ICUAS 2021 Paper Abstract

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Paper FrB3.4

Sanna, Giovanni (Politecnico di Torino), Godio, Simone (Politecnico di Torino), Guglieri, Giorgio (Politecnico di Torino)

Neural Network Based Algorithm for Multi-UAV Coverage Path Planning

Scheduled for presentation during the Regular Session "Neural Networks" (FrB3), Friday, June 18, 2021, 12:00−12:20, Edessa

2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece

This information is tentative and subject to change. Compiled on March 29, 2024

Keywords Path Planning, Swarms, Biologically Inspired UAS

Abstract

This paper discusses the Complete Coverage Path Planning (CCPP) of a complex-shaped areas executed by a fleet of AI-driven UAVs. Decision-making is delivered by a balanced “explicit vs implicit" programming. The algorithm relies on a mixed-use of decentralized Artificial Neural Networks (ANN) which confers elementary cognitive skills to each UAV, and a modified version of the famous A* pathfinder. The neural network training session imitates Human Priors in a multi-class classification, which completely bypasses common drawbacks such as the need for large databases or high computational resources. The case study focuses on complex urban areas, for which the grid resolution of the traditional approaches can't model the problem with sufficient accuracy.

 

 

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