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Using Machine Learning Techniques for Autonomous Planning and Navigation with Groups of Unmanned Vehicles

Bergwerff, G.R. (2016) Using Machine Learning Techniques for Autonomous Planning and Navigation with Groups of Unmanned Vehicles. Master's Thesis / Essay, Artificial Intelligence.

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Abstract

Planning trajectories of multiple unmanned vehicles is a complicated task with a large amount of possible solutions. In recent research different types of algorithms are used to solve these planning issues with mixed results. In this research we use two different types of machine learning algorithms and compare these against a baseline greedy method. The first method is based on reinforcement learning (RL) with features, the second method is based on multi ant colony systems (MACS). To measure the performance of the algorithms we created a grid world environment with a task where a number of UAVs need to visit a number of areas. When testing both the RL and MACS algorithm on this problem, we found that the MACS algorithm gives the best solution but is computationally intensive when the problem is scaled. The RL algorithm scales better but is outperformed by the greedy method, making the MACS algorithm the best performing among the tested algorithms.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:14
Last Modified: 15 Feb 2018 08:14
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/14261

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