Title :
A fitness-sharing based genetic algorithm for collaborative Multi Robot Localization
Author :
Bori, Francesco ; Gasparri, Andrea ; Panzieri, Stefano
Author_Institution :
Dept. of Comput. Sci., Univ. of Roma Tre, Rome, Italy
Abstract :
In this paper, a novel genetic algorithm based on a ¿collaborative¿ fitness-sharing technique to deal with the Multi-Robot Localization problem is proposed. Indeed, the use of the fitness-sharing is twofold and competitive. It preserves the diversity among individuals during the space exploration process, thus maintaining evolutionary niches over time, and reinforces the best hypotheses by means of collaboration among robots, thus augmenting the selection pressure. Simulations by exploiting the robotics framework Player/Stage have been performed along with a proper statistical analysis for performance assessment.
Keywords :
genetic algorithms; multi-robot systems; simulation; statistical analysis; collaborative multi robot localization; fitness-sharing; genetic algorithm; performance assessment; simulations; statistical analysis; Collaborative work; Genetic algorithms; Intelligent robots; International collaboration; Multirobot systems; Navigation; Orbital robotics; Robot localization; Robot sensing systems; USA Councils;
Conference_Titel :
Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on
Conference_Location :
St. Louis, MO
Print_ISBN :
978-1-4244-3803-7
Electronic_ISBN :
978-1-4244-3804-4
DOI :
10.1109/IROS.2009.5354581