Optimasi Gripper Dua Lengan Dengan Menggunakan Metode Genetic Algorithm Pada Simulator Arm Robot 5 DOF (Degree of Freedom)

An arm robot simulator 5dof (degree of freedom) which is equipped with a two-finger gripper is designed to determine the movement of the robot manipulator. To make an arm robot simulator, we used acrylic as a base material, servomotor as a driver and an Arduino Uno SMD as microcontroller. Acrylic was chosen because it is light, strong and durable. Arduino Uno SMD was chosen because it can interact with LabVIEW that will be able to control the movement angle of servomotor manually. The purpose of this final project is to make an arm robot simulator 5 dof which equipped a gripper and optimization of two finger gripper configuration use one of optimization techniques, the genetic algorithm optimization. For making the link of simulator, we use the acrylic laser cutting machine to be more precision cutting. Servomotor\u27s movement is controlled by using a program that was created using LabVIEW. Servomotor\u27s angle position error was corrected by using program functions of numerical multiply and numerical divided on LabVIEW. Optimization of gripper configuration using MATLAB software as a tool for the calculation. A genetic algorithm optimization used the default parameters, then the calculation result are always different. All the results of these calculations are correct and it affects the shape of the gripper configuration at zmax. Then the torque of gripper before optimized and after optimized can be obtained

Un Enfoque Evolutivo Multi-Objetivo al Problema de la Construcción de Grupos de Estudiantes Universitarios

The creation of working groups of students in education is a common process that is often developed by the teacher intuitively. However, such a process is actually a complex task since various students and criteria must be taken into account. In general, these criteria are often in conflict because they are a reflection of the educational interests of teachers and on the other hand, the individual preferences of students. In this sense, this paper has as general goal: to propose a mathematicalcomputational solution that efficiently automatizes, in terms of computational time and solution quality, the creation of working groups of college students. The results obtained from two real scenarios of the Universidad Tecnica Estatal de Quevedo indicate that the proposal is an effective alternative to the traditional model. &nbsp

THE BEES’ ALGORITHM FOR DESIGN OPTIMIZATION OF A GRIPPER MECHANISM

In this paper, a gripper mechanism is optimized by using bees’ algorithm (BA) to compare with Non-dominated Sorting Genetic Algorithm version II (NSGA-II). The procedure of BA is proposed. The superiority of BA is illustrated by using results in figures and tables. A sensitivity analysis using correlation test is executed. The effectiveness coefficients of design variable for the objectives are provided. Consequently, the effectual design variables and the genuine searching method of BA are clearly evaluated and discussed. The BA provides dispersed and the least crowded Pareto Front population for solution in the shortest duration. Therefore, the best solutions are selected based on curve fitting. The closest solutions to the fitted curve are selected as the best in the region

Research on Preference Polyhedron Model Based Evolutionary Multiobjective Optimization Method for Multilink Transmission Mechanism Conceptual Design

To make the optimal design of the multilink transmission mechanism applied in mechanical press, the intelligent optimization techniques are explored in this paper. A preference polyhedron model and new domination relationships evaluation methodology are proposed for the purpose of reaching balance among kinematic performance, dynamic performance, and other performances of the multilink transmission mechanism during the conceptual design phase. Based on the traditional evaluation index of single target of multicriteria design optimization, the robust metrics of the mechanism system and preference metrics of decision-maker are taken into consideration in this preference polyhedron model and reflected by geometrical characteristic of the model. At last, two optimized multilink transmission mechanisms are designed based on the proposed preference polyhedron model with different evolutionary algorithms, and the result verifies the validity of the proposed optimization method

Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation

When humans perform contact-rich manipulation tasks, customized tools are often necessary and play an important role in simplifying the task. For instance, in our daily life, we use various utensils for handling food, such as knives, forks and spoons. Similarly, customized tools for robots may enable them to more easily perform a variety of tasks. Here, we present an end-to-end framework to automatically learn tool morphology for contact-rich manipulation tasks by leveraging differentiable physics simulators. Previous work approached this problem by introducing manually constructed priors that required detailed specification of object 3D model, grasp pose and task description to facilitate the search or optimization. In our approach, we instead only need to define the objective with respect to the task performance and enable learning a robust morphology by randomizing the task variations. The optimization is made tractable by casting this as a continual learning problem. We demonstrate the effectiveness of our method for designing new tools in several scenarios such as winding ropes, flipping a box and pushing peas onto a scoop in simulation. We also validate that the shapes discovered by our method help real robots succeed in these scenarios

Parallel-Jaw Gripper and Grasp Co-Optimization for Sets of Planar Objects

We propose a framework for optimizing a planar parallel-jaw gripper for use with multiple objects. While optimizing general-purpose grippers and contact locations for grasps are both well studied, co-optimizing grasps and the gripper geometry to execute them receives less attention. As such, our framework synthesizes grippers optimized to stably grasp sets of polygonal objects. Given a fixed number of contacts and their assignments to object faces and gripper jaws, our framework optimizes contact locations along these faces, gripper pose for each grasp, and gripper shape. Our key insights are to pose shape and contact constraints in frames fixed to the gripper jaws, and to leverage the linearity of constraints in our grasp stability and gripper shape models via an augmented Lagrangian formulation. Together, these enable a tractable nonlinear program implementation. We apply our method to several examples. The first illustrative problem shows the discovery of a geometrically simple solution where possible. In another, space is constrained, forcing multiple objects to be contacted by the same features as each other. Finally a toolset-grasping example shows that our framework applies to complex, real-world objects. We provide a physical experiment of the toolset grasps.Comment: 2023 IEEE IROS conferenc

Generative design in the development of a robotic manipulator

The emergence of cyber physical production systems has brought with it an increased utilization of robotics in collaborative manufacturing environments. An approach to meet this demand is to democratize robotics by making cheaper more customizable robots that can be implemented by small and medium enterprises. To tackle this problem this research looks at using rapid prototyping techniques for the development of customizable robotic manipulators which can be implemented in cyber physical production systems. This research therefore contributes an approach for designing connected and rapid prototyped robotic manipulators. This approach considers both the software and hardware development required for implementing a robotic manipulator. Furthermore generative design, an evolutionary and artificial intelligence based approach, is used to design the link modules between the robot joints. This component has been identified as the ideal to be designed with this approach as it benefits most of the generative design approach coupled with rapid prototyping. This paper also explores a robotic manipulator control structure based on Ethernet control technology for implementation within cyber physical production systems.peer-reviewe

Creación automática de equipos de estudiantes universitarios: una experiencia desde la asignatura Inglés / Automatic Building of University Student Teams: an experience from English subject

Uno de los principales objetivos en la educación es lograr que los estudiantes desarrollen la capacidad de trabajo en equipo. Esta capacidad potencia la socialización entre los estudiantes y la resolución de problemas complejos. Comúnmente, la creación de estos equipos es realizada por el docente de la asignatura, quien debe tener en cuenta múltiples criterios como la presencia de un estudiante líder y equipos heterogéneos. Cuando la asignatura tiene poco estudiantes, esta tarea suele ser fácil. Sin embargo, cuando se debe tener en cuenta a numerosos estudiantes, la tarea se torna compleja y por lo general no existe garantía de que los equipos creados cumplan con los criterios deseados. En este sentido, con el objetivo de favorecer el desarrollo óptimo de esta tarea docente, la presente investigación propone una solución computacional que automatiza la creación de equipos de trabajo de estudiantes. Específicamente, la tarea de la creación de los equipos se modeló matemáticamente como un problema de optimización de tipo combinatorio y multi-objetivo, que fue resuelto a su vez por un algoritmo evolutivo basado en los conceptos de Dominancia de Pareto. Para validar las propuestas, se realizaron varios experimentos computacionales que involucran escenarios reales, relacionados con la Unidad de Aprendizaje Inglés en varias carreras de la Universidad Técnica Estatal de Quevedo. ABSTRACTOne of the main goals for Higher Education is to educate students to work in teams. Such a skill not only improves their social behavior in the community, but also the ability for solving complex problems. Usually, the process of making teams is carried out by professor of the subject, who has to take into account several criteria (e.g. the presence of leader, heterogeneity of the team according the level of knowledge, sex, among others). When the subject has just few students, this task becomes easy. However, in the case of classes with a large number of students, this task becomes complex and there is no warranty about the accomplishment of the considered criteria. In that sense, the present work proposes a computational solution that automatizes the task of student teams building. Specifically, it was approached as a multi-objective combinatorial optimization problem, which was solved using a Pareto Dominance-based algorithm. In order to validate the proposal we performed several computational experiments involving real case studies from the English subject of three careers at the Technical State University of Quevedo. Results show that the proposed approach is able to build balanced teams according to the considered criteria

Wind turbine blade geometry design based on multi-objective optimization using metaheuristics

Abstract: The application of Evolutionary Algorithms (EAs) to wind turbine blade design can be interesting, by reducing the number of aerodynamic-to-structural design loops in the conventional design process, hence reducing the design time and cost. Recent developments showed satisfactory results with this approach, mostly combining Genetic Algorithms (GAs) with the Blade Element Momentum (BEM) theory. The general objective of the present work is to define and evaluate a design methodology for the rotor blade geometry in order to maximize the energy production of wind turbines and minimize the mass of the blade itself, using for that purpose stochastic multi-objective optimization methods. Therefore, the multi-objective optimization problem and its constraints were formulated, and the vector representation of the optimization parameters was defined. An optimization benchmark problem was proposed, which represents the wind conditions and present wind turbine concepts found in Brazil. This problem was used as a test-bed for the performance comparison of several metaheuristics, and also for the validation of the defined design methodology. A variable speed pitch-controlled 2.5 MW Direct-Drive Synchronous Generator (DDSG) turbine with a rotor diameter of 120 m was chosen as concept. Five different Multi-objective Evolutionary Algorithms (MOEAs) were selected for evaluation in solving this benchmark problem: Non-dominated Sorting Genetic Algorithm version II (NSGA-II), Quantum-inspired Multi-objective Evolutionary Algorithm (QMEA), two approaches of the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multi-objective Optimization Differential Evolution Algorithm (MODE). The results have shown that the two best performing techniques in this type of problem are NSGA-II and MOEA/D, one having more spread and evenly spaced solutions, and the other having a better convergence in the region of interest. QMEA was the worst MOEA in convergence and MODE the worst one in solutions distribution. But the differences in overall performance were slight, because the algorithms have alternated their positions in the evaluation rank of each metric. This was also evident by the fact that the known Pareto Front (PF) consisted of solutions from several techniques, with each dominating a different region of the objective space. Detailed analysis of the best blade design showed that the output of the design methodology is feasible in practice, given that flow conditions and operational features of the rotor were as desired, and also that the blade geometry is very smooth and easy to manufacture. Moreover, this geometry is easily exported to a Computer-Aided Design (CAD) or Computer-Aided Engineering (CAE) software. In this way, the design methodology defined by the present work was validated