An adaptive-learning algorithm to solve the inverse kinematics problem of a 6 D.O.F serial robot manipulator

Hasan, Ali T.; Hamouda, A.M.S.; Ismail, Nafeeza Mohd; Al-Assadi, Hayder M. A. Ali

doi:10.1016/j.advengsoft.2005.09.010

Cited by 88 publications

(77 citation statements)

References 12 publications

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“…The use of an ANN to command the electromechanical structure turned out to be appropriate to solve the inverse kinematics. [15][16][17][18] The ADLs analyzed in this article were the following: Opening a door, drinking water, pouring water from a pitcher, answering the phone, and shaking hands. Some of these activities have been analyzed by other authors.…”

Section: Methodsmentioning

confidence: 99%

“…The purpose of using an ANN was to model the prosthesis workspace instead of specifying an explicit kinematics model. Hasan et al used a network to model the inverse kinematics of robotic systems 15 and their results showed an excellent mapping over the workspace of the robot. As a training method, the iterative algorithm of Levenberg-Marquardt was selected.…”

Section: Motion Patterns Using Artificial Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Active Upper Limb Prosthesis Based on Natural Movement Trajectories

Ramirez-Garcia¹,

Leija²,

Muñoz³

2010

Prosthetics &Amp; Orthotics International

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The motion of the current prostheses is sequential and does not allow natural movements. In this work, complex natural motion patterns from a healthy upper limb were characterized in order to be emulated for a trans-humeral prosthesis with three degrees of freedom at the elbow. Firstly, it was necessary to define the prosthesis workspace, which means to establish a relationship using an artificial neural network (ANN), between the arm-forearm (3-D) angles allowed by the prosthesis, and its actuators length. The 3-D angles were measured between the forearm and each axis of the reference system attached at the elbow. Secondly, five activities of daily living (ADLs) were analyzed by means of the elbow flexion (EF), the forearm prono-supination (FPS) and the 3-D angles, from healthy subjects, by using a video-based motion analysis system. The 3-D angles were fed to the prosthesis model (ANN) in order to analyze which ADLs could be emulated by the prosthesis. As a result, a prosthesis kinematics approximation was obtained. In conclusion, in spite of the innovative mechanical configuration of the actuators, it was possible to carry out only three of the five ADLs considered. Future work will include improvement of the mechanical configuration of the prosthesis to have greater range of motion.

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Section: Methodsmentioning

confidence: 99%

Section: Motion Patterns Using Artificial Neural Networkmentioning

confidence: 99%

Active Upper Limb Prosthesis Based on Natural Movement Trajectories

Ramirez-Garcia¹,

Leija²,

Muñoz³

2010

Prosthetics &Amp; Orthotics International

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“…until for each input vector the output vector produced by the network is the same as (or sufficiently close to) the desired output vector [18][19]. The number of hidden neurons and the learning factor are determined by trial and error [28].…”

Section: The Adaptive Learning Algorithmmentioning

confidence: 99%

“…Instead, they are trained with respect to data sets until they learn the patterns presented to them. Once they are trained, new patterns may be presented to them for prediction or classification [18,19]. Therefore, ANNs have been intensively used for solving regression and classification problems in many fields.…”

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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“…Instead, they are trained with respect to data sets until they learn the patterns presented to them. Once they are trained, new patterns may be presented to them for prediction or classification (Kalogirou, 2001;Hasan et al, 2006). Therefore, ANNs have been intensively used for solving regression and classification problems in many fields.…”

Section: Introductionmentioning

confidence: 99%