scholarly journals Solving the Inverse Kinematic Equations of Elastic Robot Arm Utilizing Neural Network

2017 ◽  
Vol 13 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Hussein M. Al-Khafaji ◽  
Muhsin J. Jweeg
Keyword(s):  
Neural Network ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Joint Angles ◽  
Kinematic Equation ◽  
End Effector ◽  
Joint Configuration ◽  
Network Training ◽  
The Neural Network ◽  
Forward Kinematic

The inverse kinematic equation for a robot is very important to the control robot’s motion and position. The solving of this equation is complex for the rigid robot due to the dependency of this equation on the joint configuration and structure of robot link. In light robot arms, where the flexibility exists, the solving of this problem is more complicated than the rigid link robot because the deformation variables (elongation and bending) are present in the forward kinematic equation. The finding of an inverse kinematic equation needs to obtain the relation between the joint angles and both of the end-effector position and deformations variables. In this work, a neural network has been proposed to solve the problem of inverse kinematic equation. To feed the neural network, experimental data were taken from an elastic robot arm for training the network, these data presented by joint angles, deformation variables and end-effector positions. The results of network training showed a good fit between the output results of the neural network and the targets data. In addition, this method for finding the inverse of kinematic equation proved its effectiveness and validation when applying the results of neural network practically in the robot’s operating software for controlling the real light robot’s position.

Solution to the Forward and Inverse Kinematic Equations Using Multilayer Back-Propagation Neural Network

1994 ◽  
Author(s):  
Phani K. Nagarjuna ◽  
Athamaram H. Soni
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Kinematic Equation ◽  
Neural Network Approach ◽  
The Neural Network

Abstract The problem of inverse kinematics in Robotics, is a nonlinear mapping from a given cartesian coordinates to the desirable joint coordinates of the robot arm. It is found that an appropriately designed neural network can be trained to learn the non-linearity of the Inverse Kinematic Equation (IKE). We present an approach for solving the Forward Kinematic Equation (FKE) and the IKE by means of a Multi Layer Back-Propagation Neural Network (Rumelhart et al., 1986). The neural network approach is applied to a Two Degrees-of-Freedom (DOF) robot manipulator and the results are compared with those obtained using the analytical solution. The results obtained from the simulation of the neural network indicate a fairly accurate learning of the FKE and IKE by the Multi Layer Back-Propagation Neural Network.

scholarly journals Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Takehiko Ogawa ◽  
Hajime Kanada
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Inverse Kinematics ◽  
Robot Arm ◽  
Joint Angles ◽  
End Effector ◽  
Inverse Kinematics Problem ◽  
Ill Posed ◽  
The Given

In the context of controlling a robot arm with multiple joints, the method of estimating the joint angles from the given end-effector coordinates is called inverse kinematics, which is a type of inverse problems. Network inversion has been proposed as a method for solving inverse problems by using a multilayer neural network. In this paper, network inversion is introduced as a method to solve the inverse kinematics problem of a robot arm with multiple joints, where the joint angles are estimated from the given end-effector coordinates. In general, inverse problems are affected by ill-posedness, which implies that the existence, uniqueness, and stability of their solutions are not guaranteed. In this paper, we show the effectiveness of applying network inversion with regularization, by which ill-posedness can be reduced, to the ill-posed inverse kinematics of an actual robot arm with multiple joints.

Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry

2021 ◽  
Author(s):  
Christopher Irrgang ◽  
Jan Saynisch-Wagner ◽  
Robert Dill ◽  
Eva Boergens ◽  
Maik Thomas
Keyword(s):  
Neural Network ◽  
Satellite Altimetry ◽  
Water Cycle ◽  
Global Water Cycle ◽  
Network Training ◽  
The Neural Network ◽  
Continental Hydrology ◽  
Terrestrial Water ◽  
Combine Machine ◽  
Groundwater Basins

<p>Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.</p><p>https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258</p>

Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

scholarly journals Modeling a Thermochemical Reactor of a Solar Refrigerator by BaCl2-NH3 Sorption Using Artificial Neural Networks and Mathematical Symmetry Groups

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Onesimo Meza-Cruz ◽  
Isaac Pilatowsky ◽  
Agustín Pérez-Ramírez ◽  
Carlos Rivera-Blanco ◽  
Youness El Hamzaoui ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Heating Temperature ◽  
Barium Chloride ◽  
Symmetry Groups ◽  
Solar Cooling ◽  
Network Training ◽  
The Neural Network ◽  
Marquardt Algorithm

The aim of this work is to present a model for heat transfer, desorbed refrigerant, and pressure of an intermittent solar cooling system’s thermochemical reactor based on backpropagation neural networks and mathematical symmetry groups. In order to achieve this, a reactor was designed and built based on the reaction of BaCl2-NH3. Experimental data from this reactor were collected, where barium chloride was used as a solid absorbent and ammonia as a refrigerant. The neural network was trained using the Levenberg–Marquardt algorithm. The correlation coefficient between experimental data and data simulated by the neural network was r = 0.9957. In the neural network’s sensitivity analysis, it was found that the inputs, reactor’s heating temperature and sorption time, influence neural network’s learning by 35% and 20%, respectively. It was also found that, by applying permutations to experimental data and using multibase mathematical symmetry groups, the neural network training algorithm converges faster.

Repeatable Joint Displacement Generation for Redundant Robotic Systems

1994 ◽  
Vol 116 (1) ◽  
pp. 11-16 ◽  
Author(s):  
Y. S. Chung ◽  
M. Griffis ◽  
J. Duffy
Keyword(s):  
Joint Space ◽  
Inverse Kinematic ◽  
Torsional Stiffness ◽  
Numerical Verification ◽  
Redundant Manipulators ◽  
Redundant Manipulator ◽  
Kinematic Equation ◽  
End Effector ◽  
Prismatic Joints ◽  
Cyclic Type

This paper presents a novel, practical, and theoretically sound kinematic control strategy for serial redundant manipulators. This strategy yields repeatability in the joint space of a serial redundant manipulator whose end effector undergoes some general cyclic type motion. This is accomplished by deriving a new inverse kinematic equation that is based on springs being theoretically or conceptually located in the joints of the manipulator (torsional springs for revolute joints, translational springs for prismatic joints). Previous researchers have also derived an inverse kinematic equation for serial redundant manipulators. However, to the authors’ knowledge, the new strategy is the first to include the free angles of torsional springs and the free lengths of translational springs. This is important because it ensures the repeatability in the joint space of a serial redundant manipulator whose end effector undergoes a cyclic type motion. Numerical verification for repeatability is done in terms of Lie bracket condition. Choices for the free angle and torsional stiffness of a joint (or the free length and translational stiffness) are made based upon the mechanical limits of the joint.

Motion Simulation of a Hybrid Parallel Robot for Ankle Rehabilitation

2014 ◽  
Author(s):  
Hamid Rakhodaei ◽  
Mozafar Saadat ◽  
Alireza Rastegarpanah
Keyword(s):  
Parallel Robot ◽  
Hybrid Structure ◽  
Inverse Kinematic ◽  
Motion Simulation ◽  
Stroke Patients ◽  
End Effector ◽  
Ankle Rehabilitation ◽  
Motion Paths ◽  
Experimental Values ◽  
Forward Kinematic

This paper addresses the path planning of a hybrid parallel robot for ankle rehabilitation. The robot contains 3-DOF parallel mechanism that is attached on top of the 6-DOF hexapod. The 6-UPU-3-UPR parallel robot is developed to simulate ankle motions for the rehabilitation of post-stroke patients with an affected ankle. The inverse kinematic of hybrid parallel robot is developed in order to track the end-effector’s position through Matlab software. The calculated stroke size of each actuator is imported to apply the forward kinematic for determining the position of end-effector. The experimental and simulation values of the hexapod are compared with those of the hybrid structure through a number of exercise motion paths. The results reveal that, in general, the simulation values follow well the experimental values, although with different degrees of variation for each of the structures considered.

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