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Exploring feasibility of reinforcement learning flight route planning
Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Arts and Sciences.
2021 (English)Independent thesis Basic level (degree of Bachelor), 12 credits / 18 HE creditsStudent thesisAlternative title
Undersökning av använding av förstärkningsinlärning för flyruttsplannering (Swedish)
Abstract [en]

This thesis explores and compares traditional and reinforcement learning (RL) methods of performing 2D flight path planning in 3D space. A wide overview of natural, classic, and learning approaches to planning s done in conjunction with a review of some general recurring problems and tradeoffs that appear within planning. This general background then serves as a basis for motivating different possible solutions for this specific problem. These solutions are implemented, together with a testbed inform of a parallelizable simulation environment. This environment makes use of random world generation and physics combined with an aerodynamical model. An A* planner, a local RL planner, and a global RL planner are developed and compared against each other in terms of performance, speed, and general behavior. An autopilot model is also trained and used both to measure flight feasibility and to constrain the planners to followable paths. All planners were partially successful, with the global planner exhibiting the highest overall performance. The RL planners were also found to be more reliable in terms of both speed and followability because of their ability to leave difficult decisions to the autopilot. From this it is concluded that machine learning in general, and reinforcement learning in particular, is a promising future avenue for solving the problem of flight route planning in dangerous environments.

Place, publisher, year, edition, pages
2021. , p. 36
Keywords [en]
SAAB, flight route planning, autorouting, auto-routing, auto routing, AI, machine learning, fighter jet, convolution, PPO, DQN, Astar, A*, C++, Python, LibTorch, PyTorch, multi threading, multi-threading, simulation, aerodynamics, world generation, Perlin noise, investigation, reward
Keywords [sv]
Flygplanering, flygruttsplannering, maskininlärning, AI, SAAB, faltning, faltningslager, belöning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-178314ISRN: LIU-IDA/KOGVET-G–21/031—SEOAI: oai:DiVA.org:liu-178314DiVA, id: diva2:1585642
External cooperation
Dynorobotics AB
Subject / course
Cognitive science
Supervisors
Examiners
Available from: 2021-09-01 Created: 2021-08-17 Last updated: 2021-09-01Bibliographically approved

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Computer Vision and Robotics (Autonomous Systems)

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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