Item – Theses Canada

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OCLC number
1344011749
Link(s) to full text
LAC copy
Author
Goodwin, Lillian.
Title
A robust and efficient autonomous exploration methodology of unknown environments for multi-robot systems.
Degree
MASc -- University of Ontario Institute of Technology, 2022
Publisher
[Oshawa, Ontario] : University of Ontario Institute of Technology, 2022
Description
1 online resource
Abstract
Multi-robot systems can provide effective solutions for exploring and inspecting environments where it is unpractical or unsafe for humans, however, adequate coordination of the multi-robot system is a challenging initiative. A robust and efficient methodology for exploration of unknown environments is presented using a k-means method to improve traditional task allocation schemes. The k-means method proposed is an efficient technique due to the algorithm's quick convergence time and its ability to segment a previously unknown map in a logical manner. In this method, a global executive receives frontiers from local robots, filters them, clusters them using the k-means method, and then reassigns them to the agents. A framework is developed in Robot Operating System (ROS) to test the effectiveness of the k-means method. The method is tested over a series of simulations and real-world tests, where it provided significant reductions in exploration time and distance travelled compared to other methods.
Other link(s)
hdl.handle.net
ir.library.ontariotechu.ca
Subject
Multi-Robot Systems (MRS)
Frontier exploration
K-means
Robot Operating System
Optimization