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Path planning for autonomous aerial vehicles using Monte Carlo tree search

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dc.contributor.authorVasutapituks, Apichart, author
dc.contributor.authorChong, Edwin K. P., advisor
dc.contributor.authorAzimi-Sadjadi, Mahmood, committee member
dc.contributor.authorPinaud, Olivier, committee member
dc.contributor.authorPezeshki, Ali, committee member
dc.date.accessioned2024-05-27T10:32:59Z
dc.date.available2024-05-27T10:32:59Z
dc.date.issued2024
dc.description.abstractUnmanned aerial vehicles (UAVs), or drones, are widely used in civilian and defense applications, such as search and rescue operations, monitoring and surveillance, and aerial photography. This dissertation focuses on autonomous UAVs for tracking mobile ground targets. Our approach builds on optimization-based artificial intelligence for path planning by calculating approximately optimal trajectories. This approach poses a number of challenges, including the need to search over large solution spaces in real-time. To address these challenges, we adopt a technique involving a rapidly-exploring random tree (RRT) and Monte Carlo tree search (MCTS). The RRT technique increases in computational cost as we increase the number of mobile targets and the complexity of the dynamics. Our MCTS approach executes a tree search based on random sampling to generate trajectories in real time. We develop a variant of MCTS for online path-planning to track ground targets together with an associated algorithm called P-UAV. Our algorithm is based on the framework of partially observable Monte Carlo planning, originally developed in the context of MCTS for Markov decision processes. Our real-time approach exploits a parallel-computing strategy with a heuristic random-sampling process. In our framework, We explicitly incorporate threat evasion, obstacle collision avoidance, and resilience to wind. The approach embodies an exploration-exploitation tradeoff in seeking a near-optimal solution in spite of the huge search space. We provide simulation results to demonstrate the effectiveness of our path-planning method.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierVasutapituks_colostate_0053A_18371.pdf
dc.identifier.urihttps://hdl.handle.net/10217/238537
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectMonte Carlo tree search
dc.subjectpartially observable Markov decision processes (POMDP)
dc.subjectunmanned aerial vehicles
dc.subjectonline path planning
dc.subjectartificial intelligence (AI)
dc.subjectpath planning
dc.titlePath planning for autonomous aerial vehicles using Monte Carlo tree search
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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