A multi-objective approach for the motion planning of redundant manipulators

Kinematic redundancy occurs when a manipulator possesses more degrees of freedom than those required to execute a given task. Several kinematic techniques for redundant manipulators control the gripper through the pseudo-inverse of the Jacobian, but lead to a kind of chaotic inner motion with unpredictable arm configurations. Such algorithms are not easy to adapt to optimization schemes and, moreover, often there are multiple optimization objectives that can conflict between them. Unlike single optimization, where one attempts to find the best solution, in multi-objective optimization there is no single solution that is optimum with respect to all indices. Therefore, trajectory planning of redundant robots remains an important area of research and more efficient optimization algorithms are needed. This paper presents a new technique to solve the inverse kinematics of redundant manipulators, using a multi-objective genetic algorithm. This scheme combines the closed-loop pseudo-inverse method with a multi-objective genetic algorithm to control the joint positions. Simulations for manipulators with three or four rotational joints, considering the optimization of two objectives in a workspace without and with obstacles are developed. The results reveal that it is possible to choose several solutions from the Pareto optimal front according to the importance of each individual objective.

Automatic tunning of Okumura-Hata model on railway communications

This paper presents the Genetic Algorithms (GA) as an efficient solution for the Okumura-Hata prediction model tuning on railways communications. A method for modelling the propagation model tuning parameters was presented. The algorithm tuning and validation were based on real networks measurements carried out on four different propagation scenarios and several performance indicators were used. It was shown that the proposed GA is able to produce significant improvements over the original model. The algorithm developed is currently been used on real GSM-R network planning process for an enhanced resources usage.

Multi-criteria optimization manipulator trajectory planning

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at destination point. Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development of an algorithm to find a continuous motion that takes the manipulator from a given starting configuration up to a desired end position without colliding with any obstacle in the workspace. The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches proposed in the literature. Section 3 formulates the problem, namely the representation adopted to solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links) when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for the 3R redundant manipulator with five goals and for other complementary experiments are described, respectively. Finally, section 8 draws the main conclusions.

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.

Fitness function evaluation through fractional algorithms

This paper proposes a Genetic Algorithm (GA) for the design of combinational logic circuits. The fitness function evaluation is calculated using Fractional Calculus. This approach extends the classical fitness function by including a fractional-order dynamical evaluation. The experiments reveal superior results when comparing with the classical method.

Automated design of microwave discrete tuning differential capacitance circuits in Si-integrated technologies

A genetic algorithm used to design radio-frequency binary-weighted differential switched capacitor arrays (RFDSCAs) is presented in this article. The algorithm provides a set of circuits all having the same maximum performance. This article also describes the design, implementation, and measurements results of a 0.25 lm BiCMOS 3-bit RFDSCA. The experimental results show that the circuit presents the expected performance up to 40 GHz. The similarity between the evolutionary solutions, circuit simulations, and measured results indicates that the genetic synthesis method is a very useful tool for designing optimum performance RFDSCAs.

A fractional calculus perspective in the evolutionary design of combinational circuits

This paper analyses the performance of a genetic algorithm (GA) in the synthesis of digital circuits using two novel approaches. The first concept consists in improving the static fitness function by including a discontinuity evaluation. The measure of variability in the error of the Boolean table has similarities with the function continuity issue in classical calculus. The second concept extends the static fitness by introducing a fractional-order dynamical evaluation.

Uma metaheurística para a programação de projectos com multi-modos e recursos limitados

Este artigo apresenta uma nova abordagem (MM-GAV-FBI), aplicável ao problema da programação de projectos com restrições de recursos e vários modos de execução por actividade, problema conhecido na literatura anglo-saxónica por MRCPSP. Cada projecto tem um conjunto de actividades com precedências tecnológicas definidas e um conjunto de recursos limitados, sendo que cada actividade pode ter mais do que um modo de realização. A programação dos projectos é realizada com recurso a um esquema de geração de planos (do inglês Schedule Generation Scheme - SGS) integrado com uma metaheurística. A metaheurística é baseada no paradigma dos algoritmos genéticos. As prioridades das actividades são obtidas a partir de um algoritmo genético. A representação cromossómica utilizada baseia-se em chaves aleatórias. O SGS gera planos não-atrasados. Após a obtenção de uma solução é aplicada uma melhoria local. O objectivo da abordagem é encontrar o melhor plano (planning), ou seja, o plano que tenha a menor duração temporal possível, satisfazendo as precedências das actividades e as restrições de recursos. A abordagem proposta é testada num conjunto de problemas retirados da literatura da especialidade e os resultados computacionais são comparados com outras abordagens. Os resultados computacionais validam o bom desempenho da abordagem, não apenas em termos de qualidade da solução, mas também em termos de tempo útil.

An optimization approach for the job shop scheduling problem

This paper presents an optimization approach for the job shop scheduling problem (JSSP). The JSSP is a difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms. The proposed approach is based on a genetic algorithm technique. The scheduling rules such as SPT and MWKR are integrated into the process of genetic evolution. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities and delay times of the operations are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set of standard instances taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed approach.

Manipulator trajectory planning using a MOEA

Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.

Desenvolvimento de modelo para a melhoria do planeamento da manutenção

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Delay approximation of fractional integrals

This paper explores the calculation of fractional integrals by means of the time delay operator. The study starts by reviewing the memory properties of fractional operators and their relationship with time delay. Based on the time response of the Mittag-Leffler function an approximation of fractional integrals consisting of time delayed samples is proposed. The tuning of the approximation is optimized by means of a genetic algorithm. The results demonstrate the feasibility of the new perspective and the limits of their application.

Análise numérica e experimental de um modelo dinâmico da ponte ferroviária de Antuã

Mestrado em Engenharia Civil – Ramo Estruturas

Adapting Meeting Tools to Agent Decision

Electricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.

Análise dinâmica de composições musicais

A composição musical é um tema de muito interesse para a computação evolucionária dentro da área da inteligência artificial. É uma área que tem sofrido vários desenvolvimentos ao longo dos últimos anos pois o interesse em que hajam computadores que façam obras musicais é deveras aliciante. Este trabalho tem por objectivo realizar mais um passo nesse sentido. Assim, foi desenvolvida uma aplicação informática que realiza composições musicais de dois géneros distintos: Músicas Infantis e Músicas Blues. A aplicação foi implementada com recurso aos Algoritmos Genéticos, que são os algoritmos evolucionários mais populares da área da computação evolucionária. O trabalho foi estruturado em duas fases de desenvolvimento. Na primeira fase, realizou-se um levantamento estatístico sobre as características específicas de cada um dos géneros musicais. Analisaram-se quinze músicas de cada género musical, com o intuito de se chegar a uma proporção do uso que cada nota tem em cada um dos casos. Na segunda fase, desenvolveu-se o software que compõe as músicas com implementação de um algoritmo genético. Além disso, foi também desenvolvida uma interface gráfica que permite ao utilizador a escolha do género musical que pretende compor. O algoritmo genético começa por gerar uma população inicial de potenciais soluções de acordo com a escolha do utilizador, realizando, de seguida, o ciclo que caracteriza o algoritmo genético. A população inicial é constituída por soluções que seguem as regras que foram implementadas de acordo com os dados recolhidos ao longo da primeira fase. Foi também implementada uma interface de avaliação, através da qual, o utilizador pode ouvir cada uma das músicas para posterior avaliação em termos de fitness. O estado de evolução do algoritmo é apresentado, numa segunda interface, a qual facilita a clareza e justiça na avaliação ao longo de todo o processo. Esta última apresenta informação sobre a média das fitness da geração anterior e actual, sendo assim possível ter uma noção da evolução do algoritmo, no sentido de se obterem resultados satisfatórios no que diz respeito às composições musicais.