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International Conference on advanced robotics and Mechatronics (ICARM)

作者： Yingbai Hu Guang Chen Longbin Zhang Hang Su Mengyao Li Yunus Schmirander Hu Cao Alois Knoll Chair of Robotics Artificial Intelligence and Realtime Systems Technical University of Munich Munich Germany College of Automotive Engineering Tongji University Shanghai China KTH MoveAbility Lab KTH Royal Institute of Technology Stockholm Sweden Politecnico di Milano Milano Italy Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Dynamic Movement Primitives (DMPs) framework is a powerful approach to imitate motor skills, which has outstanding characteristics, such as convergence to the goal position and good imitation performance. Considering complex motion scenes of manipulators, such as changing the goal position or adding obstacles, the original DMPs framework is not sufficient for the requirements. In this paper, we propose a learning control-based hierarchical control strategy to adapt to new goal positions and avoid obstacles: the high-level learning scheme is targeted at imitating the motor skill and generating the optimization trajectory for obstacle avoidance; the lower-level control scheme focuses on the safety and stability of the robot's movement with unknown disturbances. Firstly, the enhanced DMPs framework is presented to imitate the trajectory from human demonstrations, where the novel DMPs can adapt to new goal position with the changing goal, and avoid single or multiple obstacles. Then, the fuzzy adaptive control method is employed to control redundant manipulators, where the fuzzy logic system (FLS) is incorporated to approximate an unknown nonlinear function term of the unknown disturbance. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with simulation results. The results show that the developed hierarchical strategy has good performance for new goal adaptation and obstacle avoidance.

关键词： Collision avoidance Trajectory Manipulator dynamics Nonlinear dynamical systems Adaptive control

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Dynamic obstacle tracking based on high-definition map in urban scene

Dynamic obstacle tracking based on high-definition map in ur...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Li, Yuxiao Wang, Can Su, Zhilong Duan, Shengcai Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728163215

The application of High-Definition map can realize centimeter-level position in urban scenes, the development of deep learning has made great breakthrough in the point cloud dynamic obstacle recognition. All these technologies make obstacle detection and tracking based on High-Definition map effectively realize in the modern smart city scene. It is different from the previous obstacle detection and tracking methods based purely on vision the using of High-Definition map can provide high-precision positioning and reduce the difficulty of point cloud classification. The using of lidar also solves the problem of up-to-scale in dynamic detection. In this paper, we put forward a new Multi-camera Lidar Point Cloud Map, we complete the map at normal speed on the highway and get a satisfactory result. At the same time, we also find a robust combination of traditional kalman filter, Hungary algorithm and current deep learning to solve dynamic obstacle tracking and detection. The experimental results show that our system can effectively complete the special problem of generating map and target tracking on urban scene. © 2019 IEEE.

关键词： Optical radar

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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LED Visible Light Localization Based on Illuminance Analysis

LED Visible Light Localization Based on Illuminance Analysis

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

作者： Chenting Gu Xiaofeng Liu Yongsheng Ou Chao Liu Hao Tao Tianyun Shi HoHai University Nanjing China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen China Key Laboratory of Special Robots Changzhou China Changzhou Key Laboratory of Robots and Intelligent Technology Changzhou China College of IoT Engineering Hohai University Changzhou China Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China China Ship Development and Design Center Wuhan Tian-yun Shi China Academy of Railway Sciences Corporation Limited Beijing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

LED based positioning technology has recently become a popular choice due to the low cost, convenience for maintenance and strong adaptability to the environment. In this paper, we introduce an illuminance-distance analysis method with a rolling shutter camera, for a low-cost indoor positioning system to improve robustness in the case of insufficient LEDs. Firstly, the binary threshold method is used to decode the light signals. Then the gray value information of image is extracted to analyze the depth of LEDs, using the illuminance attenuation theorem. It provides potential solution to the position of devices with the camera by combining depth information, image and camera intrinsic. Our proposed method reduced the minimum number of LEDs observed by the camera from three to two, using only the camera. We evaluated the proposed method with real scene data and demonstrated the effectiveness of illumination-distance model based localization.

关键词： Light emitting diodes Cameras Receivers Light sources Robots Mathematical model Image sensors

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Visual SLAM based on geometric cluster matching

Visual SLAM based on geometric cluster matching

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Tian, Chaoran Ou, Yongsheng Qu, Yangyang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University OfChinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes OfAdvanced Technology Chinese Academy OfSciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781728137261

The approach of feature-based visual SLAM is very popular in Micro Aerial Vehicle (MAV) navigation and wheeled mobile robot (WMR) navigation. Most modern feature-based visual SLAM frameworks extract a fixed number of feature points and then match the feature points by brute force or adopt Fast Library for Approximate Nearest Neighbors (FLANN). The features' extracting and matching processes will determine the localization accuracy and mapping quality significantly. In this paper, we propose a feature matching algorithm for the robot equipped with a binocular camera, which provides accurate real-time localization performance and robustness. In our feature matching method, both the geometric relationship of image feature points and the similarity of descriptor are considered. This paper presents a visual SLAM framework based on geometric cluster matching and the effectiveness of the proposed method is verified by practical experiments. © 2019 IEEE.

关键词： Mobile robots

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Corrigendum to “SIAT-WEXv2: A Wearable Exoskeleton for Reducing Lumbar Load during Lifting Tasks”

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Complexity 2021年第1期2021卷

作者： Xinyu Ji Dashuai Wang Pengfei Li Liangsheng Zheng Jianquan Sun Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen *** CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen *** Wuhan University of Technology Wuhan *** Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

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Walking Control of Biped Robots on Uneven Terrains Based on SLIP Model

Walking Control of Biped Robots on Uneven Terrains Based on ...

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IEEE Workshop on advanced robotics and its Social Impacts, ARSO

作者： Xiang Meng Zhangguo Yu Gao Huang Xuechao Chen Weijie Qi Qiang Huang Bo Su School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robotics and Systems Beijing Institute of Technology Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing Advanced Innovation Center for Intelligent Robotics and Systems Beijing Institute of Technology Beijing China China North Vehicle Research Institute Beijing China

Having human-like legged structure, biped robots are regarded as the most promising robots that can take the place of human to work in the environments designed for human beings. To this end, it is the premise for biped robots to achieve stable walking on uneven ground. This paper presents a control strategy for a biped robot based on spring-loaded inverted pendulum (SLIP) model, which controls the joint torque of the robot by obtaining the errors of length and angular position of virtual spring legs. Additionally, this paper proposes a new control method of adjusting the length of legs to enable the robot to achieve stable walking on unstructured terrains, such as staircases. In the end, the simulation results corroborate the effectiveness of the proposed approaches.

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Development of an Adaptive Prosthetic Hand *

Development of an Adaptive Prosthetic Hand *

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IEEE International Conference on robotics and Biomimetics

作者： Xu Yong Xiaobei Jing Xinyu Wu Jiang Jiang Hiroshi Yokoi Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences (CAS) CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Department of Mechanical Engineering and Intelligent Systems The University of Electro-communications

Inspired by biomechanics, an adaptive prosthetic hand which not only considered the function of the motion of the digits but also the palm, is presented in this paper. The human hand consists of different kinds of bones, the phalanges in the digits and the metacarpals in the palm, which cooperate to perform adaptable and dexterous motions. Many existent prosthetic hands focus mainly on the movements of the digits, ignoring the palm. The proposed hand has 15 movable joints in the fingers and the thumb, and 2 movable metacarpals in the palm. The thumb and the four fingers are able to flex independently and adaptively wrap along with the shape of the object. The palm could perform an adaptive holding to some extent through the grasping process, due to the movable metacarpals. Furthermore, the hand design is also considered the weight and size limitations for being used as a prosthesis.

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