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.

A recurrent planning strategy for UAV optimum path identification in a dynamic environment based on bit-coded flight manoeuvres

Luciano Blasi
;
Massimiliano Mattei;
2020

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.
2020
978-172814277-7
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/436853
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 1
social impact