國立虎尾科技大學 |

Data-Driven Predictive Modeling to Enhance Search Efficiency of Glowworm-Inspired Robotic Swarms in Multiple Emission Source Localization Tasks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Data-Driven Predictive Modeling to Enhance Search Efficiency of Glowworm-Inspired Robotic Swarms in Multiple Emission Source Localization Tasks./
作者:	Nandi, Payal.
面頁冊數:	1 online resource (75 pages)
附註:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
標題:	Mechanical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798380392921

Data-Driven Predictive Modeling to Enhance Search Efficiency of Glowworm-Inspired Robotic Swarms in Multiple Emission Source Localization Tasks.
Nandi, Payal.

Data-Driven Predictive Modeling to Enhance Search Efficiency of Glowworm-Inspired Robotic Swarms in Multiple Emission Source Localization Tasks. - 1 online resource (75 pages)

Source: Masters Abstracts International, Volume: 85-03.

Thesis (M.S.)--Old Dominion University, 2023.

Includes bibliographical references

In time-sensitive search and rescue applications, a team of multiple mobile robots broadens the scope of operational capabilities. Scaling multi-robot systems (< 10 agents) to larger robot teams (10 - 100 agents) using centralized coordination schemes becomes computationally intractable during runtime. One solution to this problem is inspired by swarm intelligence principles found in nature, offering the benefits of decentralized control, fault tolerance to individual failures, and self-organizing adaptability. Glowworm swarm optimization (GSO) is unique among swarm-based algorithms as it simultaneously focuses on searching for multiple targets. This thesis presents GPR-GSO-a modification to the GSO algorithm that incorporates Gaussian Process Regression (GPR) based data-driven predictive modeling-to improve the search efficiency of robotic swarms in multiple emission source localization tasks. The problem formulation and methods are presented, followed by numerical simulations to illustrate the working of the algorithm. Results from a comparative analysis show that the GPR-GSO algorithm exceeds the performance of the benchmark GSO algorithm on evaluation metrics of swarm size, search completion time, and travel distance.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380392921Subjects--Topical Terms:

557493
Mechanical engineering.
Subjects--Index Terms:

AlgorithmIndex Terms--Genre/Form:

554714
Electronic books.

Data-Driven Predictive Modeling to Enhance Search Efficiency of Glowworm-Inspired Robotic Swarms in Multiple Emission Source Localization Tasks.
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