The diffusion of the Unmanned Aerial Vehicles (UAVs) requires a suitable approach to define safe flight operations. In this paper, an innovative algorithm able to quantify the risk to the population and to search for the minimum risk path is proposed. The method has two main phases: in the former, a risk-map is generated quantifying the risk of a specific area, in the latter, a path planning algorithm seeks for the optimal path minimizing the risk. The risk-map is generated with a risk assessment method combining layers related to the population density, the sheltering factor, no-fly zones and obstacles. The risk-aware path planning is based on the well-known Optimal Rapidly-exploring Random Tree, with the minimization of the risk cost with respect to the flight time. Simulation results corroborate the validity of the approach.

An Innovative Algorithm to Estimate Risk Optimum Path for Unmanned Aerial Vehicles in Urban Environments / Primatesta, Stefano; SPANO' CUOMO, Luca; Guglieri, Giorgio; Rizzo, Alessandro. - In: TRANSPORTATION RESEARCH PROCEDIA. - ISSN 2352-1465. - ELETTRONICO. - 35:(2018), pp. 44-53. ((Intervento presentato al convegno International Conference on Air Transport - INAIR 2018 tenutosi a Hainburg an der Donau, Austria nel 20-21 November 2018 [10.1016/j.trpro.2018.12.006].

An Innovative Algorithm to Estimate Risk Optimum Path for Unmanned Aerial Vehicles in Urban Environments

Stefano Primatesta;SPANO' CUOMO, LUCA;Giorgio Guglieri;Alessandro Rizzo
2018

Abstract

The diffusion of the Unmanned Aerial Vehicles (UAVs) requires a suitable approach to define safe flight operations. In this paper, an innovative algorithm able to quantify the risk to the population and to search for the minimum risk path is proposed. The method has two main phases: in the former, a risk-map is generated quantifying the risk of a specific area, in the latter, a path planning algorithm seeks for the optimal path minimizing the risk. The risk-map is generated with a risk assessment method combining layers related to the population density, the sheltering factor, no-fly zones and obstacles. The risk-aware path planning is based on the well-known Optimal Rapidly-exploring Random Tree, with the minimization of the risk cost with respect to the flight time. Simulation results corroborate the validity of the approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2713113
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