Trajectory planning for industrial robot using genetic algorithms

Abstract

En el nombre de Allah, el Misericordioso, el Compasivo(1) ¡Lee en el nombre de su Señor que ha creado! (2) Ha creado al hombre de un coágulo. ABSTRACTIn the last decades, many researches have been proposed concerning the path and trajectory planning for manipulators. Path and trajectory planning have important applications in many areas, for example industrial robotics, autonomous systems, virtual prototyping, and computer-aided drug design. On the other hand, the evolutionary algorithms have been applied in this plethora of fields, which motivates the author's interest on its application to the path and trajectory planning for industrial robots.In this work, an exhaustive search of the existing literature related to the thesis has been carried out, which has served to create a comprehensive database used to perform a detailed historical review of developments since its origins to the current state of the art and the latest trends.This thesis presents a new methodology that uses genetic algorithms to develop and evaluate path and trajectory planning algorithms. Problem-specific knowledge and heuristic knowledge are incorporated into encoding, evaluation and genetic operators of the genetic algorithm.This methodology introduces new approaches that aim at solve the problem of path planning and trajectory planning for industrial robotic systems operating in 3D environments with static obstacles. Therefore, two algorithms (somehow, they are similar, but with some variations) are created to solve the mentioned planning problems.Obstacles modeling have been done by using combinations of simple geometric objects (spheres, cylinders, and plans) which provide an efficient algorithm for collision avoidance. Trajectory Planning for Industrial Robots Using Genetic AlgorithmsPath planning algorithm is based on global genetic algorithms optimization techniques, which aim to minimize the sum of the distances between significant points of the robot along the path considering the restrictions to avoid collisions with obstacles. The path is composed of adjacent configurations obtained by an optimization technique using genetic algorithms, seeking to minimize a multiobjective function that involves the distance between significant points of the two adjacent configurations, and the distance from the points of the current configuration to the final one. An evaluation method is designed according to the problem presentation by defining individuals and genetic operators capable of providing efficient solutions to the problem. The result of this algorithm is the shortest path between two configurations given by the user.Trajectory planning algorithm is also based on genetic algorithms optimization techniques using the direct procedure. The algorithm is similar to the mentioned previously algorithm for path planning problem, but with some differences in the objective function and some details related to the conceptual difference between path and trajectory planning. The objective of this algorithm is to minimize the time required to mo...

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

“…In the direct approaches, on the other hand, the search takes place in the system's state space. These approaches involve optimal control and numerical optimization [10][11][12][13]. The method used in this paper is oriented towards the attainment of an algorithm that avoids obtaining a prior path.…”