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Simulation Environment Evaluating AI Algorithms for Search Missions Using Drone Swarms
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5562-1424
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 191-204Conference paper, Published paper (Refereed)
Abstract [en]

Search missions for objects are relevant in both industrial and civilian context, such as searching for a missing child in a forest or to locating equipment in a building or large factory. To send out a drone swarm to quickly locate a misplaced item in a factory, a missing machine on a building site or a missing child in a forest is very similar. Image-based Machine Learning algorithms are now so powerful that they can be trained to identify objects with high accuracy in real time. The next challenge is to perform the search as efficiently as possible, using as little time and energy as possible. If we have information about the area to search, we can use heuristic and probabilistic methods to perform an efficient search. In this paper, we present a case study where we developed a method and approach to evaluate different search algorithms enabling the selection of the most suitable, i.e., most efficient search algorithm for the task at hand. A couple of probabilistic and heuristic search methods were implemented for testing purposes, and they are the following: Bayesian Search together with a Hill Climbing search algorithm and Bayesian Search together with an A-star search algorithm. A swarm adapted lawn mower search strategy is also implemented. In our case study, we see that the performance of the search heavily depends on the area to search in and domain knowledge, e.g., knowledge about how a child is expected to move through a forest area when lost. In our tests, we see that there are significant gains to be made by selecting a search algorithm suitable for the search context at hand.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2024. p. 191-204
Keywords [en]
AI, Drone Swarm, Drones, Optimization, Search and rescue, Search missions, Simulation environment, Swarm, Heuristic algorithms, Heuristic methods, Lawn mowers, Learning algorithms, Machine learning, Statistical tests, Bayesian, Case-studies, Missing children, Optimisations, Search Algorithms
National Category
Robotics
Identifiers
URN: urn:nbn:se:mdh:diva-65359DOI: 10.1007/978-3-031-39619-9_14Scopus ID: 2-s2.0-85181984345ISBN: 9783031396182 (print)OAI: oai:DiVA.org:mdh-65359DiVA, id: diva2:1828599
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved

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Sundelius, NilsFunk, PeterSohlberg, Rickard

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