Predicting the Motion of a Robot Manipulator with Unknown Trajectories Based on an Artificial Neural Network

Abstract

Mathematically, the motion of a robot manipulator can be computed through the integration of kinematics, dynamics, and trajectories calculations. However, the calculations are complex and only can be applied if the configuration of the robot and the characteristics of the joint trajectories are known. This paper introduces the use of artificial neural networks (ANN) to overcome these shortcomings by solving nonlinear functions and adapting the characteristics of unknown trajectories. A virtual six-degree-of-freedom (DOF) robot manipulator is exploited as an example to show the robustness of the developed ANN topology.

Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator

“…Hidden layers (3), (3,3,3,3), (3,3), (4,3,3,4), (6,6,6,6), (6,6), (7,6,6,7), (6,4,6), (6,12,12,6), (10,10,10,10,10), (12,12,12), (10,20,20,10), (4,4), ( 4), ( 6), (100), (100,100,100), (100,100,100,100), 66 and it provides the information of what is the average difference between real values in vector y and corresponding predicted values in vectorŷ. R 2 is defined in range 0; 1, with values closer to 1 representing better quality solution, 80 that is, solution that predicts values, which have less unexplained variance.…”

“…ANN is an artificial intelligence or machine learning algorithm 4 that can be used for solving various regression and classification tasks. 5,6 Artificial intelligence methods such as this have a wide variety of applications. ANNs and other artificial intelligence methods, such as evolutionary computing algorithms, [7][8][9] have a wide variety of uses in robotics in areas such as computer vision 10,11 and path optimization [12][13][14] and navigation.…”

Energy Saving Control Approach for Trajectory Tracking of Autonomous Mobile Robots

“…For trajectory tracking control design, AMRs must be capable of converging the tracking errors of the real trajectory and the desired trajectory as close to zero as possible while considering the influence of modeling uncertainties. A survey of the literature revealed that many studies have focused on the trajectory tracking control of AMRs, for example, trajectory tracking control through backstepping [7][8][9][10], sliding mode control [11][12][13][14], feedback linearization [15][16][17], neural networks [18][19][20][21][22], fuzzy control [23][24][25][26][27][28], and the H 2 [29,30] approach. In practice, it is challenging to implement microchip operation and torque output with low energy consumption by using the aforementioned control algorithm methodologies or extremely complex theoretical structures.…”

Research on Active Control of Rotating Motion and Vibration of Flexible Manipulator

“…Active control research, such as PD control, optimal control, variable structure control, and adaptive control, as well as robust control, intelligent control methods, etc. [2][3][4]. Shafei et al [5,6] research the study of a general formulation and numerical solution for the dynamic load-carrying capacity of a mechanical manipulator with elastic links.…”