The use of micro aerial vehicles (MAV) for civilian use such as exploration and inspection of varying structures, equipment and areas have garnered some interest as of late. MAVs have the mobility and agility to traverse three dimensional space quickly and access hard to reach areas where other alternatives would struggle, but a flying platform such as a MAV comes with it’s own set of distinct problems. Almost any collision with the environment results in a complete failure of the platform. Any exploratory framework would need to perform obstacle avoidance and online path planning in a fully unknown environment with low computation times to ensure that the limited battery resources on the MAV is used in the most efficient way. In this thesis the exploratory rapidly-exploring random tree (ERRT) framework will be further optimized and an efficient strategy for finding valid exploration paths which are not in the immediate vicinity of the MAV will be developed and integrated. The method is demonstrated and proven through results from simulations and real life experiments.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-95126 |
| Date | January 2023 |
| Creators | Löfgren, Kalle |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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