433 research outputs found
Inverse Kinematics Solution for Robot Manipulator based on Neural Network under Joint Subspace
Neural networks with their inherent learning ability have been widely applied to solve the robot manipulator inverse kinematics problems. However, there are still two open problems: (1) without knowing inverse kinematic expressions, these solutions have the difficulty of how to collect training sets, and (2) the gradient-based learning algorithms can cause a very slow training process, especially for a complex configuration, or a large set of training data. Unlike these traditional implementations, the proposed metho trains neural network in joint subspace which can be easily calculated with electromagnetism-like method. The kinematics equation and its inverse are one-to-one mapping within the subspace. Thus the constrained training sets can be easily collected by forward kinematics relations. For issue 2, this paper uses a novel learning algorithm called extreme learning machine (ELM) which randomly choose the input weights and analytically determines the output weights of the single hidden layer feedforward neural networks (SLFNs). In theory, this algorithm tends to provide the best generalization performance at extremely fast learning speed. The results show that the proposed approach has not only greatly reduced the computation time but also improved the precision
Deep Neural Network Based Subspace Learning of Robotic Manipulator Workspace Mapping
The manipulator workspace mapping is an important problem in robotics and has
attracted significant attention in the community. However, most of the
pre-existing algorithms have expensive time complexity due to the reliance on
sophisticated kinematic equations. To solve this problem, this paper introduces
subspace learning (SL), a variant of subspace embedding, where a set of robot
and scope parameters is mapped to the corresponding workspace by a deep neural
network (DNN). Trained on a large dataset of around
samples obtained from a MATLAB implementation of a classical method
and sampling of designed uniform distributions, the experiments demonstrate
that the embedding significantly reduces run-time from s of traditional discretization method to s, with high
accuracies (average F-measure is with batch gradient descent
and resilient backpropagation).Comment: 12 pages, 12 figures, accepted for presentation at ICCAIRO 201
Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations
A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec
SELF-LEARNING OF DELTA ROBOT USING INVERSE KINEMATICS AND ARTIFICIAL NEURAL NETWORKS
As known as Parallel-Link Robot, Delta Robot is a kind of Manipulator Robot that consists of three arms mounted in parallel. Delta Robot has a central joint constructed as an end-effector represented as a gripper. An Analysis of Inverse Kinematic (IK) used to convert the end-effector trajectory (X, Y) into rotations of stepper motors (ZA, ZB and ZC). The proposed method used Artificial Neural Networks (ANNs) to simplify the process of IK solver. The IK solver generated the datasets contain motion data of the Delta robot. There are 11 KB Datasets consist of 200 motion data used to be trained. The proposed method was trained in 58.78 seconds in 5000 iterations. Using a learning rate (α) 0.05 and produced the average accuracy was 97.48%, and the average loss was 0.43%. The proposed method was also tested to transfer motion data over Socket.IO with 115.58B in 6.68ms
The Parameter-Less Self-Organizing Map algorithm
The Parameter-Less Self-Organizing Map (PLSOM) is a new neural network
algorithm based on the Self-Organizing Map (SOM). It eliminates the need for a
learning rate and annealing schemes for learning rate and neighbourhood size.
We discuss the relative performance of the PLSOM and the SOM and demonstrate
some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally
we discuss some example applications of the PLSOM and present a proof of
ordering under certain limited conditions.Comment: 29 pages, 27 figures. Based on publication in IEEE Trans. on Neural
Network
Robotic joint-motion optimization of functionally-redundant tasks for joint-limits and singularity avoidance
La méthodologie de décomposition du torseur de vitesse (TWA) et évitement des limites articulaires -- Évitement des limites articulaires et singularités -- auto-adaptation des poids en TWA -- Adaptation dynamique des pondération en TWA -- Background and basic terminology -- Problem formulation -- Research objective -- Literature review -- Level of kinematic analysis -- Differential kinematics and redundancy -- Local optimization algorithms -- Global optimization algorithms -- Redundancy-resolution in intelligent control -- Functional redundancy-resolution -- Twist decomposition approach and joint-limits avoidance -- Kinematic inversion of functionally-redundant manipulators -- Puma 500 -- Fanuc M16iB -- Fanuc 710c50 -- General task projectors -- Joint-limits and singularity avoidance in TWA -- Performance criteria -- Numerical examples -- Self-adaptation of weights in TWA -- Joint-limits and singularity avoidances -- Weights self-adaptation system -- Dynamic-adaptation of weights in TWA -- Weights dynamic-adaptation system
Learning Grasps in a Synergy-based Framework
In this work, a supervised learning strategy has been applied in conjunction with a control strategy to provide anthropomorphic hand-arm systems with autonomous grasping capabilities. Both learning and control algorithms have been developed in a synergy-basedframework in order to address issues related to high dimension of the configuration space, that typically characterizes robotic hands and arms with humanlike kinematics. An experimental setup has been built to learn hand-arm motion from humans during reaching and grasping tasks. Then, a Neural Network (NN) has been realized to generalize the grasps learned by imitation. Since the NN approximates the relationship between the object characteristics and the grasp configuration of the hand-arm system, a synergy-based control strategy has been applied to overcome planning errors. The reach-to-grasp strategy has been tested on a setup constituted by the KUKA LWR 4+Arm and the SCHUNK 5-Finger Hand
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