.. .. Contexte,

.. .. Contexte,

. .. De-chemin, , p.93

. .. Principe,

M. .. De-calcul-de-chemin, , p.98

, 98 6.3.2 Calcul d'un chemin à partir des champs de potentiels discrets

, Avantages par rapport à un APF classique, p.108

. .. , 109 6.4.2 Distance maximale à parcourir en une itération

. .. Résultats, 112 6.5.1 Distance parcourue en fonction de la taille de maille 113 6.5.2 Nombre d'itérations nécessaires en fonction de la taille de maille

. .. Limitations-du-modèle, 120 6.5.4 Conclusion sur la performance du modèle, p.122

.. .. Résumé,

.. .. Principe,

, 3.5 Exemple de calcul de chemin par des drones collaboratifs

, Cas particulier : vol en formation compacte, p.129

. .. Simulations,

.. .. Résumé,

, Dans ce chapitre nous présentons les ajouts à effectuer à la méthode de calcul de chemin présentée dans le chapitre précédent, permettant à plusieurs drones collaboratifs, évoluant dans un même environnement complexe, de l'utiliser. Il est important de noter que la méthode reste fondamentalement identique et que son utilisation dans un système multiplateforme représente une contrainte qui lui est ajoutée

, Liste des sigles 3D Dull, Dirty and Dangerous

, APF Artificial Potential Field, vol.56, pp.51-54

C. Command and . Control,

, CARUS Cooperative Autonomous Reconfigurable UAV Swarm, p.15

, CAS Close Air Support, vol.16

. Ce-commission-européenne,

, DoD Department of Defense

, DWA Dynamic Window Approach, vol.45, p.57

, FANET Flying Ad hoc Network, vol.11, p.36

, GNRON Goal Non Reachable with Obstacle Nearby, vol.53, p.108

, Reconnaissance and Surveillance, ISR Intelligence, p.16

, OACI Organisation de l'Aviation Civile Internationale, vol.7

, PoI Point of Interest, vol.15

, PSO Particle Swarm Optimization, vol.51, p.57

, RPAS Remotely Piloted Aircraft System, vol.8

, SEAD Suppression of Enemy Aerial Defenses, p.16

, UAS Unmanned Aerial System, pp.6-8

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