2,729 research outputs found
An Evolutionary and Local Search Algorithm for Motion Planning of Two Manipulators
A method for obtaining coordinated motion plans of robot manipulators is presented.
A decoupled planning approach has been used; that is, the problem has been
decomposed into two subproblems: path planning, where a collision-free path is
found for each robot independently only considering fixed obstacles, and trajectory
planning, where the paths are timed and synchronized to avoid collisions with other
robots. This article focuses on the second problem. The proposed plan can easily be
implemented by programs written in most industrial robot programming languages.
The generated programs minimize the total motion time of the robots along their
paths. The method does not require accurate dynamic models of the robots and uses
an evolutionary algorithm followed by a local search which produces near optimal
solutions with a relatively small computational cost
Using Genetic Algorithms with Variable-length Individuals for Planning Two-Manipulators Motion
International Conference on Artificial Neural Networks and Genetic Algorithms. 01/01/1997. NorwichA method based on genetic algorithms for obtaining coordinated motion plans of manipulator robots is presented. A decoupled planning approach has been used; that is, the problem has been decomposed into two subproblems: path planning and trajectory planning. This paper focuses on the second problem. The generated plans minimize the total motion time of the robots along their paths. The optimization problem is solved by evolutionary algorithms using a variable-length individuals codification and specific genetic operators
The Ariadne's Clew Algorithm
We present a new approach to path planning, called the "Ariadne's clew
algorithm". It is designed to find paths in high-dimensional continuous spaces
and applies to robots with many degrees of freedom in static, as well as
dynamic environments - ones where obstacles may move. The Ariadne's clew
algorithm comprises two sub-algorithms, called Search and Explore, applied in
an interleaved manner. Explore builds a representation of the accessible space
while Search looks for the target. Both are posed as optimization problems. We
describe a real implementation of the algorithm to plan paths for a six degrees
of freedom arm in a dynamic environment where another six degrees of freedom
arm is used as a moving obstacle. Experimental results show that a path is
found in about one second without any pre-processing
Point trajectory planning of flexible redundant robot manipulators using genetic algorithms
The paper focuses on the problem of point-to-point trajectory planning for flexible redundant robot manipulators (FRM) in joint space. Compared with irredundant flexible manipulators, a FRM possesses additional possibilities during point-to-point trajectory planning due to its kinematics redundancy. A trajectory planning method to minimize vibration and/or executing time of a point-to-point motion is presented for FRMs based on Genetic Algorithms (GAs). Kinematics redundancy is integrated into the presented method as planning variables. Quadrinomial and quintic polynomial are used to describe the segments that connect the initial, intermediate, and final points in joint space. The trajectory planning of FRM is formulated as a problem of optimization with constraints. A planar FRM with three flexible links is used in simulation. Case studies show that the method is applicable
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
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Artificial intelligence makes computers lazy
This paper looks at the age-old problem of trying to instil some degree of intelligence in computers. Genetic Algorithms (GA) and Genetic Programming (GP) are techniques that are used to evolve a solution to a problem using processes that mimic natural evolution. This paper reflects on the experience gained while conducting research applying GA and GP to two quite different problems: Medical Diagnosis and Robot Path Planning. An observation is made that when these algorithms are not applied correctly the computer seemingly exhibits lazy behaviour, arriving at a suboptimal solutions. Using examples, this paper shows how this 'lazy' behaviour can be overcome
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
Trajectory planning for industrial robot using genetic algorithms
En las últimas décadas, debido la importancia de sus aplicaciones, se han propuesto muchas investigaciones sobre la planificación de caminos y trayectorias para los manipuladores, algunos de los ámbitos en los que pueden encontrarse ejemplos de aplicación son; la robótica industrial, sistemas autónomos, creación de prototipos virtuales y diseño de fármacos asistido por ordenador. Por otro lado, los algoritmos evolutivos se han aplicado en muchos campos, lo que motiva el interés del autor por investigar sobre su aplicación a la planificación de caminos y trayectorias en robots industriales.
En este trabajo se ha llevado a cabo una búsqueda exhaustiva de la literatura existente relacionada con la tesis, que ha servido para crear una completa base de datos utilizada para realizar un examen detallado de la evolución histórica desde sus orígenes al estado actual de la técnica y las últimas tendencias.
Esta tesis presenta una nueva metodología que utiliza algoritmos genéticos para desarrollar y evaluar técnicas para la planificación de caminos y trayectorias. El conocimiento de problemas específicos y el conocimiento heurístico se incorporan a la codificación, la evaluación y los operadores genéticos del algoritmo.
Esta metodología introduce nuevos enfoques con el objetivo de resolver el problema de la planificación de caminos y la planificación de trayectorias para sistemas robóticos industriales que operan en entornos 3D con obstáculos estáticos, y que ha llevado a la creación de dos algoritmos (de alguna manera similares, con algunas variaciones), que son capaces de resolver los problemas de planificación mencionados.
El modelado de los obstáculos se ha realizado mediante el uso de combinaciones de objetos geométricos simples (esferas, cilindros, y los planos), de modo que se obtiene un algoritmo eficiente para la prevención de colisiones.
El algoritmo de planificación de caminos se basa en técnicas de
optimización globales, usando algoritmos genéticos para minimizar una función
objetivo considerando restricciones para evitar las colisiones con los obstáculos. El
camino está compuesto de configuraciones adyacentes obtenidas mediante una
técnica de optimización construida con algoritmos genéticos, buscando minimizar
una función multiobjetivo donde intervienen la distancia entre los puntos
significativos de las dos configuraciones adyacentes, así como la distancia desde
los puntos de la configuración actual a la final. El planteamiento del problema
mediante algoritmos genéticos requiere de una modelización acorde al
procedimiento, definiendo los individuos y operadores capaces de proporcionar
soluciones eficientes para el problema.Abu-Dakka, FJM. (2011). Trajectory planning for industrial robot using genetic algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10294Palanci
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