By Topic

Biogeography-based Optimization approach based on Predator-Prey concepts applied to path planning of 3-DOF robot manipulator

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Silva, M.A.C. ; Pontifical Catholic Univ. of Parana (PUCPR), Curitiba, Brazil ; dos S Coelho, L. ; Freire, R.Z.

A fundamental problem in robotics consists in trajectory planning. The main task of path planning for robot manipulators is to find an optimal collision-free trajectory from an initial to a final configuration. Furthermore, trajectory planning is devoted to generate the reference inputs for the control system of the manipulator, so as to be able to execute the motion. Many important contributions to this problem have been made in recent years. Recently, techniques based on metaheuristics of natural computing, mainly evolutionary algorithms (EA), have been successfully applied to a large number of robotic applications, including the generation of optimized trajectories for robot manipulators. The aim of this paper is to evaluate a modified Biogeography-based Optimization (BBO) approach based on Predator-Prey concepts (PPBBO) to solve the trajectory planning of a robot manipulator. Simulation experiments are carried on a robot manipulator with three degrees of freedom (3-DOF) to illustrate the efficacy of the BBO approach. Biogeography deals with the geographical distribution of biological organisms. BBO is an optimization method which is motivated by the nature's way of distributing habitats. Similar to genetic algorithms, BBO is a population-based stochastic global optimizer. However, in BBO, problem solutions are represented as islands, and the sharing of features between solutions is represented as migration between islands. Results demonstrated that the proposed PPBBO approach converged to promising solutions in terms of quality and convergence rate when compared with the classical BBO.

Published in:

Emerging Technologies and Factory Automation (ETFA), 2010 IEEE Conference on

Date of Conference:

13-16 Sept. 2010