Skill based motion planning of a redundant manipulator by genetic algorithm

This paper proposes a modeling method of criteria of skilled operators for motion planning of a redundant manipulator in industrial applications. The method employs fuzzy-ID3 to extract important factors with certainties from the criteria and GMDH (group method of data handling) to model it to evaluate motion plans. Then, it applies a genetic algorithm to optimize redundancy of a manipulator. The proposed method reduces the operator's labor and time for task teaching process thus a path, without considering redundant parameters, only needs to be determined. Experimental results show the effectiveness of the proposed method.

This paper proposes a motion planning method to cut a three dimensional workpiece by a redundant manipulator with six degrees of freedom. The method applies a genetic algorithm to optimize the rotational angles of the end-effector on a path. For a fitness function, an evaluation function is defined based on references from skilled operators. The proposed method reduces the operator's labor, so that he only has to determine a path without considering redundant parameters. Both simulations and experiments show the effectiveness of the proposed method.

Fault-tolerant motion planning and control of redundant manipulator

This paper investigates the motion planning of redundant free-floating manipulators with seven prismatic joints. On the earth, prismatic-jointed manipulators could only position their end-effectors in a desired way. However, in space, the end-effectors of free-floating manipulators can achieve both the desired orientation and desired position due to the dynamical coupling between manipulator and satellite movement, which is formally expressed by linear and angular momentum conservation laws. In this study, a tractable algorithm particle swarm optimization combined with differential evolution (PSODE) is provided to deal with the motion planning of redundant free-floating prismatic-jointed manipulators, which could avoid the pseudo inverse of the Jacobian matrix. The polynomial functions, as argument in sine functions are used to specify the joint paths. The coefficients of the polynomials are optimized to achieve the desired end-effector orientation and position, and simultaneously minimize the unit-mass-kinetic energy using the redundancy. Relevant simulations prove that this method provides satisfactory smooth paths for redundant free-floating prismatic-jointed manipulators. This study could help to recognize the advantages of redundant prismatic-jointed space manipulators.

Summary Many methods have been developed to determine the solution gas/oil ratio (Rs), starting with experiments, followed by empirical correlations establishments, and recently with machine learning applications receiving much interest due to their ability to produce precise results compared with empirical correlations. In this paper, the group method of data handling (GMDH) and the enhanced GMDH based on discrete differential evolution (GMDH-DDE) are used for the first time to estimate the Rs and to provide a correlation to the laboratory measured Rs from bubblepoint pressure (Pb), oil API gravity (API), gas-specific gravity (γg), and reservoir temperature (T) without crude oil properties. These two methods are compared with backpropagation neural networks (BPNN). The reason for using the hybrid GMDH (GMDH-DDE) is to overcome the drawbacks of the GMDH, such as the method used to calculate neuron weights (i.e., quadratic polynomial transfer function), which seems to have inaccuracies. Also, in selecting model inputs, the GMDH tends to choose the most appropriate inputs for the model; however, the selection criteria are not straightforward and may affect the final results. Furthermore, the GMDH has a multicollinearity problem, affecting model coefficient stability and overfitting problems, etc. A total of 420 data sets from the Mpyo oil field were used, with 70% used for training and 30% used for testing. According to the findings, the GMDH-DDE outperformed both the GMDH and BPNN. In comparison with the GMDH and BPNN, the GMDH-DDE has a higher correlation coefficient (R), lower root-mean-square error (RMSE), and lower mean absolute error (MAE). During training, R, RMSE, and MAE were 0.9849, 0.090, and 0.010, respectively, and during testing, R = 0.9603, RMSE = 0.290, and MAE = 0.017. The second-best technique (GMDH) produces R, RMSE, and MAE values of 0.9611, 0.122, and 0.032 in training, and R = 0.9438, RMSE = 0.349, and MAE = 0.055 in testing. Furthermore, the GMDH-DDE used less computational time (1.32 seconds) compared with the GMDH (2.01 seconds) and BPNN (4.96 seconds), proving that the GMDH-DDE has accurate and fast convergence compared with the GMDH and BPNN. These findings show that the GMDH-DDE and GMDH can be adopted as alternative methods for predicting the Rs.

In solving specific problems, physical laws and mathematical theorems directly express the connections between variables with equations/inequations. At times, it could be extremely hard or not viable to solve these equations/inequations directly. The PE (principle of equivalence) is a commonly applied pragmatic method across multiple fields. PE transforms the initial equations/inequations into simplified equivalent equations/inequations that are more manageable to solve, allowing researchers to achieve their objectives. The problem-solving process in many fields benefits from the use of PE. Recently, the ZE (Zhang equivalency) framework has surfaced as a promising approach for addressing time-dependent optimization problems. This ZEF (ZE framework) consolidates constraints at different tiers, demonstrating its capacity for the solving of time-dependent optimization problems. To broaden the application of ZEF in time-dependent optimization problems, specifically in the domain of motion planning for redundant manipulators, the authors systematically investigate the ZEF-I2I (ZEF of the inequation-to-inequation) type. The study concentrates on transforming constraints (i.e., joint constraints and obstacles avoidance depicted in different tiers) into consolidated constraints backed by rigorous mathematical derivations. The effectiveness and applicability of the ZEF-I2I are verified through two optimization motion planning schemes, which consolidate constraints in the velocity-tier and acceleration-tier. Schemes are required to accomplish the goal of repetitive motion planning within constraints. The firstly presented optimization motion planning schemes are then reformulated as two time-dependent quadratic programming problems. Simulative experiments conducted on the basis of a six-joint redundant manipulator confirm the outstanding effectiveness of the firstly presented ZEF-I2I in achieving the goal of motion planning within constraints.

The current study proposes a two-step approach for pore facies characterization in the carbonate reservoirs with an example from the Kangan and Dalanformations in the South Pars gas field. In the first step, pore facies were determined based on Mercury Injection Capillary Pressure (MICP) data incorporation with the Hierarchical Clustering Analysis (HCA) method. In the next step, polynomial meta-models were established based on the evolved Group Method of Data Handling (GMDH) neural networks for the purpose of pore facies identification from well log responses. In this way, the input data table used for training GMDH-type neural network consists of CALI, GR , CGR , SGR, DT, NPHI, RHOB, PEF, PHIE and VDL logs. The MICP-HCA derived pore facies were considered as the desired outputs. Moreover, multi-objective genetic algorithms (GAs) are used to the evolutionary design of GMDH-type neural networks. Training error and prediction error of neural network have been considered as conflicting objectives for Pareto multi-objective optimization. The results of this study indicate the successful implementation of GMDH neural networks for classification of pore facies in the heterogeneous gas bearing carbonate rocks of South Pars gas field.

This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles detected before the robot starts to move; this is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; this is realized using a velocity control law based on the null space method for redundancy control. Furthermore, an additional term of the control law is introduced which takes into account the speed of the obstacles, as well as their position. In order to test the algorithms, a set of simulations are presented: the redundant collaborative robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. The simulated data show that the proposed method for the smoothing of the trajectory can give a reduction of the angular accelerations of the motors of the order of 90%, with an increase of less than 15% of the calculation time. Furthermore, the dependence of the on-line control law on the speed of the obstacle can lead to reductions in the maximum speed and acceleration of the joints of approximately 50% and 80%, respectively, without significantly increasing the computational effort that is compatible for transferability to a real system.

This paper centres on a new GMDH (group method of data handling) algorithm based on the k-nearest neighbour (k-NN) method. Instead of the transfer function that has been used in traditional GMDH, the k-NN kernel function is adopted in the proposed GMDH to characterise relationships between the input and output variables. The proposed method combines the advantages of the k-nearest neighbour (k-NN) algorithm and GMDH algorithm, and thus improves the predictive capability of the GMDH algorithm. It has been proved that when the bandwidth of the kernel is less than a certain constant C, the predictive capability of the new model is superior to that of the traditional one. As an illustration, it is shown that the new method can accurately forecast consumer price index (CPI).

A new algorithm for autoregresive moving average (ARMA) parameter estimation is introduced. The algorithm is based on the group method of data handling (GMDH) first introduced by the Russian cyberneticist, A. G. Ivakhnenko, for solving high-order regression polynomials. The GMDH is heuristic in nature and self-organizes into a model of optimal complexity without any a priori knowledge about the system's inner workings. We modified the GMDH algorithm to solve for ARMA model parameters. Computer simulations have been performed to examine the efficacy of the GMDH and comparison of the GMDH is made to one of the most accurate and one of the most widely used algorithms, the fast orthogonal search (FOS) and the least-squares methods, respectively. The results show that in some cases with noise contamination and incorrect model order assumptions, the GMDH performs better than either the FOS or the least-squares methods in providing only the parameters that are associated with the true model terms.

A proper and reliable estimation of bearing capacity of thin-walled foundations is of importance and necessary for accurate design of these structures. This study proposes a new hybrid intelligent technique, i.e., adaptive neuro-fuzzy inference system (ANFIS)–polynomial neural network (PNN) optimized by the genetic algorithm (GA), called ANFIS–PNN–GA, for prediction of bearing capacity of the thin-walled foundations. In fact, in ANFIS–PNN–GA system, GA was used to optimize the ANFIS–PNN structure. To achieve the aim of this study, a series of data samples were collected from literature. After establishing the database, many ANFIS–PNN–GA models were constructed and proposed to estimate the bearing capacity of the aforementioned foundations. To show capability of this advance hybrid model, two pre-developed models i.e., ANFIS and PNN were also built to predict bearing capacity. The performance prediction of the proposed models were evaluated through the use of several performance indices, e.g., correlation coefficient (R) and mean square error (MSE). The R values of (0.9825, 0.9071, and 0.9928) and (0.8630, 0.7595 and 0.9241) were obtained for training and testing data of the ANFIS, PNN and ANFIS–PNN–GA, models, respectively. Accordingly, because of the role of GA as a practical optimization algorithm in improving the efficiency of both PNN and ANFIS models, results obtained by the ANFIS–PNN–GA model are more accurate compared to other implemented methods. The proposed advance hybrid model can be introduced as a new and applicable technique for solving problems in field of geotechnics and civil engineering.

Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data

Since the Elman Neural Networks was proposed, it has attracted wide attention. This method has fast convergence and high prediction accuracy. In this study, a new hybrid model that combines the Elman Neural Networks and the group method of data handling (GMDH) is used to forecast the exchange rate. The GMDH algorithm is used for system modeling. Input variables are selected by the external standards. Based on the output of the GMDH algorithm, valid input variables can be used as an input for the Elman Neural Networks for time series prediction. The empirical results show that the new hybrid algorithm is a useful tool.

Electric load forecasting is indispensable for the effective planning and operation of power systems. Various decisions related to power systems depend on the future behavior of loads. In this paper, we propose a new input selection procedure, which combines the group method of data handling (GMDH) and bootstrap method for support vector regression based hourly load forecasting. To construct the GMDH network, a learning dataset is divided into training and test datasets by bootstrapping. After constructing GMDH networks several times, the inputs that appeared frequently in the input layers of the completed networks were selected as the significant inputs. Filter methods based on linear correlation and mutual information (MI) were employed as comparison methods, and the performance of hybrids of the filter methods and the proposed method were also confirmed. In total, five input selection methods were compared. To verify the performance of the proposed method, hourly load data from South Korea was used and the results of one-hour, one-day and one-week-ahead forecasts were investigated. The experimental results demonstrated that the proposed method has higher prediction accuracy compared with the filter methods. Among the five methods, a hybrid of an MI-based filter with the proposed method shows best prediction performance.

This paper presents a novel motion planner for redundant robotic manipulators by utilizing rapidly exploring randomized trees and artificial potential fields. Rapidly exploring randomized trees and artificial potential fields are two well-known navigational strategies in the robotics industry; however, their potential benefits and synergy when implemented together has been largely unexplored. In the proposed motion planner, rapidly exploring randomized trees is first used to determine a suitable path for the end-effector to follow that maneuvers around all obstacles in the robot's workspace. Once a path has been determined, attractive and repulsive potential fields are implemented at all points along the path and are used in a gradient optimization algorithm to determine joint trajectories to reach the desired location. To supplement the attractive and repulsive potential fields, an orientation field is proposed to minimize the error between the actual end-effector orientation and the desired orientation during joint trajectory planning. The motion planner is examined through both analytical and experimental evaluation using the 7 degrees of freedom Kinova JACO and Kinova Gen3 robotic arms. The conclusions drawn from this work substantiate the efficacy and superiority of the proposed two-stage motion planner for the control of redundant manipulators in obstacle-ridden environments.