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

Published online by Cambridge University Press:  10 March 2014

Fares J. Abu-Dakka ,
Francisco J. Valero ,
Jose Luis Suñer  and
Vicente Mata
Fares J. Abu-Dakka*
Affiliation:
Centro de Investigación en Tecnología de Vehículos, Universitat Politècnica de València, Valencia 46022, Spain
Francisco J. Valero
Affiliation:
Centro de Investigación en Tecnología de Vehículos, Universitat Politècnica de València, Valencia 46022, Spain
Jose Luis Suñer
Affiliation:
Centro de Investigación en Tecnología de Vehículos, Universitat Politècnica de València, Valencia 46022, Spain
Vicente Mata
Affiliation:
Centro de Investigación en Tecnología de Vehículos, Universitat Politècnica de València, Valencia 46022, Spain
*
*Corresponding author. E-mail: fabudakk@ing.uc3m.es
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Summary

This paper presents a new genetic algorithm methodology to solve the trajectory planning problem. This methodology can obtain smooth trajectories for industrial robots in complex environments using a direct method. The algorithm simultaneously creates a collision-free trajectory between initial and final configurations as the robot moves. The presented method deals with the uncertainties associated with the unknown kinematic properties of intermediate via points since they are generated as the algorithm evolves looking for the solution. Additionally, the objective of this algorithm is to minimize the trajectory time, which guides the robot motion. The method has been applied successfully to the PUMA 560 robotic system. Four operational parameters (execution time, computational time, end-effector distance traveled, and significant points distance traveled) have been computed to study and analyze the algorithm efficiency. The experimental results show that the proposed optimization algorithm for the trajectory planning problem of an industrial robot is feasible.

Keywords

RoboticsTrajectory planningObstacles avoidanceGenetic algorithms
Type
Articles
Information
Robotica , Volume 33 , Issue 3 , March 2015 , pp. 669 - 683
Copyright
Copyright © Cambridge University Press 2014 

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