dc.contributor.advisor | Nokleby, Scott B. | |
dc.contributor.author | Goodwin, Lillian | |
dc.date.accessioned | 2022-05-17T16:25:46Z | |
dc.date.accessioned | 2022-06-14T18:08:09Z | |
dc.date.available | 2022-05-17T16:25:46Z | |
dc.date.available | 2022-06-14T18:08:09Z | |
dc.date.issued | 2022-05-01 | |
dc.identifier.uri | https://hdl.handle.net/10155/1457 | |
dc.description.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. | en |
dc.description.sponsorship | University of Ontario Institute of Technology | en |
dc.language.iso | en | en |
dc.subject | Multi-Robot Systems (MRS) | en |
dc.subject | Frontier exploration | en |
dc.subject | K-means | en |
dc.subject | Robot Operating System | en |
dc.subject | Optimization | en |
dc.title | A robust and efficient autonomous exploration methodology of unknown environments for multi-robot systems | en |
dc.type | Thesis | en |
dc.degree.level | Master of Applied Science (MASc) | en |
dc.degree.discipline | Mechanical Engineering | en |