2020 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第3页

A Bio-Signal Enhanced Adaptive Impedance Controller for Lower Limb Exoskeleton 下肢外骨骼生物信号增强自适应阻抗控制器

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196774

Lin-qing Xia, Yachun Feng, Fan Chen, Xinyu Wu

引用次数: 4

CCAN: Constraint Co-Attention Network for Instance Grasping 实例抓取的约束共注意网络

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197182

Junhao Cai, X. Tao, Hui Cheng, Zhanpeng Zhang

{"title":"CCAN: Constraint Co-Attention Network for Instance Grasping","authors":"Junhao Cai, X. Tao, Hui Cheng, Zhanpeng Zhang","doi":"10.1109/ICRA40945.2020.9197182","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197182","url":null,"abstract":"Instance grasping is a challenging robotic grasping task when a robot aims to grasp a specified target object in cluttered scenes. In this paper, we propose a novel end-to-end instance grasping method using only monocular workspace and query images, where the workspace image includes several objects and the query image only contains the target object. To effectively extract discriminative features and facilitate the training process, a learning-based method, referred to as Constraint Co-Attention Network (CCAN), is proposed which consists of a constraint co-attention module and a grasp affordance predictor. An effective co-attention module is presented to construct the features of a workspace image from the extracted features of the query image. By introducing soft constraints into the co-attention module, it highlights the target object’s features while trivializes other objects’ features in the workspace image. Using the features extracted from the co-attention module, the cascaded grasp affordance interpreter network only predicts the grasp configuration for the target object. The training of the CCAN is totally based on simulated self-supervision. Extensive qualitative and quantitative experiments show the effectiveness of our method both in simulated and real-world environments even for totally unseen objects.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"8 1","pages":"8353-8359"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84972810","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: 8

Hybrid Topological and 3D Dense Mapping through Autonomous Exploration for Large Indoor Environments 基于自主探索的大型室内环境混合拓扑和三维密集映射

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197226

Clara Gómez, M. Fehr, A. Millane, A. C. Hernández, Juan I. Nieto, R. Barber, R. Siegwart

{"title":"Hybrid Topological and 3D Dense Mapping through Autonomous Exploration for Large Indoor Environments","authors":"Clara Gómez, M. Fehr, A. Millane, A. C. Hernández, Juan I. Nieto, R. Barber, R. Siegwart","doi":"10.1109/ICRA40945.2020.9197226","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197226","url":null,"abstract":"Robots require a detailed understanding of the 3D structure of the environment for autonomous navigation and path planning. A popular approach is to represent the environment using metric, dense 3D maps such as 3D occupancy grids. However, in large environments the computational power required for most state-of-the-art 3D dense mapping systems is compromising precision and real-time capability. In this work, we propose a novel mapping method that is able to build and maintain 3D dense representations for large indoor environments using standard CPUs. Topological global representations and 3D dense submaps are maintained as hybrid global map. Submaps are generated for every new visited place. A place (room) is identified as an isolated part of the environment connected to other parts through transit areas (doors). This semantic partitioning of the environment allows for a more efficient mapping and path-planning. We also propose a method for autonomous exploration that directly builds the hybrid representation in real time.We validate the real-time performance of our hybrid system on simulated and real environments regarding mapping and path-planning. The improvement in execution time and memory requirements upholds the contribution of the proposed work.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"38 1","pages":"9673-9679"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85624719","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: 22

Prediction of Gait Cycle Percentage Using Instrumented Shoes with Artificial Neural Networks 用人工神经网络预测仪表鞋的步态周期百分比

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196747

Antonio Prado, Xiya Cao, Xiangzhuo Ding, S. Agrawal

{"title":"Prediction of Gait Cycle Percentage Using Instrumented Shoes with Artificial Neural Networks","authors":"Antonio Prado, Xiya Cao, Xiangzhuo Ding, S. Agrawal","doi":"10.1109/ICRA40945.2020.9196747","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196747","url":null,"abstract":"Gait training is widely used to treat gait abnormalities. Traditional gait measurement systems are limited to instrumented laboratories. Even though gait measurements can be made in these settings, it is challenging to estimate gait parameters robustly in real-time for gait rehabilitation, especially when walking over-ground. In this paper, we present a novel approach to track the continuous gait cycle during overground walking outside the laboratory. In this approach, we instrument standard footwear with a sensorized insole and an inertial measurement unit. Artificial neural networks are used on the raw data obtained from the insoles and IMUs to compute the continuous percentage of the gait cycle for the entire walking session. We show in this paper that when tested with novel subjects, we can predict the gait cycle with a Root Mean Square Error (RMSE) of 7.2%. The onset of each cycle can be detected within an RMSE time of 41.5 ms with a 99% detection rate. The algorithm was tested with 18840 strides collected from 24 adults. In this paper, we tested a combination of fully-connected layers, an Encoder-Decoder using convolutional layers, and recurrent layers to identify an architecture that provided the best performance.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"54 1","pages":"2834-2840"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85875753","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: 9

Using multiple short hops for multicopter navigation with only inertial sensors 多旋翼飞行器仅用惯性传感器导航的多短跳方法

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196610

Xiangyu Wu, M. Mueller

{"title":"Using multiple short hops for multicopter navigation with only inertial sensors","authors":"Xiangyu Wu, M. Mueller","doi":"10.1109/ICRA40945.2020.9196610","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196610","url":null,"abstract":"In certain challenging environments, such as inside buildings on fire, the main sensors (e.g. cameras, LiDARs and GPS systems) used for multicopter localization can become unavailable. Direct integration of the inertial navigation sensors (the accelerometer and rate gyroscope), is however unaffected by external disturbances, but the rapid error accumulation quickly makes a naive application of such a strategy feasible only for very short durations. In this work we propose a motion strategy for reducing the inertial navigation state estimation error of multicopters. The proposed strategy breaks a long duration flight into multiple short duration hops between which the vehicle remains stationary on the ground. When the vehicle is stationary, zero-velocity pseudo-measurements are introduced to an extended Kalman Filter to reduce the state estimation error. We perform experiments for closed-loop control of a multicopter for evaluation. The mean absolute position estimation error was 3.4% over a total flight distance of 5m in the experiments. The results showed a 80% reduction compared to the standard inertial navigation method without using this strategy. In addition, an additional experiment with total flight distance of 10m is conducted to demonstrate the ability of this method to navigate a multicopter in real-world environment. The final trajectory tracking error was 3% of the total flight distance.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"109 1","pages":"8559-8565"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77102778","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: 4

Automated detection of soleus concentric contraction in variable gait conditions for improved exosuit control 可变步态条件下比目鱼同心圆收缩的自动检测，以改进外太空服控制

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197428

R. Nuckols, K. Swaminathan, Sangjun Lee, L. Awad, C. Walsh, R. Howe

{"title":"Automated detection of soleus concentric contraction in variable gait conditions for improved exosuit control","authors":"R. Nuckols, K. Swaminathan, Sangjun Lee, L. Awad, C. Walsh, R. Howe","doi":"10.1109/ICRA40945.2020.9197428","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197428","url":null,"abstract":"Exosuits can reduce metabolic demand and improve gait. Controllers explicitly derived from biological mechanisms that reflect the user's joint or muscle dynamics should in theory allow for individualized assistance and enable adaptation to changing gait. With the goal of developing an exosuit control strategy based on muscle power, we present an approach for estimating, at real time rates, when the soleus muscle begins to generate positive power. A low-profile ultrasound system recorded B-mode images of the soleus in walking individuals. An automated routine using optical flow segmented the data to a normalized gait cycle and estimated the onset of concentric contraction at real-time rates (~130Hz). Segmentation error was within 1% of the gait cycle compared to using ground reaction forces. Estimation of onset of concentric contraction had a high correlation (R2=0.92) and an RMSE of 2.6% gait cycle relative to manual estimation. We demonstrated the ability to estimate the onset of concentric contraction during fixed speed walking in healthy individuals that ranged from 39.3% to 45.8% of the gait cycle and feasibility in two persons post-stroke walking at comfortable walking speed. We also showed the ability to measure a shift in onset timing to 7% earlier when the biological system adapts from level to incline walking. Finally, we provided an initial evaluation for how the onset of concentric contraction might be used to inform exosuit control in level and incline walking.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"21 1","pages":"4855-4862"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78557831","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: 7

Enhanced Teleoperation Using Autocomplete 增强远程操作使用自动完成

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197140

Mohammad Kassem Zein, Abbas Sidaoui, Daniel C. Asmar, I. Elhajj

引用次数: 3

Nonlinear Synchronization Control for Short-Range Mobile Sensors Drifting in Geophysical Flows 地球物理流中漂移的近程移动传感器非线性同步控制

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196701

Cong Wei, H. Tanner, M. A. Hsieh

引用次数: 1

Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos Motion2Vec:基于手术视频的半监督表示学习

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197324

A. Tanwani, P. Sermanet, Andy Yan, Raghav V. Anand, Mariano Phielipp, Ken Goldberg

{"title":"Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos","authors":"A. Tanwani, P. Sermanet, Andy Yan, Raghav V. Anand, Mariano Phielipp, Ken Goldberg","doi":"10.1109/ICRA40945.2020.9197324","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197324","url":null,"abstract":"Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we learn a motion-centric representation of surgical video demonstrations by grouping them into action segments/subgoals/options in a semi-supervised manner. We present Motion2Vec, an algorithm that learns a deep embedding feature space from video observations by minimizing a metric learning loss in a Siamese network: images from the same action segment are pulled together while pushed away from randomly sampled images of other segments, while respecting the temporal ordering of the images. The embeddings are iteratively segmented with a recurrent neural network for a given parametrization of the embedding space after pre-training the Siamese network. We only use a small set of labeled video segments to semantically align the embedding space and assign pseudo-labels to the remaining unlabeled data by inference on the learned model parameters. We demonstrate the use of this representation to imitate surgical suturing kinematic motions from publicly available videos of the JIGSAWS dataset. Results give 85.5% segmentation accuracy on average suggesting performance improvement over several state-of-the-art baselines, while kinematic pose imitation gives 0.94 centimeter error in position per observation on the test set. Videos, code and data are available at: https://sites.google.com/view/motion2vec","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"2174-2181"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84069170","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: 30

A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps 基于多视角先验地图的水下路径规划方法

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196680

Daniel Cagara, M. Dunbabin, P. Rigby

{"title":"A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps","authors":"Daniel Cagara, M. Dunbabin, P. Rigby","doi":"10.1109/ICRA40945.2020.9196680","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196680","url":null,"abstract":"This paper presents a path planning methodology which enables Autonomous Underwater Vehicles (AUVs) to navigate in shallow complex environments such as coral reefs. The approach leverages prior information from an aerial photographic survey, and derived bathymetric information of the corresponding area. From these prior maps, a set of features is obtained which define an expected arrangement of objects and bathymetry likely to be perceived by the AUV when underwater. A navigation graph is then constructed by predicting the arrangement of features visible from a set of test points within the prior, which allows the calculation of the shortest paths from any pair of start and destination points. A maximum likelihood function is defined which allows the AUV to match its observations to the navigation graph as it undertakes its mission. To improve robustness, the history of observed features are retained to facilitate possible recovery from non-detectable or misclassified objects. The approach is evaluated using a photo-realistic simulated environment, and results illustrate the merits of the approach even when only a relatively small number of features can be identified from the prior map.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"78 1","pages":"8573-8579"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84086342","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