. Second, the BiLSTM network is trained to learn the relationship between the deformation and F/T and verified on the test set. After that, BLDE is added to the control system of SBLLE to estimate and compensate for the deformation. Finally, four same movements and the walking experiment are conducted on the exoskeleton AutoLEE-G2 with BLDE. The experimental results have proven that BLDE can predict and compensate for deformation by only using F/T sensors.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"87 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":"139186764","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

Automatic Control System for Reach-to-Grasp Movement of a 7-DOF Robotic Arm Using Object Pose Estimation with an RGB Camera 利用 RGB 摄像机进行物体姿态估计的 7-DOF 机械臂伸抓运动自动控制系统

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354531

Shuting Bai, Jiazhen Guo, Yinlai Jiang, Hiroshi Yokoi, Shunta Togo

引用次数: 0

Decoupled Control of Bipedal Locomotion Based on HZD and H-LIP 基于 HZD 和 H-LIP 的双足运动解耦控制

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354624

Yinong Ye, Yongming Yue, Wei Gao, Shiwu Zhang

引用次数: 0

A Quick Means for the Burnt Skin Area Calculation via Multiple-view Structured Light Sensors 通过多视角结构光传感器计算烧伤皮肤面积的快速方法

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354748

Di Wu, Yuping Ye, Feifei Gu, Zhan Song

引用次数: 0

3D Semantic Segmentation for Grape Bunch Point Cloud Based on Feature Enhancement 基于特征增强的葡萄串点云三维语义分割

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354793

Jiangtao Luo, Dongbo Zhang, Tao Yi

{"title":"3D Semantic Segmentation for Grape Bunch Point Cloud Based on Feature Enhancement","authors":"Jiangtao Luo, Dongbo Zhang, Tao Yi","doi":"10.1109/ROBIO58561.2023.10354793","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354793","url":null,"abstract":"As a representative bunch-type fruit,the collision-free and undamaged harvesting of grapes is of great significance. To obtain accurate 3D spatial semantic information,this paper proposes a method for multi-feature enhanced semantic segmentation model based on Mask R-CNN and PointNet++. Firstly, a depth camera is used to obtain RGBD images. The RGB images are then inputted into the Mask-RCNN network for fast detection of grape bunches. The color and depth information are fused and transformed into point cloud data, followed by the estimation of normal vectors. Finally, the nine-dimensional point cloud,which include spatial location, color information, and normal vectors, are inputted into the improved PointNet++ network to achieve semantic segmentation of grape bunches, peduncles, and leaves. This process obtains the extraction of spatial semantic information from the surrounding area of the bunches. The experimental results show that by incorporating normal vector and color features, the overall accuracy of point cloud segmentation increases to 93.7%, with a mean accuracy of 81.8%. This represents a significant improvement of 12.1% and 13.5% compared to using only positional features. The results demonstrate that the model method presented in this paper can effectively provide precise 3D semantic information to the robot while ensuring both speed and accuracy. This lays the groundwork for subsequent collision-free and damage-free picking.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"63 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":"139187023","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

Speech-image based Multimodal AI Interaction for Scrub Nurse Assistance in the Operating Room 基于语音图像的多模态人工智能交互，为手术室内的洗刷护士提供帮助

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO) Pub Date : 2023-12-04 DOI: 10.1109/ROBIO58561.2023.10354726

W. Ng, Han Yi Wang, Zheng Li

{"title":"Speech-image based Multimodal AI Interaction for Scrub Nurse Assistance in the Operating Room","authors":"W. Ng, Han Yi Wang, Zheng Li","doi":"10.1109/ROBIO58561.2023.10354726","DOIUrl":"https://doi.org/10.1109/ROBIO58561.2023.10354726","url":null,"abstract":"With the increasing surgical need in our aging society, there is a lack of experienced surgical assistants, such as scrub nurses. To facilitate the training of junior scrub nurses and to reduce human errors, e.g., missing surgical items, we develop a speech-image based multimodal AI framework to assist scrub nurses in the operating room. The proposed framework allows real-time instrument type identification and instance detection, which enables junior scrub nurses to become more familiar with the surgical instruments and guides them throughout the surgical procedure. We construct an ex-vivo video-assisted thorascopic surgery dataset and benchmark it on common object detection models, reaching an average precision of 98.5% and an average recall of 98.9% on the state-of-the-art YOLO-v7. Additionally, we implement an oriented bounding box version of YOLO-v7 to address the undesired bounding box suppression in instrument crossing over. By achieving an average precision of 95.6% and an average recall of 97.4%, we improve the average recall by up to 9.2% compared to the previous oriented bounding box version of YOLO-v5. To minimize distraction during surgery, we adopt a deep learning-based automatic speech recognition model to allow surgeons to concentrate on the procedure. Our physical demonstration substantiates the feasibility of the proposed framework in providing real-time guidance and assistance for scrub nurses.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"73 2","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":"139187027","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