Adaptive sensitivity decision based path planning algorithm for unmanned aerial vehicle with improved particle swarm optimization

Abstract : Automatic path planning is an essential aspect of unmanned aerial vehicle (UAV) autonomy. This paper presents a three dimensional path planning algorithm based on adaptive sensitivity decision operator combined with particle swarm optimization (PSO) technique. In the proposed method, an adaptive sensitivity decision area is constructed to overcome the defects of local optimal and slow convergence. By using this specified area, the potential particle locations with high probabilities are determined and other candidates are deleted to improve computational capacity. Then the searching space of particles is constrained in a limited boundary to avoid premature state. In addition, the searching accuracy is enhanced by the relative particle directivity from current location. The objective function is redesigned by taking into account the distance to destination and UAV self-constraints. To evaluate the path length, the paired-sample T-Test is performed and the straight line rate (SLR ) index is introduced. In the two scenarios applied in this paper, our proposed method is 35.4%35.4%, 21.6%21.6% and 49.5%49.5% better compared with other three tested optimization algorithms in the path cost on average. Correspondingly it is 9.6%9.6%, 12.8%12.8%, and 25.3%25.3% better in SLR, which is capable of generating higher quality paths efficiently for UAVs.
Type de document :
Article dans une revue
Aerospace Science and Technology, Elsevier, 2016, 58, pp. 92-102. 〈10.1016/j.ast.2016.08.017〉
Liste complète des métadonnées

https://hal-enac.archives-ouvertes.fr/hal-01364792
Contributeur : Laurence Porte <>
Soumis le : lundi 12 septembre 2016 - 20:14:58
Dernière modification le : mercredi 23 mai 2018 - 17:58:04

Identifiants

Citation

Liu Yang, Xuejun Zhang, Xiangmin Guan, Daniel Delahaye. Adaptive sensitivity decision based path planning algorithm for unmanned aerial vehicle with improved particle swarm optimization. Aerospace Science and Technology, Elsevier, 2016, 58, pp. 92-102. 〈10.1016/j.ast.2016.08.017〉. 〈hal-01364792〉

Partager

Métriques

Consultations de la notice

209