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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: (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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A DRL-based framework for self-balancing exoskeleton walking

A DRL-based framework for self-balancing exoskeleton walking

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

作者： Yao Dong Yong He Xinyu Wu Guangju Gao Wei Feng A Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

Self-balancing exoskeleton is a robot that can be worn by paraplegic patients for walking without external support. The exoskeleton is similar to humanoid robot with two normal human feet, which cause inconvenience to existing control methods. These model-based methods require mathematical modeling and usually do not consider the impact of the controller like limits of position and torque. This paper proposes a deep reinforce learning (DRL) based training framework for self-balancing exoskeleton, for the purpose of learning a feasible walking policy which can generate walk pattern similar to the given reference motion for paraplegia with exoskeleton. The framework is based on policy gradient descent algorithm, and a way to simplify the model is proposed to speed up model training. The experimental results indicate that this training framework which fully considers the controller can acquire a feasible control policy for self-balancing exoskeleton.

关键词： Cows Robots Real-time systems Conferences

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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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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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Obstacle avoidance of manipulators based on improved artificial potential field method

Obstacle avoidance of manipulators based on improved artific...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Li, Hao Wang, Zhiyang Ou, Yongsheng Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9781728163215

Obstacle avoidance is a key problem of robot motion planning. This paper introduces an improved method to realize obstacle avoidance of manipulators based on artificial potential field method which offers a relatively fast and effective way to solve the problem. We establish the attractive potential field function of the goal and the repulsive potential filed functions of obstacles in the workspace instead of the configuration space. Then a series of control points on the manipulator are chosen to be influenced by the total potential. A policy is proposed that only the end point is attracted to the goal while all selected points are repulsed by obstacles. Afterwards, we transfer the potential force of each point in workspace into the moment in configuration space to make the manipulator move by iteration of joint angles. The effectiveness of the method is confirmed by simulation experiments. © 2019 IEEE.

关键词： Motion planning

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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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Movement Control and Attitude Adjustment of Climbing Robot on Flexible Surfaces

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2618-2628页

作者： Wu, Xinyu Liu, Jia Zhou, Yimin Lv, Qin Hu, Chaofang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and 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-Synergy Systems and 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-Synergy Systems and 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-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Electrical and Information Engineering Tianjin University Tianjin China

In this paper, a series of control algorithms have been designed for the climbing cloth robot, "Clothbot" to climb vertically. The developed algorithm consists of three parts: a sliding mode control with integral action (SMC with integral action), a linear quadratic regulator, and dead reckoning for semi-autonomous navigation. First, the SMC with integral action is developed for high-precision position tracking and speed tracking of the permanent magnet direct current (PMDC) motor installed on the robot. The error between the expected velocity and the actual velocity of the PMDC motor is adopted to design the sliding surface. In order to compensate the fluctuation of the load torque due to the varied friction during climbing, an observer is formulated to estimate the load torque in real-time. Second, the LQR is employed for attitude adjustment by an active torque so as to prevent the robot from falling or being jammed during the movement. Third, due to the onboard-limited energy, the robot is just equippedwith semi-autonomous navigation named dead reckoning carrying on primary localization and primary path planning. Experiments are performed to verify the efficacy of the proposed control algorithms.

关键词： Climbing robot flexible surface linear quadratic regulator load observer sliding mode control with integral action

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Mobile robot localization based on optical sensor

Mobile robot localization based on optical sensor

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

作者： Liu, Yang Ou, YongSheng Han, WeiChao Wuhan University of Technology Wuhan430070 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781728137261

This paper presents a method of using optical sensor for mobile robot localization, which improves the accuracy of position estimation by using extended Kalman filter to fuse three kinds of sensor data. By installing the optical mouse sensor on the edge of the robot's central axis, the linear and angular speed of the robot can be calculated. These help to solve the problem of wheel-slip and the accumulation error of yaw angle that obtained by low-cost MEMS IMU (Inertial Measurement Unit). An extended Kalman filter method that fuse the data from wheel encoder, IMU and optical mouse sensor is implemented. Eventually, experiments of position estimation were carried out on our own two-wheel drive robot. By comparing the accuracy of fusion algorithm in many ways, it indicated that the location result that of three kinds of sensors is better than that of one or two sensors in traditional way, and the error of position estimation is generally within 0.6%. © 2018 IEEE

关键词： Extended Kalman filters

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