Artificial neural network-based kinematics Jacobian solution for serial manipulator passing through singular configurations

Hasan, Ali T.; Ismail, Nafeeza Mohd; Hamouda, A.M.S.; Aris, Ishak; Marhaban, Mohammad Hamiruce; Al-Assadi, Hayder M. A. Ali

doi:10.1016/j.advengsoft.2009.06.006

Cited by 84 publications

(38 citation statements)

References 27 publications

(32 reference statements)

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“…Martín, Lope, and Santos (2007) proposed a method to learn the IK of multi-link manipulators by evolving neuro-controllers, validated both over a 3 DoF planar manipulator and over a SCARA robot; for each experiment, the authors used two different adaptation methods: the covariance matrix adaptation evolution strategy (CMA-ES) and neuro-evolution of augmenting topologies (NEAT). Finally, (Hasan et al, 2010) presented a solution of the kinematics Jacobian of a 6 DoF manipulator using a fully connected feed forward ANN with one hidden layer that departing from the Cartesian position, orientation (given as Euler angles) and linear velocity of the end-effector calculates both the angular position of every joint and their corresponding angular velocities.…”

Section: Related Workmentioning

confidence: 99%

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Pérez‐Rodríguez

Marcano-Cedeño

Costa

et al. 2012

Expert Systems with Applications

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Objective: This research is focused in the creation and validation of a solution to the inverse kinematics problem for a 6 degrees of freedom human upper limb. This system is intended to work within a realtime dysfunctional motion prediction system that allows anticipatory actuation in physical Neurorehabilitation under the assisted-as-needed paradigm. For this purpose, a multilayer perceptron-based and an ANFIS-based solution to the inverse kinematics problem are evaluated. Materials and methods: Both the multilayer perceptron-based and the ANFIS-based inverse kinematics methods have been trained with three-dimensional Cartesian positions corresponding to the end-effector of healthy human upper limbs that execute two different activities of the daily life: 'serving water from a jar' and 'picking up a bottle'. Validation of the proposed methodologies has been performed by a 10 fold cross-validation procedure. Results: Once trained, the systems are able to map 3D positions of the end-effector to the corresponding healthy biomechanical configurations. A high mean correlation coefficient and a low root mean squared error have been found for both the multilayer perceptron and ANFIS-based methods. Conclusions:The obtained results indicate that both systems effectively solve the inverse kinematics problem, but, due to its low computational load, crucial in real-time applications, along with its high performance, a multilayer perceptron-based solution, consisting in 3 input neurons, 1 hidden layer with 3 neurons and 6 output neurons has been considered the most appropriated for the target application.

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Section: Related Workmentioning

confidence: 99%

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Pérez‐Rodríguez

Marcano-Cedeño

Costa

et al. 2012

Expert Systems with Applications

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show abstract

“…Besides, there are always kinematics uncertainties present in the real world such as ill-defined linkage parameters and backlashes in gear trains [26,27]. In this article, and to overcome whichever uncertainty presented in the real world, data were recorded experimentally from sensors fixed on each joint for a horizontal two-link under-actuated robot.…”

Section: Introductionmentioning

confidence: 99%

Development of a real-time-position prediction algorithm for under-actuated robot manipulator by using of artificial neural network

Al-Assadi

Isa

Hasan

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part C:

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An adaptive learning algorithm using an artificial neural network (ANN) has been proposed to predict the passive joint position of under-actuated robot manipulator. In this approach, a specific ANN model has been designed and trained to learn a desired set of joint angular positions for the passive joint from a given set of input torque and angular position for the active joint over a certain period of time. Trying to overcome the disadvantages of many used techniques in the literature, the ANNs have a significant advantage of being a model-free method. The learning algorithm can directly determine the position of its passive joint, and can, therefore, completely eliminate the need for any system modelling. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot's joints to overcome the effect of kinematics uncertainties present in the real world such as ill-defined linkage parameters and backlashes in gear trains. An ANN was trained using the experimentally obtained data and then used to predict the path of the passive joint that is positioned by the dynamic coupling of the active joint. The generality and efficiency of the proposed algorithm are demonstrated through simulations of an under-actuated robot manipulator; finally, the obtained results were successfully verified experimentally.

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“…In real world application, no physical property such as the friction coefficient can be exactly derived. Besides, there are always kinematics uncertainties presence in the real world such as ill-defined linkage parameters and backlashes in gear trains (Hasan et al, 2009;Hasan et al, 2010). In this paper, and to overcome whichever uncertainty presented in the real world, data were recorded experimentally from sensors fixed on each joint for a horizontal two-link under-actuated robot.…”

Section: Introductionmentioning

confidence: 99%

Real-Time-Position Prediction Algorithm for Under-Actuated Robot Manipulator Using of Artificial Neural Network

Isa¹,

Al-Assadi²,

Hasan³

2010

Advanced Strategies for Robot Manipulators

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Artificial neural network-based kinematics Jacobian solution for serial manipulator passing through singular configurations

Cited by 84 publications

References 27 publications

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Inverse kinematics of a 6 DoF human upper limb using ANFIS and ANN for anticipatory actuation in ADL-based physical Neurorehabilitation

Development of a real-time-position prediction algorithm for under-actuated robot manipulator by using of artificial neural network

Real-Time-Position Prediction Algorithm for Under-Actuated Robot Manipulator Using of Artificial Neural Network

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