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International Conference on Informative and Cybernetics for Computational Social systems (ICCSS)

作者： Rui Liang Kai Wu Sheng Xu Tiantian Xu Xiaoyi Ma Lianyu Fu City West Power Supply Branch State Grid Tianjin Electric Power Company Tianjin China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China State Grid Tianjin Information & Telecommunication Company Tianjin China

ISBN: (纸本)9781665402460

Angle-of-arrival (AOA) target localization using the unmanned aerial vehicle (UAV) has been widely applied in many practical applications. To localize an invasive target quickly and accurately, both the estimation and UAV path optimization algorithms are required. This paper focuses on developing a path optimization method to improve the target estimation performance. Firstly, the problem formulation of AOA target localization is introduced. Secondly, the classical pseudolinear Kalman filter (PLKF) and the gradient-based path optimization are presented. Thirdly, we analyze the problems that existed in the previous methods and propose an improved gradient-descent path optimization algorithm combined with a simple grid search method. Finally, the simulation examples verify the effectiveness of the proposed methods.

关键词： Location awareness Adaptation models Three-dimensional displays Target tracking Search methods Estimation Optimization methods

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A grasping CNN with image segmentation for mobile manipulating robot

A grasping CNN with image segmentation for mobile manipulati...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Yu, Yingying Cao, Zhiqiang Liang, Shuang Liu, Zhicheng Yu, Junzhi Chen, Xuechao Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Intelligent Robotics Institute BIT Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (纸本)9781728163215

This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FPN network combined with point cloud clustering is used to obtain the mask of the target object. Then, the grayscale map and the depth map corresponding to the target object are combined and sent to the DrGNet network for providing multi-scale images. On this basis, depthwise separable convolution is used for encoding. The results of encoders are refined according to the light-weight RefineNet as well as sSE, which can achieve a better grasp detection. The proposed method is verified by the experiments on mobile manipulating robot. © 2019 IEEE.

关键词： Image segmentation

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Automatic Operating Method of Robot Dexterous Multi-fingered Hand Using Heuristic Rules 1

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4th International Conference on Cognitive systems and Information Processing, ICCSIP 2018

作者： Wang, Wei Zhang, Qiangbing Sun, Zeyuan Chen, Xi Jiang, Zhihong IRI School of Mechatronical Engineering Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robotics and Systems Beijing China

ISBN: (数字)9789811379833

ISBN: (纸本)9789811379826

Automatic grasp planning of robotic hand is always a difficult problem in robotics. Although researchers have developed underactuated hands with less degrees of freedom, automatic grasp planning is still a difficult problem because of the huge number of possible hand configurations. But humans simplify this problem by applying several usual grasp starting postures on most of grasp tasks. In this paper, a method for grasping planning is presented which can use a set of heuristic rules to limit the possibilities of hand configurations. By modeling the object as a set of primitive shapes and applying appropriate grasp starting postures on it, many grasp samples can be generated. And this is the basic content of the heuristic rules. Then, given grasp samples in finite number, a Simulink and Adams co-simulator is built to simulate these discrete grasp samples. The main purpose of the co-simulator is to simulate the dynamic behavior of robotic hand when grasping and measure the grasp quality using force closure and grasp measurement of largest-minimum resisted wrench. Last, a grasp library with force closure grasps is built where all items of grasp are sorted by the function value of grasp measurement. With the library, it’s easy to plan a grasp for certain object by just selecting an appropriate grasp from the library. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Simulink

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A novel multi-scale fourier descriptor based on plane orthogonal vectors for fast shape retrieval

A novel multi-scale fourier descriptor based on plane orthog...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Ma, Jiajun Zhu, Jiafeng Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong Province China

ISBN: (纸本)9781728163215

Shape retrieval is an interesting and challenging issue in computer vision and robot vision. The recent works employing Fourier-based multi-scale descriptors achieve some success in shape retrieval, especially make real-time application possible by improving the efficiency. However, these methods are commonly adopting really complex shape descriptors to present the shape information which is not intuitive enough. In our work, we propose a new Fourier-based method with novel multi-scale descriptor which is a combination of contour feature and regional feature. This novel descriptor has two main characteristics: First, utilize six different invariant signatures which are based on plane orthogonal vectors;Second, leverage multiple scale structures to describe local and global information of shapes. Except the easiness in understanding, this descriptor approach also produces a more distinct topology of shapes than other approaches which means the property of stability on variations, linear or nonlinear. The method is tested on some popular databases that provides a promising retrieval rate(87.46% and 99.27%) with constant time complexity optimization. The results confirm that our method is more capable of representing the topology of shapes than most other methods based on Fourier descriptor. © 2019 IEEE.

关键词： Topology

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Task-space control interface for SoftBank humanoid robots and its human-robot interaction applications

arXiv

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arXiv 2020年

作者： Bolotnikova, Anastasia Gergondet, Pierre Tanguy, Arnaud Courtois, Sébastien Kheddar, Abderrahmane SoftBank Robotics Europe Paris France University of Montpellier–CNRS LIRMM Interactive Digital Humans Montpellier France CNRS-AIST Joint Robotics Laboratory IRL 3218 Tsukuba Japan Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

We present an open-source software interface, called mc naoqi, that allows to perform whole-body task-space Quadratic Programming based control, implemented in mc rtc framework, on the SoftBank robotics Europe humanoid robots. We describe the control interface, associated robot description packages, robot modules and sample whole-body controllers. We demonstrate the use of these tools in simulation for a robot interacting with a human model. Finally, we showcase and discuss the use of the developed open-source tools for running the human-robot close contact interaction experiments with real human subjects inspired from assistance scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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Monitoring of Olive Trees Temperatures under Different Irrigation Strategies by UAV Thermal Infrared Imagery

Monitoring of Olive Trees Temperatures under Different Irrig...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Pedro Marques Luís Pádua Thyago Brito Joaquim J. Sousa Anabela Fernandes-Silva Centre of Research and Technology of Agro-Environment and Biological Sciences Vila Real Portugal Centre for Robotics in Industry and Intelligent Systems. INESC-TEC Porto Portugal CEFAGE - Centre for Advanced Studies in Management and Economics Évora Portugal

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

With the continuous escalation of global warming and consequent water scarcity, techniques to optimize water use of irrigation in agriculture are needed. Thus, deficit irrigation strategies (DI) can be used for a sustainable water usage. However, it is necessary to recursively monitor plant response under DI to ensure their productivity and prevent from severe water stress. The goal of this study is to assesscanopy and soil surface temperatures of olive trees under different irrigation strategies, through thermal infrared images obtained by Unmanned Aerial Vehicle (UAV). The temperatures from the different irrigation strategies were analysed with three approaches using the difference between canopy and air temperatures (Tc-Ta). The use of UAV-based thermal infrared imagery has proven to be extremely useful to the estimation of olive canopy and soil surface temperatures, which allow to discriminate different irrigation treatments.

关键词： Irrigation Temperature measurement Vegetation Temperature sensors Soil Stress Land surface temperature

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Inertial Estimation and Energy-Efficient Control of a Cable-suspended Load with a Team of UAVs

Inertial Estimation and Energy-Efficient Control of a Cable-...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： A. Petitti D. Sanalitro M. Tognon A. Milella J. Cortés A. Franchi Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA) CNR Bari Italy LAAS-CNRS Université de Toulouse CNRS Toulouse France Autonomous Systems Lab ETH Zurich Zürich Switzerland Robotics and Mechatronics lab University of Twente Enschede The Netherlands

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

The Fly-Crane is a multi-robot aerial manipulator system composed of three aerial vehicles towed to a platform by means of six cables. This paper presents a method to estimate the mass and the position of the center of mass of a loaded platform (i.e. the Fly-Crane platform including a transported load). The precise knowledge of these parameters allows to sensibly minimize the total effort exerted during a full-pose manipulation task The estimation is based on the measure of the forces applied by the aerial vehicles to the platform in different static configurations. We demonstrate that only two different configurations are sufficient to estimate the inertial parameters. Far-from-ideal numerical simulations show the effectiveness of the estimation method. Once the parameters are estimated, we show the enhancement of the system performances by minimizing the total exerted effort. The validity of the proposed algorithm in non-ideal conditions is presented through simulations based on the Gazebo simulator.

关键词： Estimation Task analysis Numerical models Robots Vehicle dynamics Trajectory Load modeling

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Obstacle avoidance with reinforcement learning and adaptive resonance theory

Obstacle avoidance with reinforcement learning and adaptive ...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Ye, Lingjian Zhou, Yimin Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China

ISBN: (纸本)9781728163215

The reinforcement learning (RL) of the autonomous mobile agent is one of the actual research topics. It permits mobile agents to interact constantly with their environment and to avoid obstacles. First, this paper presents an algorithm which integrates Deep Deterministic Policy Gradient (DDPG) algorithm and Fuzzy Adaptive Resonance Theory (ART) in order to improve generalization performance of RL. Then the curiosity is introduced to integrate with the first proposed algorithm to solve the problem of slow convergence caused by Fuzzy ART. Results of the simulation experiments demonstrate the effectiveness of the proposed algorithm. It shows that the algorithms perform well in both low-dimension and high-dimension state space. © 2019 IEEE.

关键词： Reinforcement learning

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A multi-task learning convolutional neural network for object pose estimation

A multi-task learning convolutional neural network for objec...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Wang, Yurui Jin, Shaokun Ou, Yongsheng Shenzhen Institutes of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Beijing China University of Chinese Academy of Sciences Beijing China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728163215

Estimating 6D poses of objects from RGB images is very crucial for robots to interact with the surrounding environment and to cooperate with humans. It is a challenging problem due to the various shapes of objects, the occlusions among objects, as well as the complexity of the scene. In this study, we present a new multi-task convolutional neural network for 6D object pose estimation, which also learns the object region in the image to further improve the estimation result. A weighted loss computed from the training samples is adopted to guarantee that the components of the estimated pose are equally accurate. Additionally, we sythesize new virtual data based on the existing training data so as to overcome the possible adverse situation caused by insufficiency of training data. Experiments on the YCB-video dataset are undertaken to validate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Semantic Segmentation

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A method of cliff detection in robot navigation based on multi-sensor

A method of cliff detection in robot navigation based on mul...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Zhilong Su Can Wang Yuxiao Li Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technolog Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

This paper proposed a method to detect cliff edge by using an RGB-D camera, and then to fuse the cliff information with LiDAR, finally to provide navigation information for robot autonomous exploration and autonomous movement. Compared with the prior methods which set sonar or infra-red sensor in the bottom of the robot, our method could detect cliff earlier by set the RGB-D camera forward. In cliff detection, we use region growing algorithm and the normal vector of plane to segment out ground plane in point cloud, then extract the boundary points of ground, and we proposed an Edge Point Filter (EPF) to get the edge points of cliff, then use the edge points to simulate LiDAR data and combine with real LiDAR data to mark the untraversable area. We verified the practicability of this method in simulation, and verified the accuracy with real sensors.

关键词： Laser radar Robots Three-dimensional displays Robot sensing systems Indexes Cameras Image edge detection

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