Path planning based on Genetic Algorithms and the Monte-Carlo method to avoid aerial vehicle collisions under uncertainties

This paper presents a collision-free path planning method for an aerial vehicle sharing airspace with other aerial vehicles. It is based on grid models and genetic algorithms to find safe trajectories. Monte-Carlo method is used to evaluate the best predicted trajectories considering different sourc...

Full description

Saved in:
Bibliographic Details
Main Authors: Cobano, J. A., Conde, R., Alejo, D., Ollero, A.
Format: Conference Proceeding
Language:English
Subjects:
Atmospheric modeling
Computational modeling
Genetic algorithms
Prediction algorithms
Trajectory
Uncertainty
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a collision-free path planning method for an aerial vehicle sharing airspace with other aerial vehicles. It is based on grid models and genetic algorithms to find safe trajectories. Monte-Carlo method is used to evaluate the best predicted trajectories considering different sources of uncertainty such as the wind, the inaccuracies in the vehicle model and limitations of on-board sensors and control system.
ISSN:1050-4729
2577-087X
DOI:10.1109/ICRA.2011.5980246