13,656 research outputs found

    Passive Decomposition of Mechanical Systems With Coordination Requirement

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    Balanced Truncation of Networked Linear Passive Systems

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    This paper studies model order reduction of multi-agent systems consisting of identical linear passive subsystems, where the interconnection topology is characterized by an undirected weighted graph. Balanced truncation based on a pair of specifically selected generalized Gramians is implemented on the asymptotically stable part of the full-order network model, which leads to a reduced-order system preserving the passivity of each subsystem. Moreover, it is proven that there exists a coordinate transformation to convert the resulting reduced-order model to a state-space model of Laplacian dynamics. Thus, the proposed method simultaneously reduces the complexity of the network structure and individual agent dynamics, and it preserves the passivity of the subsystems and the synchronization of the network. Moreover, it allows for the a priori computation of a bound on the approximation error. Finally, the feasibility of the method is demonstrated by an example

    A Survey of the Spacecraft Line-Of-Sight Jitter Problem

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    Predicting, managing, controlling, and testing spacecraft Line-of-Sight (LoS) jit- ter due to on-board internal disturbance sources is a challenging multi- disciplinary systems engineering problem, especially for those observatories hosting extremely sensitive optical sensor payloads with stringent requirements on allowable LoS jitter. Some specific spacecraft jitter engineering challenges will be introduced and described in this survey paper. Illustrative examples of missions where dynamic interactions have to be addressed to satisfy demanding payload instrument LoS jitter requirements will be provided. Some lessons learned and a set of recommended rules of thumb are also presented to provide guidance for analysts on where to initiate and how to approach a new spacecraft jitter design problem. These experience-based spacecraft jitter lessons learned and rules of thumb are provided in the hope they can be leveraged on new space system development projects to help overcome unfamiliarity with previously identified jitter technical pitfalls and challenges

    Trajectory generation of space telerobots

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    The purpose is to review a variety of trajectory generation techniques which may be applied to space telerobots and to identify problems which need to be addressed in future telerobot motion control systems. As a starting point for the development of motion generation systems for space telerobots, the operation and limitations of traditional path-oriented trajectory generation approaches are discussed. This discussion leads to a description of more advanced techniques which have been demonstrated in research laboratories, and their potential applicability to space telerobots. Examples of this work include systems that incorporate sensory-interactive motion capability and optimal motion planning. Additional considerations which need to be addressed for motion control of a space telerobot are described, such as redundancy resolution and the description and generation of constrained and multi-armed cooperative motions. A task decomposition module for a hierarchical telerobot control system which will serve as a testbed for trajectory generation approaches which address these issues is also discussed briefly

    Engineering Crowdsourced Stream Processing Systems

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    A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

    ๋ถ„์‚ฐ ์ œ์•ฝํ•˜์—์„œ ์›๊ฒฉ ์ œ์–ด๋˜๋Š” ๋‹ค์ˆ˜์˜ ๋…ผํ™€๋กœ๋…ธ๋ฏน ์ด๋™ํ˜• ๋กœ๋ด‡ ๋Œ€ํ˜• ์žฌ๊ตฌ์„ฑ ์ œ์–ด

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2019. 2. ์ด๋™์ค€.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ณ€ํ™”ํ•˜๋Š” ์ฃผํ–‰ ํ™˜๊ฒฝ์—์„œ ๋ถ„์‚ฐ ์ œ์•ฝ ํ•˜์— ๋‹ค์ˆ˜์˜ ์›๊ฒฉ์œผ๋กœ ์ œ์–ด๋˜๋Š” ๋…ผํ™€๋กœ๋…ธ๋ฏน ์ด๋™ํ˜• ๋กœ๋ด‡ ๋Œ€ํ˜• ์žฌ๊ตฌ์„ฑ ์ œ์–ด์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ์„ผ์‹ฑ๊ณผ ์ปดํ“จํŒ… ๋Šฅ๋ ฅ์ด ๊ฐ–์ถ”์–ด์ง„ ์˜จ๋ณด๋“œ ์‹œ์Šคํ…œ ๋กœ๋ด‡๋“ค์„ ํ™œ์šฉํ•˜์—ฌ ์ตœ๊ทผ ๊ฐœ๋ฐœ๋œ ์˜ˆ์ธก ๋””์Šคํ”Œ๋ ˆ์ด ๊ธฐ๋ฒ•์„ ์ ์šฉ, ํšจ์œจ์ ์ธ ๊ตฐ์ง‘ ๋กœ๋ด‡์˜ ์›๊ฒฉ ์ œ์–ด๊ฐ€ ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ์ž˜ ์•Œ๋ ค์ง„ ๋…ผํ™€๋กœ๋…ธ๋ฏน ํŒจ์‹œ๋ธŒ ๋””์ปดํฌ์ง€์…˜ ๊ธฐ๋ฒ•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋Œ€ํ˜• ๋ณ€๊ฒฝ์ด ๊ฐ€๋Šฅํ•˜๋„๋ก ์ƒˆ๋กœ์šด ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ถ”๊ฐ€, ๋Œ€ํ˜• ๋ณ€๊ฒฝ๊ฐ„ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌธ์ œ๋“ค์— ๋Œ€ํ•ด ํŒŒ์•…ํ•˜๊ณ  ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ํฌํ…์…œ ํ•„๋“œ๋ฅผ ํ™œ์šฉํ•˜์˜€๋‹ค. n๋Œ€์˜ ๋กœ๋ด‡์œผ๋กœ ๋‹ค์–‘ํ•œ ๋Œ€ํ˜• ๋ณ€๊ฒฝ์ด ๊ฐ€๋Šฅํ† ๋ก ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ™˜๊ฒฝ์„ ์กฐ์„ฑ, 39๋Œ€์˜ ํƒฑํฌ๋ฅผ ์ด์šฉํ•˜์—ฌ์—ฌ 5๊ฐ€์ง€์˜ ๊ฐ๊ธฐ ๋‹ค๋ฅธ ๋Œ€ํ˜•์œผ๋กœ์˜ ๋ณ€ํ™˜์„ ์ƒˆ๋กœ์ด ์ œ์‹œํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜์—ฌ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์‹ค์ œ ๋กœ๋ด‡ 3๋Œ€๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํšจ์šฉ์„ฑ์— ๋Œ€ํ•œ ์‹คํ—˜์„ ํ•„๋‘๋กœ ์ข์€ ๊ธธ๋ชฉ, ๊ฐœํ™œ์ง€ ๋“ฑ ์—ฐ์†์ ์œผ๋กœ ๋ณ€ํ™”ํ•˜๋Š” ํ™˜๊ฒฝ ์†์—์„œ์˜ ๊ตฌ๋™์„ ํ†ตํ•ด ์ตœ์ข…์ ์œผ๋กœ ์ œ์‹œํ•œ ํ”„๋ ˆ์ž„์›Œํฌ์˜ ํƒ€๋‹น์„ฑ์— ๋Œ€ํ•ด ๊ฒ€์ฆํ•˜์˜€๋‹ค.We propose a novel framework for formation reconguration of multiple nonholonomic wheeled mobile robots (WMRs) in the changing driving environment. We utilize an onboard system of WMRs with the capability of sensing and computing. Each WMR has the same computing power for visualizing the driving environment, handling the sensing information and calculating the control action. One of the WMRs is the leader with the FPV camera and SLAM, while others with monocular cameras with limited FoV, as the followers, keep a certain desired formation during driving in a distributed manner. We set two control objectives, one is group driving and the other is holding the shape of the formation. We have to capture the control objectives separately and simultaneously, we make the best use of nonholonomic passive decomposition to split the WMRs' kinematics into those of the formation maintaining and group driving. The repulsive potential function to prevent the collision among WMRs and attractive potential function to restrict the boundary of follower WMRs' moving space due to limited FoV range of the monocular cameras while switching their formation are also used. Simulation with 39 tanks and experiments with three WMRs are also performed to verify the proposed framework.Acknowledgements iii List of Figures vii Abbreviations ix 1 Introduction 1 2 Formation Reconguration Control Design 5 2.1 Nonholonomic Passive Decomposition . . . . . . . . . . . . . . . 5 2.2 Attractive and Repulsive Potential Function . . . . . . . . . . . . 10 2.3 Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3 Estimation and Predictive Display 20 3.1 Distributed Pose Estimation . . . . . . . . . . . . . . . . . . . . . 20 3.1.1 EKF Pose Estimation of Leader WMR . . . . . . . . . . . 20 3.1.2 EKF Pose Estimation of Follower WMRs . . . . . . . . . 22 3.2 Predictive Display for Distributed WMRs Teleoperation . . . . . 23 4 Experiment 27 4.1 Test Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2 Demonstrate the Proposed Algorithm . . . . . . . . . . . . . . . 30 4.3 Teleoperation Experiment with the Algorithm . . . . . . . . . . . 33 5 Conclusion 40Maste
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