Object Detection and Reinforcement Learning Approach for Intelligent Control of UAV

Miljković, Zoran; Jevtić, Dragan

doi:10.1007/978-3-031-05230-9_79

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2023

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“…Different from other approaches, this research aims to investigate the performance of a Q-learning-based collision avoidance system for UAVs, which is a part of the larger navigation system presented in [4].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-based Collision Avoidance for UAV

Jevtić,

Miljković,

Petrović

et al. 2023

2023 10th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN)

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One of the significant aspects for enabling the intelligent behavior to the Unmanned Aerial Vehicles (UAVs) is by providing an algorithm for navigation through the dynamic and unseen environment. Therefore, to be autonomous, they need sensors to perceive their surroundings and utilize gathered information to decide which action to take. Having that in mind, in this paper, the authors designed the system for obstacle avoidance and also investigate the elements of the Markov decision process and their influence on each other. The flying mobile robot used within the considered problem is quadrotor type and has an integrated Lidar sensor which is utilized to detect obstacles. The sequential decision-making model based on Q-learning is trained within the MATLAB Simulink environment. The simulation results demonstrate that the UAV can navigate through the environment in most algorithm runs without colliding with surrounding obstacles.

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Section: Introductionmentioning

confidence: 99%