ICUAS 2020 Paper Abstract

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Paper ThA2.5

Bassolillo, Salvatore (University of Campania), Blasi, Luciano (UniversitÓ degli Studi della Campania "L.Vanvitelli"), D'Amato, Egidio (University of Naples "Parthenope"), Mattei, Massimiliano (Seconda UniversitÓ di Napoli), Notaro, Immacolata (University of Campania "Luigi Vanvitelli")

A Recurrent Planning Strategy for UAV Optimum Path Identification in a Dynamic Environment Based on Bit-Coded Flight Manoeuvres

Scheduled for presentation during the Regular Session "Path Planning IV" (ThA2), Thursday, September 3, 2020, 11:20−11:40, Kozani

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on September 25, 2020

Keywords Path Planning, UAS Applications

Abstract

The applicability of a novel algorithm identifying optimal paths for Unmanned Aerial Vehicles in 2-D dynamic environments has been preliminarily assessed in this paper. Optimality is evaluated taking path length or flight time as objective functions. Flight trajectories, compliant with both air vehicle and environmental constraints, are made up of a finite number of circular arcs and straight lines. Such a geometrical sequence is converted into a finite sequence of two bit-coded basic flight manoeuvres. Identification of optimum path is obtained coupling such a manoeuvering model with a Particle Swarm Optimizer (PSO). To deal with the problem of a dynamic environment a recurrent planning strategy has been developed. Following this approach, final path is obtained by performing both the path planning and the path tracking phase within a series of constant time windows. While the air vehicle is tracking the current sub-optimal trajectory, the algorithm identifies a new sub-optimal trajectory, based on the obstacles estimated position at the end of the current time window, that will be tracked by the air vehicle in the next time window. This way, the computed path turns out to be a piecewise sequence of sub-optimal trajectories reaching the destination point. The identification of obstacles future position is carried out by the algorithm only by monitoring their current position. To test the effectiveness of the proposed flight path planner, we set different 2-D scenarios with obstacles having both constant and variable speed.

 

 

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