Rasit Koker

A study of neural network based inverse kinematics solution for a three-joint robot

Abstract A neural network based inverse kinematics solution of a robotic manipulator is presented in this paper. Inverse kinematics problem is generally more complex for robotic manipulators. Many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. In this study, a three-joint robotic manipulator simulation software, developed in our previous studies, is used. Firstly, we have generated many initial and final points in the work volume of the robotic manipulator by using cubic trajectory planning. Then, all of the angles according to the real-world coordinates (x, y, z) are recorded in a file named as training set of neural network. Lastly, we have used a designed neural network to solve the inverse kinematics problem. The designed neural network has given the correct angles according to the given (x, y, z) cartesian coordinates. The online working feature of neural network makes it very successful and popular in this solution.

A parallel neural network approach to prediction of Parkinson's Disease

Recently the neural network based diagnosis of medical diseases has taken a great deal of attention. In this paper a parallel feed-forward neural network structure is used in the prediction of Parkinsons Disease. The main idea of this paper is using more than a unique neural network to reduce the possibility of decision with error. The output of each neural network is evaluated by using a rule-based system for the final decision. Another important point in this paper is that during the training process, unlearned data of each neural network is collected and used in the training set of the next neural network. The designed parallel network system significantly increased the robustness of the prediction. A set of nine parallel neural networks yielded an improvement of 8.4% on the prediction of Parkinsons Disease compared to a single unique network. Furthermore, it is demonstrated that the designed system, to some extent, deals with the problems of imbalanced data sets.

Reliability-based approach to the inverse kinematics solution of robots using Elman's networks

The solution of inverse kinematics problem of redundant manipulators is a fundamental problem in robot control. The inverse kinematics problem in robotics is the determination of joint angles for a desired cartesian position of the end effector. For the solution of this problem, many traditional solutions such as geometric, iterative and algebraic are inadequate if the joint structure of the manipulator is more complex. Furthermore, many neural network approaches have been done to this problem. But the neural network-based solutions are not much reliable due to the error at the end of learning. Therefore, a reliability-based neural network inverse kinematics solution approach has been presented, and applied to a six-degrees of freedom (dof) robot manipulator in this paper. The structure of the proposed method is based on using three networks designed parallel to minimize the error of the whole system. Elman network, which has a profound impact on the learning capability and performance of the network, is chosen and designed according to the proposed solution method. At the end of parallel implementation, the results of each network are evaluated using direct kinematics equations to obtain the network with best result.

Design and performance of an intelligent predictive controller for a six-degree-of-freedom robot using the Elman network

The aim of this paper was to propose a recurrent neural network-based predictive controller for robotic manipulators. A neural network controller for a six-joint Stanford robotic manipulator was designed using the generalized predictive control (GPC) and the Elman network. The GPC algorithm, which is a class of digital control method, requires long computational time. This is a disadvantage in real-time robot control; therefore, the Elman network controller was designed to reduce processing time by avoiding the highly mathematical and computational complexity of the GPC. The main reason for choosing the Elman network, amongst several neural network algorithms, was that the presence of feedback loops have a profound impact on the learning capability of the network. The designed neural network controller was able to recover quickly because of its significant generalization capability, which allowed it to adapt very rapidly to changes in inputs. The performance of the controller was also shown graphically using simulation software, including the dynamics and kinematics of the robot model.

Parallel robot scheduling to minimize mean tardiness with precedence constraints using a genetic algorithm

Identical parallel robot scheduling problem for minimizing mean tardiness with precedence constraints is a very important scheduling problem. But, the solution of this problem becomes much difficult when there are a number of robots, jobs and precedence constraints. Genetic algorithm is an efficient tool in the solution of combinatorial optimization problems, as it is well known. In this study, a genetic algorithm is used to schedule jobs that have precedence constraints minimizing the mean tardiness on identical parallel robots. The solutions of problems, which have been taken in different scales, have been done using simulated annealing and genetic algorithm. In particular, genetic algorithm is found noteworthy successful in large-scale problems.

A neuro-simulated annealing approach to the inverse kinematics solution of redundant robotic manipulators

The neural-network-based inverse kinematics solution is one of the recent topics in the robotics because of the fact that many traditional inverse kinematics problem solutions such as geometric, iterative and algebraic are inadequate for redundant robots. However, since the neural networks work with an acceptable error, the error at the end of inverse kinematics learning should be minimized. In this study, simulated annealing (SA) algorithm was used together with the neural-network-based inverse kinematics problem solution robots to minimize the error at the end effector. The solution method is applied to Stanford and Puma 560 six-joint robot models to show the efficiency. The proposed algorithm combines the characteristics of neural network and an optimization technique to obtain the best solution for the critical robotic applications. Three Elman neural networks were trained using separate training sets and different parameters, since one of them can give better results than the others can. The best result is selected within three neural network results by computing the end effector error via direct kinematics equation of the robotic manipulator. The decimal part of the neural network result was improved up to 10 digits using simulated annealing algorithm. The obtained best solution is given to the simulated annealing algorithm to find the best-fitting 10 digits for the decimal part of the solution. The end effector error was reduced significantly.

Parallel Robot Scheduling to Minimize Mean Tardiness with Unequal Release Date and Precedence Constraints Using a Hybrid Intelligent System

This paper considers the problem of scheduling a given number of jobs on a specified number of identical parallel robots with unequal release dates and precedence constraints in order to minimize mean tardiness. This problem is strongly NP-hard. The author proposes a hybrid intelligent solution system, which uses Genetic Algorithms and Simulated Annealing (GA+SA). A genetic algorithm, as is well known, is an efficient tool for the solution of combinatorial optimization problems. Solutions for problems of different scales are found using genetic algorithms, simulated annealing and a Hybrid Intelligent Solution System (HISS). Computational results of empirical experiments show that the Hybrid Intelligent Solution System (HISS) is successful with regards to solution quality and computational time.

A neuro-genetic-simulated annealing approach to the inverse kinematics solution of robots: a simulation based study

AbstractIn this study, a hybrid intelligent solution system including neural networks, genetic algorithms and simulated annealing has been proposed for the inverse kinematics solution of robotic manipulators. The main purpose of the proposed system is to decrease the end effector error of a neural network based inverse kinematics solution. In the designed hybrid intelligent system, simulated annealing algorithm has been used as a genetic operator to decrease the process time of the genetic algorithm to find the optimum solution. Obtained best solution from the neural network has been included in the initial solution of genetic algorithm with randomly produced solutions. The end effector error has been reduced micrometer levels after the implementation of the hybrid intelligent solution system.

Model Based Intelligent Control of a 3-Joint Robotic Manipulator: A Simulation Study Using Artificial Neural Networks

Recently, there has been a great deal of interest in intelligent control of robotic manipulators. Artificial neural network (ANN) is a widely used intelligent technique on this way. Using ANN, these controllers learn about the systems to be online controlled by them. In this paper, a neural network controller was designed using traditional generalized predictive control algorithm (GPC). The GPC algorithm, which belongs to a class of digital control methods and known as Model Based Predictive Control, require long computational time and can result in a poor control performance in robot control. Therefore, to reduce the process time, in other words, to avoid from the highly mathematical computational structure of GPC, a neural network was designed for a 3-Joint robot. The performance of the designed control system was shown to be successful using the simulation software, which includes the dynamics and kinematics of the robot model.

Development of a vision based object classification system for an industrial robotic manipulator

Due to the growing cost of raw materials, workmanship, energy, and growing competition environment, manufacturers are forced to produce cheaper and higher quality productions. This force results in the need for automation techniques. We have developed a vision based system for industrial robotic manipulators to classify objects on a moving conveyor. Low and intermediate level image processing algorithms are implemented as a first step. In the implementation of application level image processing, a neural network is designed to classify objects, and moment invariants are used as feature vector set. The system is tested for different objects moving on a conveyor. The speed and the position of the object are computed. This information represents the most used criteria in the control of robots. The hardware implementation and overview of the algorithms with their results are presented.