Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/84848

A Survivability Enhanced Swarm Robotic Searching System Using Multi-Objective Particle Swarm Optimization

Authors Yuen, Cheuk Ho HKUST affiliated (currently or previously)
Woo, Kam Tim View this author's profile
Issue Date 2017
Source Lecture Notes in Computer Science , v. 10386, 24 Jun 2017, p. 167-175
Summary This paper aims at outlining an algorithm for groups of swarm robots solely powered by light energy to survive and complete target searching tasks in unknown fields where light energy charging points and targets are scattered. To sustain the searching operation and solve energy consumption conflicts between surviving and searching, this paper introduces a multi-robot algorithm based on Multi-Objective Particle Swarm Optimization (MOPSO) and energy-saving decision rules. A novel mechanism of selecting the best performing particle in PSO is introduced. Several sets of simulation experiments were conducted and results show that a 15-robot swarm system running this algorithm is able to search a single target and stabilize the energy level for the long-term simultaneously. It demonstrates the feasibility of applying this energy-optimized MOPSO as a design framework for a long-term searching swarm robot system.
Conference The Eighth International Conference on Swarm Intelligence (ICSI 2017), Fukuoka, Japan, 27 July - 1 August 2017
Publisher Springer International Publishing
Subjects
ISBN 9783319618326
3319618326
Language English
Format Conference paper
Access View full-text via DOI
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