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IEEE International Conference on Control Science and systems Engineering (CCSSE)

作者： Jianfei Cao Yaobing Wang Jiyang Yu Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

The development of lunar rovers is critical for human exploration of the lunar surface. These rovers will need to travel longer distances at higher speeds, and the efficient control of their paths is essential for mission success. Model Predictive Control (MPC) is a path tracking method with foresight and robustness, capable of predicting vehicle movement and solving optimization problems to minimize cost functions. However, the complexity of lunar environments necessitates simplified models, such as kinematic models, to reduce computational cost. The study proposes a special MPC method that uses zeroth-order gradient optimization to directly solve the nonlinear model and generate precise control commands, significantly reducing computational burden and meeting the computational cost requirements of lunar rover control.

关键词： space vehicles Tracking Computational modeling Moon Optimization methods Kinematics Predictive models Robustness Computational efficiency Predictive control

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Hierarchical Knowledge Representation of Complex Tasks Based on Dynamic Motion Primitives 7th

Hierarchical Knowledge Representation of Complex Tasks Based...

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7th International Conference on Cognitive systems and Information Processing, ICCSIP 2022

作者： Miao, Shengyi Zhong, Daming Miao, Runqing Sun, Fuchun Wen, Zhenkun Huang, Haiming Zhang, Xiaodong Wang, Na College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Modern Post Beijing University of Posts and Telecommunications Beijing100083 China Department of Computer Science and Technology Tsinghua University Beijing100083 China College of Computer Science and Software Shenzhen University Shenzhen518060 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Application Beijing100083 China

ISBN: (纸本)9789819906161

Current service robots without learning ability are not qualified for many complex tasks. Therefore, it is very significant to decompose the complex task into repeatable execution unit. In this paper, we propose a complex task representation method based on dynamic motion primitives, and use hierarchical knowledge graph to represent the analytic results of complex tasks. To realize the execution of complex robot manipulation tasks, we decompose the semantic tasks into the minimum motion units that can be executed by the robot and combine the multi-modal information: posture, force and robot joint parameters, obtained by the sensors. We use the knowledge graph to record the end-effector required by the robot to perform different tasks and make appropriate selection of end-effector according to different needs. Finally, Taking the long sequence complex task of service scene as an example, we use UR5 robot to verify the effectiveness and feasibility of this design. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： End effectors

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Globally optimal consensus maximization for robust visual inertial localization in point and line map

arXiv

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arXiv 2020年

作者： Jiao, Yanmei Wang, Yue Fu, Bo Tan, Qimeng Chen, Lei Wang, Minhang Huang, Shoudong Xiong, Rong State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China Application Lab Huawei Incorporated Company China University of Technology Sydney Australia

Map based visual inertial localization is a crucial step to reduce the drift in state estimation of mobile robots. The underlying problem for localization is to estimate the pose from a set of 3D-2D feature correspondences, of which the main challenge is the presence of outliers, especially in changing environment. In this paper, we propose a robust solution based on efficient global optimization of the consensus maximization problem, which is insensitive to high percentage of outliers. We first introduce translation invariant measurements (TIMs) for both points and lines to decouple the consensus maximization problem into rotation and translation subproblems, allowing for a two-stage solver with reduced search space. Then we show that (i) the rotation can be estimated by minimizing TIMs using only 1-dimensional branch-and-bound (BnB), (ii) the translation can be estimated by running 1-dimensional search for each of the three axes with prioritized progressive voting. Compared with the popular randomized solver, our solver achieves deterministic global convergence without requiring an initial value. Furthermore, ours is exponentially faster compared with existing BnB based methods. Finally, our experiments on both simulation and real-world datasets demonstrate that the proposed method gives accurate pose estimation even in the presence of 90% outliers (only 2 inliers). Copyright © 2020, The Authors. All rights reserved.

关键词： Statistics

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Compound Asynchronous Exploration and Exploitation

Compound Asynchronous Exploration and Exploitation

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2019 2nd International Conference on Mechanical,Electronic and Engineering technology(MEET 2019)

作者： Jie Bai Li Liu Yaobing Wang Haoyu Zhang Jianfei Li Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering College of Engineering Peking University Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering

Data efficiency has always been a significant key topic for deep reinforcement learning. The main progress has been on sufficient exploration and effective exploitation. However, the two are often discussed separately. Profit from distributed systems, we propose an asynchronous approach to deep reinforcement learning by combining exploration and exploitation. We apply our framework to off-the-shelf deep reinforcement learning algorithms, and experimental results show that our algorithm is superior in final performance and efficiency.

关键词： Deep Reinforcement Learning Exploration and Exploitation Asynchronous Methods

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Figure-ground Image Segmentation via Semantic Information

Figure-ground Image Segmentation via Semantic Information

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

作者： Ding Yuan Jingjing Qiang Jianfei Li Hong Zhang Xiaoyan Luo School of Astronautics Beihang University Beijing China 802 Institute of Shanghai Academy of Space Flight Technology Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Institute of Spacecraft System Engineering CAST

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Image segmentation is a technique to segment the image into particular regions which are consistent in some specific characteristics. It is still a non-trivial task to segment the foreground from the background completely, as these regions are seldom uniform in color or texture which are defined as commonly-used low-level features adopted in the traditional techniques. In this work we present a new segmentation framework by employing the semantic information, which is classified as the high-level visual features and holds important clue to indicate the foreground from the background. Firstly, the image is segmented hierarchically by using the semantic edge constraint, and the result is represented as an Ultrametric Contour Map (UCM). Then the UCM is employed in the segmentation energy function as the data term and smoothness term. Finally, the optimal segmentation labels are obtained via the Graph-cut. Experiments achieve good performance testing on the MSRC 21 dataset.

关键词： Semantics Image segmentation Image edge detection Labeling Image color analysis Microsoft Windows Support vector machines

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Design and Validation of Maintenance system for space Manipulator Grapple Fixture

Design and Validation of Maintenance System for Space Manipu...

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International Conference on Reliability, Maintainability and Safety,ICRMS

作者： Lei Zeng Ben Gao Chao Zhu Chengwei Hu Minghua Xiong Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China Tianjin Institute of Aerospace Mechanical and Electrical Equipment Tianjin China

ISBN: (数字)9798350329988

ISBN: (纸本)9798350329995

The space manipulator plays an integral role in the assembly and construction of the China space station. The grapple fixture is positioned at designated locations on the space station as the foundation for the space manipulator. It is utilized in conjunction with the space manipulator to capture target objects and complete crawling tasks. The performance of the space manipulator is directly impacted by the grapple fixture, and a failure of the grapple fixture can even jeopardize the space station mission. This paper analyzed the maintenance requirements of the grapple fixture, proposed a maintenance system for it, and executed replacement tasks with the assistance of maintenance tools and astronauts in orbit. The feasibility of this approach is verified through ground testing and simulation analysis. The results will serve as a valuable reference and engineering guidance for future space manipulator maintenance.

关键词： space vehicles space missions Fixtures Manipulators Reliability engineering Orbits Maintenance Safety space stations Testing

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Trajectory Planning Simulation of 3-DoF Parallel Wrist Mechanisms Based on SimMechanics

Trajectory Planning Simulation of 3-DoF Parallel Wrist Mecha...

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IEEE International Conference on Cyber technology in Automation, Control, and intelligent systems

作者： Chen Yang Liu Xin Wei Shimin Zhang Ying Huang Qineng School of Automation Beijing University of Posts and Telecommunications Beijing PR China Beijing Institute of Spacecraft System Engineering Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing PR China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

In this paper, the trajectory planning problem of a 3-DoF parallel wrist mechanism based on SimMechanics is proposed, which is a symmetric spherical 3-RRR parallel mechanism. The formulation of the inverse kinematics for the mechanism is firstly established by D-H matrix method, and the analytical solution is obtained. Then, the trajectory planning analysis of the mechanism is performed. Finally, the mechanism model is constructed based on the SimMechanics module of Matlab/Simulink, and the trajectory planning simulation of the mechanism is carried out. The simulation results agree well with the theoretical results, which indicate that the proposed method is feasible and convenient for the trajectory simulation of parallel mechanisms.

关键词： Kinematics Trajectory Conferences Automation intelligent systems Wrist

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Learning a Faster Locomotion Gait for a Quadruped Robot with Model-Free Deep Reinforcement Learning

Learning a Faster Locomotion Gait for a Quadruped Robot with...

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IEEE International Conference on robotics and Biomimetics

作者： Biao Hu Shibo Shao Zhengcai Cao Qing Xiao Qunzhi Li Chao Ma College of Information Science and Technology Beijing University of Chemical Technology Beijing China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China

Quadruped robots have great agility, flexibility and stability, which enables them to walk through uneven terrain. Motion control of legged robots is always a difficult problem. Previous approaches mostly either use a predefined gait that results in clumsy and unnatural behavior, or use reinforcement learning approach to generate a gait strategy that needs longtime computation and elegant network design. In this paper, we present an effective approach that uses deep reinforcement learning with prior knowledge to optimize the gait of quadruped robot. By using a specific quadruped robot walking gait as a priori knowledge, this approach adopts the technique of distributed proximal policy optimization to optimize the search for better gait. The proposed approach does not require modelling of complex robots, and has good network convergence speed and learning effect. Simulation results demonstrate that our proposed approach converges faster than other deep reinforcement learning methods without prior knowledge. Besides, our achieved gait has higher speed that is 50% faster than the trot gait without optimization.

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key Techniques on Unified Identity Authentication in OpenMBEE Integration 4

Key Techniques on Unified Identity Authentication in OpenMBE...

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4th IEEE International Conference on Electronics and Communication Engineering, ICECE 2021

作者： Xue, Junjie Zhou, Junhua Shi, Guoqiang Feng, Chaoqun Liu, Penghua Quan, Hongyan Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China Institute of Electronic System Engineering Beijing China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9781728194226

In order to make full use of the concept of model-based systems engineering (MBSE) and the collaborative design capability of the open model-based engineering environment (OpenMBEE) in establishing complex product development platform, we propose an effective method to solve the problem of unified identity authentication in OpenMBEE integration. The proposed method is based on the Model View Controller(MVC) architecture in OpenMBEE, which has the characteristics strong plasticity and can be benefit for expanding the function in the controller layer. In our research, we make full use of the Software Development Kit (SDK) of OpenMBEE to expand the existing functions in the controller layer, and increase the function of communicating with the application development platform, then realize the function of sharing user authentication information between the development platform and OpenMBEE. After expansion, the system includes three modules, including the Front-end Service Module(FSM), MQ based Information Receiving Service Module(IFRSM), and the OpenMBEE Back-end Server Module(BSM). The effectiveness of the proposed strategies is verified by some practical instances, which verifies that our study can provide an effective design idea for the identity authentication in OpenMBEE integration. ©2021 IEEE

关键词： Integration

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Semantic and Neighborhood Preserving Dictionar Learning for Symmetric Positive-Definite Matrice

Semantic and Neighborhood Preserving Dictionar Learning for ...

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Daming Li Lei Chen Fei Wang The School of Electronic and Information Engineering Xi’an Jiaotong University Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering

The symmetric positive-definite(SPD) matrices form a Riemannian manifold, and sparse representation on the SPD matrix manifold has received increasingly attentions recently. In this paper, we propose a semantic and neighborhood preserving dictionary learning method for the sparse representation on the SPD matrix manifold. The semantic preserving ensures that the semantic similarity information can be kept while sparse representation, and the neighborhood preserving is introduced to better reflect the geometrical structure of the manifold. To effectively model the semantic similarity of the SPD matrices and the geometrical structure, a Laplacian smooth operator is incorporated into the objective function of dictionary *** the learned dictionary, the obtained sparse representations of SPD matrices are more discriminative. Experimental results on publicly available datasets demonstrate the effectiveness of the proposed method.

关键词： Dictionary learning symmetric positive-definite matrix sparse representation Riemannian manifold

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