Determination of optimum robot base location considering discrete end-effector positions by means of hybrid genetic algorithm

Fuzzy optimisation based symbolic grounding for service robots

“…The optimal configuration can be determined by evaluating a solution space using the objective function (Zhao et al, 1992, Hsu et al, 1999, Mitsi et al, 2008, Berenson et al, 2008. Limitations in the optimisation based approaches can be summarised as: 1) the optimal robot configuration may not be reachable since the influences of obstacles are not taken into account (Zhao et al, 1992); 2) object grasping is not considered (Hsu et al, 1999); 3) time spent for searching the solution space is too long which may not be able to meet the requirement of realtime task implementation (Mitsi et al, 2008); uncertainty of object location is not considered which means the object location must be precisely known (Berenson et al, 2008). …”

“…In optimisation based approach, an objective function or evaluation function is often define based on certain criteria such as grasp quality, manipulability measure of the arm, distance to the obstacles, task execution time, etc. The optimal configuration can be determined by evaluating a solution space (Zhao et al, 1992, Hsu et al, 1999, Mitsi et al, 2008, Berenson et al, 2008). In analytic model based approaches, the workspace of the robot is analysed and a model which measures how easily a specific robot configuration can be reached is generated.…”

“…The problem of positioning error of the robot base is also not addressed in this work. Mitsi et al (2008) dealt the problem of determining the optimalbase location and joint angles for a robot to reach some prescribed end-effector poses with an optimisation algorithm. The optimisation is conducted through a hybrid heuristic method that combines the advantages of a genetic algorithm (GA), a quasiNewton algorithm (QNA) and a constraints handling method (CHM).…”

An approach to base placement for effective collaboration of multiple autonomous industrial robots

“…Mitsi et al [9] developed an optimization based method that combines genetic algorithm, quasi Newton algorithm and constraints handling methods to find the optimal placement of a single manipulator by considering discrete end-effector positions. Vosniakos et al [10] presented an approach that uses genetic algorithm and considers maximizing manipulability of a single manipulator to deal with the high accuracy required for the milling application.…”

Optimización de manipulabilidad y consumo eléctrico mediante el Algoritmo Heurístico de Kalman en manipuladores seriales

“…La eficiencia con la cual los desplazamientos articulares de un robot se convierten en movimientos de su órgano terminal, llamado índice de manipulabilidad, es un criterio usado para cuantificar este desempeño. Uno de los métodos más comunes encontrados en la literatura para medir la manipulabilidad es el propuesto por Yoshikawa [3] y ha sido usado como base en trabajos que desarrollan métodos para planeación de trayectorias en manipuladores robóticos [4], [5], [6].…”