Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks

Jin, Long; Li, Shuai; La, Hung Manh; Luo, Xin

doi:10.1109/tie.2017.2674624

Cited by 305 publications

(87 citation statements)

References 34 publications

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“…Tracking control and correction: [164], [174], [175] [167], [180], [182] [184], [186], [190] [191] Collaboration: [179], [187] Mobile Robots Localization: [165], [169], [172] Navigation: [166], [168], [171] [173], [181], [188] Collaboration: [176]- [178], [185] Other operations: [163], [183], [192] to be one of the most efficient control schemes for complex industrial processes due to the difficulty in obtaining accurate mathematical control models [155] and to the frequent existence of nonlinearities and stochastic disturbances [156]. In fact, data delivery latency is among the most active topics in the NCS field recently.…”

Section: Stationary Robotsmentioning

confidence: 99%

“…Energy and power efficient methods have also been presented, for a number of cost functions [180]. Manipulability optimization of redundant manipulators is shown to be achieved through dynamic neural networks [182]. Neural control is also applied in the case of bimanual robots (which are able to perform more complicated tasks that a single manipulator), resulting in guaranteed stability and precision [184], or in reduced vibrations [186].…”

Section: Stationary Robotsmentioning

confidence: 99%

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Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: Stationary Robotsmentioning

confidence: 99%

Section: Stationary Robotsmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

View full text Add to dashboard Cite

show abstract

“…comparison in [12], where it is concluded that the volatility of the manipulability gradient has a significant effect on convergence. The method in [8] primarily focusses on avoiding singularities, and also reaches the lowest manipulability index values.…”

Section: Reaching Taskmentioning

confidence: 99%

Fast Manipulability Maximization Using Continuous-Time Trajectory optimization

Marić

Limoyo

Petrović

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable joint-space trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint space-to-task space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous control-based approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint space-to-task space motion mappings in various scenarios. By representing the manipulator trajectory as a continuous-time Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations.

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“…In [33], based on control perspective and projection neural networks, the authors researched distributed task allocation problem of multiple robots. Utilizing projection neural networks, literature [34] researches manipulability optimization problem of redundant manipulators. These new applications in robotics extend new application fields for projection neural network.…”

Section: Application To Compressedmentioning

confidence: 99%

Robust Linear Neural Network for Constrained Quadratic Optimization

Liu

Xiong

2017

Discrete Dynamics in Nature and Society

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Based on the feature of projection operator under box constraint, by using convex analysis method, this paper proposed three robust linear systems to solve a class of quadratic optimization problems. Utilizing linear matrix inequality (LMI) technique, eigenvalue perturbation theory, Lyapunov-Razumikhin method, and LaSalle's invariance principle, some stable criteria for the related models are also established. Compared with previous criteria derived in the literature cited herein, the stable criteria established in this paper are less conservative and more practicable. Finally, a numerical simulation example and an application example in compressed sensing problem are also given to illustrate the validity of the criteria established in this paper.

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Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks

Cited by 305 publications

References 34 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

Fast Manipulability Maximization Using Continuous-Time Trajectory optimization

Robust Linear Neural Network for Constrained Quadratic Optimization

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