2017 18th International Conference on Advanced Robotics (ICAR)最新文献_第10页

Cooperative motion planning of redundant rover manipulators on uneven terrains 非均匀地形上冗余漫游机器人的协同运动规划

2017 18th International Conference on Advanced Robotics (ICAR) Pub Date : 2017-07-01 DOI: 10.1109/ICAR.2017.8023502

R. Raja, B. Dasgupta, A. Dutta

{"title":"Cooperative motion planning of redundant rover manipulators on uneven terrains","authors":"R. Raja, B. Dasgupta, A. Dutta","doi":"10.1109/ICAR.2017.8023502","DOIUrl":"https://doi.org/10.1109/ICAR.2017.8023502","url":null,"abstract":"In this paper we consider the problem of cooperative motion planning for redundant mobile manipulator on uneven terrains. This approach involves formulating the trajectory planning as non-linear constrained minimization problem of joint angle movement of mobile manipulator at each instance. The main problem is to solve (i) the redundancy exist in the system considering parameters of wheel-terrain interactions, (ii) the cooperative behavior of the mobile manipulator while performing the task, and (iii) the manipulability issues. To perform task the manipulator moves towards desired location, while the mobile robot moves to enhance the manipulator task space. A weighting factors has been introduced to define the level of importance of movement of each joint of the mobile manipulator. A quality measure has been computed to measure the ability of mobile manipulator for a particular configuration. The problem of trajectory planning and redundancy resolution has been solved by Augmented Lagrangian Method (ALM). To evaluate the method several simulations have been performed. The simulation and experimental results have been presented, which shows that the method provides feasible trajectories and successfully tracks the desired end-effector path.","PeriodicalId":198633,"journal":{"name":"2017 18th International Conference on Advanced Robotics (ICAR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121871960","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

Controlled tactile exploration and haptic object recognition 控制触觉探索和触觉对象识别

2017 18th International Conference on Advanced Robotics (ICAR) Pub Date : 2017-06-27 DOI: 10.1109/ICAR.2017.8023495

Massimo Regoli, Nawid Jamali, G. Metta, L. Natale

引用次数: 10

Independent motion detection with event-driven cameras 独立运动检测与事件驱动的相机

2017 18th International Conference on Advanced Robotics (ICAR) Pub Date : 2017-06-27 DOI: 10.1109/ICAR.2017.8023661

Valentina Vasco, Arren J. Glover, Elias Mueggler, D. Scaramuzza, L. Natale, C. Bartolozzi

{"title":"Independent motion detection with event-driven cameras","authors":"Valentina Vasco, Arren J. Glover, Elias Mueggler, D. Scaramuzza, L. Natale, C. Bartolozzi","doi":"10.1109/ICAR.2017.8023661","DOIUrl":"https://doi.org/10.1109/ICAR.2017.8023661","url":null,"abstract":"Unlike standard cameras that send intensity images at a constant frame rate, event-driven cameras asynchronously report pixel-level brightness changes, offering low latency and high temporal resolution (both in the order of micro-seconds). As such, they have great potential for fast and low power vision algorithms for robots. Visual tracking, for example, is easily achieved even for very fast stimuli, as only moving objects cause brightness changes. However, cameras mounted on a moving robot are typically non-stationary and the same tracking problem becomes confounded by background clutter events due to the robot ego-motion. In this paper, we propose a method for segmenting the motion of an independently moving object for event-driven cameras. Our method detects and tracks corners in the event stream and learns the statistics of their motion as a function of the robot's joint velocities when no independently moving objects are present. During robot operation, independently moving objects are identified by discrepancies between the predicted corner velocities from ego-motion and the measured corner velocities. We validate the algorithm on data collected from the neuromorphic iCub robot. We achieve a precision of ∼ 90% and show that the method is robust to changes in speed of both the head and the target.","PeriodicalId":198633,"journal":{"name":"2017 18th International Conference on Advanced Robotics (ICAR)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128003510","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: 27

Robot trajectory planning method based on genetic chaos optimization algorithm 基于遗传混沌优化算法的机器人轨迹规划方法

2017 18th International Conference on Advanced Robotics (ICAR) Pub Date : 2017-05-31 DOI: 10.1109/ICAR.2017.8023673

Qiwan Zhang, Mingting Yuan, R. Song

引用次数: 6

A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition 一种完全端到端的深度学习方法，用于实时同步3D重建和材料识别

2017 18th International Conference on Advanced Robotics (ICAR) Pub Date : 2017-03-14 DOI: 10.1109/ICAR.2017.8023499

Cheng Zhao, Li Sun, R. Stolkin

{"title":"A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition","authors":"Cheng Zhao, Li Sun, R. Stolkin","doi":"10.1109/ICAR.2017.8023499","DOIUrl":"https://doi.org/10.1109/ICAR.2017.8023499","url":null,"abstract":"This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction has predominantly focused on recognition of everyday domestic objects (tables, chairs etc.), whereas previous work on material recognition has largely been confined to single 2D images without any 3D reconstruction. Meanwhile, most 3D semantic reconstruction methods rely on computationally expensive post-processing, using Fully-Connected Conditional Random Fields (CRFs), to achieve consistent segmentations. In contrast, we propose a deep learning method which performs 3D reconstruction while simultaneously recognising different types of materials and labeling them at the pixel level. Unlike previous methods, we propose a fully end-to-end approach, which does not require hand-crafted features or CRF post-processing. Instead, we use only learned features, and the CRF segmentation constraints are incorporated inside the fully end-to-end learned system. We present the results of experiments, in which we trained our system to perform real-time 3D semantic reconstruction for 23 different materials in a real-world application. The run-time performance of the system can be boosted to around 10Hz, using a conventional GPU, which is enough to achieve realtime semantic reconstruction using a 30fps RGB-D camera. To the best of our knowledge, this work is the first real-time end-to-end system for simultaneous 3D reconstruction and material recognition.","PeriodicalId":198633,"journal":{"name":"2017 18th International Conference on Advanced Robotics (ICAR)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128777344","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: 38

Non-iterative SLAM 价值大满贯

2017 18th International Conference on Advanced Robotics (ICAR) Pub Date : 2017-01-19 DOI: 10.1109/ICAR.2017.8023500

Chen Wang, Junsong Yuan, Lihua Xie

{"title":"Non-iterative SLAM","authors":"Chen Wang, Junsong Yuan, Lihua Xie","doi":"10.1109/ICAR.2017.8023500","DOIUrl":"https://doi.org/10.1109/ICAR.2017.8023500","url":null,"abstract":"The goal of this paper is to create a new framework for dense SLAM that is light enough for micro-robot systems based on depth camera and inertial sensor. Feature-based and direct methods are two mainstreams in visual SLAM. Both methods minimize photometric or reprojection error by iterative solutions, which are computationally expensive. To overcome this problem, we propose a non-iterative framework to reduce computational requirement. First, the attitude and heading reference system (AHRS) and axonometric projection are utilized to decouple the 6 Degree-of-Freedom (DoF) data, so that point clouds can be matched in independent spaces respectively. Second, based on single key-frame training, the matching process is carried out in frequency domain by Fourier transformation, which provides a closed-form non-iterative solution. In this manner, the time complexity is reduced to O(n log n), where n is the number of matched points in each frame. To the best of our knowledge, this method is the first non-iterative and online trainable approach for data association in visual SLAM. Compared with the state-of-the-arts, it runs at a faster speed and obtains 3-D maps with higher resolution yet still with comparable accuracy.","PeriodicalId":198633,"journal":{"name":"2017 18th International Conference on Advanced Robotics (ICAR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131670236","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: 25

PCA-aided fully convolutional networks for semantic segmentation of multi-channel fMRI pca辅助的全卷积网络在多通道fMRI语义分割中的应用

2017 18th International Conference on Advanced Robotics (ICAR) Pub Date : 2016-10-06 DOI: 10.1109/ICAR.2017.8023506

L. Tai, Haoyang Ye, Qiong Ye, Ming Liu

引用次数: 13

ROSRemote, using ROS on cloud to access robots remotely ROSRemote，使用云上的ROS远程访问机器人

2017 18th International Conference on Advanced Robotics (ICAR) Pub Date : 1900-01-01 DOI: 10.1109/ICAR.2017.8023621

Alyson B. M. Pereira, G. S. Bastos

引用次数: 14