QP-based refined manipulability-maximizing scheme for coordinated motion planning and control of physically constrained wheeled mobile redundant manipulators

Zhang, Yunong; Yan, Xiaogang; Chen, Dechao; Guo, Dongsheng; Li, Weibing

doi:10.1007/s11071-016-2681-9

Cited by 72 publications

(45 citation statements)

References 42 publications

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“…7: Experiment on real manipulator. The left image shows a trajectory generated using [11], while the image on the right shows a trajectory generated by our method. Fig.…”

Section: Discussionmentioning

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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“…7: Experiment on real manipulator. The left image shows a trajectory generated using [11], while the image on the right shows a trajectory generated by our method. Fig.…”

Section: Discussionmentioning

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)

View full text Add to dashboard Cite

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“…More detailed research by utilizing the PDNNs with application to robot autonomy can also be refereed in the state-of-the-art works. [155][156][157]…”

Section: Pdnn In Robot Autonomymentioning

confidence: 99%

A Review on Neural Dynamics for Robot Autonomy

Chen

2018

IJRC

Self Cite

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Exploiting neural networks to solve control problems of robots is becoming commonly and effectively in academia and engineering. Due to the remarkable features like distributed storage, parallelism, easy implementation by hardware, adaptive self-learning capability, and free of off-line training, the solutions of neural networks break the bottlenecks of serial-processing strategies and methods, and serve as significant alternatives for robotic engineers and researchers. Especially, various types and branches of recurrent neural networks (RNNs) have been sequentially developed since the seminal works by Hopfield and Tank. Successively, many classes and branches of RNNs such as primal-dual neural networks (PDNNs), zeroing neural networks (ZNNs) and gradient neural networks (GNNs) are proposed, investigated, developed and applied to the robot autonomy. The objective of this paper is to present a comprehensive review of the research on neural networks (especially RNNs) for control problems solving of different kinds of robots. Specifically, the state-of-the-art research of RNNs, PDNNs, ZNNs and GNNs in different robot control problems solving are detailedly revisited and reported. The readers can readily find many effective and valuable solutions on the basis of neural networks for the robot autonomy in this paper.

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“…The second approach, discussed in the works [15][16][17][18][19][20][21][22][23][24][25][26], is based on the application of the (generalised) pseudo-inverse of the mobile manipulator Jacobian matrix in the control formulation. Control algorithms developed from the pseudo-inverse of the Jacobian matrix are attractive and further examined by many researchers; they also have some disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Optimal cascaded control of mobile manipulators

Galicki

2019

Nonlinear Dyn

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In this study, the solution to the kinematically optimal control problem of the mobile manipulators is proposed. Both dynamic equations are assumed to be uncertain, and globally unbounded disturbances are allowed to act on the mobile manipulator when tracking the trajectory by the end effector. We propose a computationally efficient class of cascaded control algorithms, which are based on an extended Jacobian transpose matrix. Our controllers involve two new nonsingular terminal sliding mode manifolds defined by nonlinear integral equalities of both the second order with respect to the task space tracking error and the first order with respect to reduced mobile manipulator acceleration. Using the Lyapunov stability theory, we prove that the proposed Jacobian transpose cascaded control schemes are finite time stable provided that some practically reasonable assumptions are fulfilled during the mobile manipulator movement. The numerical examples carried out for mobile manipulators [consisting of a non-holonomic platform of type (2, 0) and holonomic manipulators of 2 and 3 revolute kinematic pairs], which operate in two-dimensional and threedimensional work spaces, respectively, illustrate both the trajectory tracking performance of the proposed control schemes and simultaneously their minimising property for some practically useful objective function.

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QP-based refined manipulability-maximizing scheme for coordinated motion planning and control of physically constrained wheeled mobile redundant manipulators

Cited by 72 publications

References 42 publications

Fast Manipulability Maximization Using Continuous-Time Trajectory optimization

Fast Manipulability Maximization Using Continuous-Time Trajectory optimization

A Review on Neural Dynamics for Robot Autonomy

Optimal cascaded control of mobile manipulators

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