Path Planning for a 6 DoF Robotic Arm Based on Whale Optimization Algorithm and Genetic Algorithm

The trajectory planning for robotic arms is a significant area of research, given its role in facilitating seamless trajectory execution and enhancing movement efficiency and accuracy. This paper focuses on the development of path planning algorithms for a robotic arm with six degrees of freedom. Specifically, three alternative approaches are explored: polynomial (cubic and quantic), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). The comparison of outcomes between different methods revealed that polynomial methods were found to be more straightforward to implement, albeit constrained by the intricacy of the pathway. Upon examining the functioning of the WOA, it has been shown that it is well suited for all types of pathways, regardless of their level of complexity. In addition, when GA is applied, it has been shown less smoothness than WOA but also less complexity. In brief, WOA is deemed superior in the path planning process since it is more thorough in determining the optimal path due to the conical spiral path technique it employs in offering optimized path planning. in comparison to GA, WOA is better in implementation speed and accuracy. However, GA is smoother in start and finish path

Path Planning for a 6 DoF Robotic Arm Based on Whale Optimization Algorithm and Genetic Algorithm

The trajectory planning for robotic arms is a significant area of research, given its role in facilitating seamless trajectory execution and enhancing movement efficiency and accuracy. This paper focuses on the development of path planning algorithms for a robotic arm with six degrees of freedom. Specifically, three alternative approaches are explored: polynomial (cubic and quantic), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). The comparison of outcomes between different methods revealed that polynomial methods were found to be more straightforward to implement, albeit constrained by the intricacy of the pathway. Upon examining the functioning of the WOA, it has been shown that it is well suited for all types of pathways, regardless of their level of complexity. In addition, when GA is applied, it has been shown less smoothness than WOA but also less complexity. In brief, WOA is deemed superior in the path planning process since it is more thorough in determining the optimal path due to the conical spiral path technique it employs in offering optimized path planning. in comparison to GA, WOA is better in implementation speed and accuracy. However, GA is smoother in start and finish path

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Data reduction for SVM training using density-based border identification.

Numerous classification and regression problems have extensively used Support Vector Machines (SVMs). However, the SVM approach is less practical for large datasets because of its processing cost. This is primarily due to the requirement of optimizing a quadratic programming problem to determine the decision boundary during training. As a result, methods for selecting data instances that have a better likelihood of being chosen as support vectors by the SVM algorithm have been developed to help minimize the bulk of training data. This paper presents a density-based method, called Density-based Border Identification (DBI), in addition to four different variations of the method, for the lessening of the SVM training data through the extraction of a layer of border instances. For higher-dimensional datasets, the extraction is performed on lower-dimensional embeddings obtained by Uniform Manifold Approximation and Projection (UMAP), and the resulting subset can be repetitively used for SVM training in higher dimensions. Experimental findings on different datasets, such as Banana, USPS, and Adult9a, have shown that the best-performing variations of the proposed method effectively reduced the size of the training data and achieved acceptable training and prediction speedups while maintaining an adequate classification accuracy compared to training on the original dataset. These results, as well as comparisons to a selection of related state-of-the-art methods from the literature, such as Border Point extraction based on Locality-Sensitive Hashing (BPLSH), Clustering-Based Convex Hull (CBCH), and Shell Extraction (SE), suggest that our proposed methods are effective and potentially useful

Trajectory Optimization for a 6 DOF Robotic Arm Based on Reachability Time

The design of the robotic arm's trajectory is based on inverse kinematics problem solving, with additional refinements of certain criteria. One common design issue is the trajectory optimization of the robotic arm. Due to the difficulty of the work in the past, many of the suggested ways only resulted in a marginal improvement. This paper introduces two approaches to solve the problem of achieving robotic arm trajectory control while maintaining the minimum reachability time. These two approaches are based on rule-based optimization and a genetic algorithm. The way we addressed the problem here is based on the robot’s forward and inverse kinematics and takes into account the minimization of operating time throughout the operation cycle. The proposed techniques were validated, and all recommended criteria were compared on the trajectory optimization of the KUKA KR 4 R600 six-degree-of-freedom robot. As a conclusion, the genetic based algorithm behaves better than the rule-based one in terms of achieving a minimal trip time. We found that solutions generated by the Genetic based algorithm are approximately 3 times faster than the other solutions generated by the rule-based algorithm to the same paths. We argue that as the rule-based algorithm produces its solutions after discovering all the problem’s searching space which is time consuming, and it is not the case as per the genetic based algorithm.</p

A Novel Hybrid Deep Neural Network Classifier for EEG Emotional Brain Signals

The field of brain computer interface (BCI) is one of the most exciting areas in the field of scientific research, as it can overlap with all fields that need intelligent control, especially the field of the medical industry. In order to deal with the brain and its different signals, there are many ways to collect a dataset of brain signals, the most important of which is the collection of signals using the non-invasive EEG method. This group of data that has been collected must be classified, and the features affecting changes in it must be selected to become useful for use in different control capabilities. Due to the need for some fields used in BCI to have high accuracy and speed in order to comply with the environment’s motion sequences, this paper explores the classification of brain signals for their usage as control signals in Brain Computer Interface research, with the aim of integrating them into different control systems. The objective of the study is to investigate the EEG brain signal classification using different techniques such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), as well as the machine learning approach represented by the Support Vector Machine (SVM). We also present a novel hybrid classification technique called CNN-LSTM which combines CNNs with LSTM networks. This proposed model processes the input data through one or more of the CNN’s convolutional layers to identify spatial patterns and the output is fed into the LSTM layers to capture temporal dependencies and sequential patterns. This proposed combination uses CNNs’ spatial feature extraction and LSTMs’ temporal modelling to achieve high efficacy across domains. A test was done to determine the most effective approach for classifying emotional brain signals that indicate the user’s emotional state. The dataset used in this research was generated from a widely available MUSE EEG headgear with four dry extra-cranial electrodes. The comparison came in favor of the proposed hybrid model (CNN-LSTM) in first place with an accuracy of 98.5% and a step speed of 244 milliseconds/step; the CNN model came in the second place with an accuracy of 98.03% and a step speed of 58 milliseconds/step; and in the third place, the LSTM model recorded an accuracy of 97.35% and a step speed of 2 sec/step; finally, in last place, SVM came with 87.5% accuracy and 39 milliseconds/step running speed.</p

Comparison of methods on the USPS dataset.

The distribution of the different methods in the solution space of the three optimization objectives, in addition to the ratio of the reduced dataset, is shown for each pair of objectives. The proposed methods, except SVO, are predominantly closest to the optimal point.</p

Pareto set of different methods on the Adult9a dataset.

Pareto set of different methods on the Adult9a dataset.</p

Ranking of Pareto set methods on the USPS dataset based on closeness to the optimal point.

The optimal point is the zero point, representing the ideal of minimizing all the optimized metrics. The score is calculated as the reciprocal of the Euclidean distance from the optimal point.</p

Reduced dataset selection using the proposed SVO method for a non-overlapping dataset.

(a) A moons-shaped dataset. (b) Identified support vectors. (c) Reduced subset using SVO at k = 15.</p