Research Article
Bio-inspired and Voronoi-based Algorithms for Self-positioning of Autonomous Vehicles in Noisy Environments
@INPROCEEDINGS{10.4108/icst.bict.2014.257917, author={jianmin zou and Stephen Gundry and Janusz Kusyk and Cem Sahin and Umit Uyar}, title={Bio-inspired and Voronoi-based Algorithms for Self-positioning of Autonomous Vehicles in Noisy Environments}, proceedings={8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)}, publisher={ICST}, proceedings_a={BICT}, year={2015}, month={2}, keywords={genetic algorithms self-organizing networks topology con trol voronoi tessellation manets}, doi={10.4108/icst.bict.2014.257917} }- jianmin zou
Stephen Gundry
Janusz Kusyk
Cem Sahin
Umit Uyar
Year: 2015
Bio-inspired and Voronoi-based Algorithms for Self-positioning of Autonomous Vehicles in Noisy Environments
BICT
ACM
DOI: 10.4108/icst.bict.2014.257917
Abstract
Many topology control methods for autonomous mobile vehicles assume exact knowledge of the locations of neighboring nodes to make meaningful movement decisions. We present our node-spreading Voronoi algorithm (NSVA) and node-spreading Voronoi-based genetic algorithm (NSVGA), for self-positioning autonomous nodes in noisy environments. The performance of NSVA and NSVGA were evaluated in simulation experiments by measuring the network area coverage, average distance traveled and number of disconnected nodes. Experimental results show that both NSVA and NSVGA can adequately cover the deployment area despite errors in neighbor location information. NSVGA can tolerate location errors and maintain network connectivity better than NSVA at the cost of increased movement.