ICUAS'23 Paper Abstract

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Paper FrB3.3

de Oliveira, Iure Rosa Lima (Universidade Federal de Viçosa), de Carvalho, Kevin Braathen (Universidade Federal de Viçosa), Brandao, Alexandre Santos (Universidade Federal de Viçosa)

UAV Navigation in 3D Urban Environments with Curriculum-Based Deep Reinforcement Learning

Scheduled for presentation during the Regular Session "Navigation" (FrB3), Friday, June 9, 2023, 14:40−15:00, Room 464

2023 International Conference on Unmanned Aircraft Systems (ICUAS), June 6-9, 2023, Lazarski University, Warsaw, Poland

This information is tentative and subject to change. Compiled on March 28, 2024

Keywords Navigation, Path Planning, Aerial Robotic Manipulation

Abstract

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV's decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 220 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments.

 

 

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