Geocaching-inspired resilient path planning for drone swarms

Michel Barbeau (1) , Joaquin Garcia-Alfaro (2, 3, 4, 5) , Evangelos Kranakis (1)

1 School of Computer Science [Ottawa]
2 R3S-SAMOVAR - Réseaux, Systèmes, Services, Sécurité
3 TSP - RST - Département Réseaux et Services de Télécommunications
4 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
5 CNRS - Centre National de la Recherche Scientifique

A path planning algorithm for drone swarms is presented. From the outset, none of the drones knows the path and final destination. Together, they collectively determine and unravel step-by-step the waypoints and final destination, resolving a localization problem at each step. It is a shared-information path planning algorithm. The algorithm is fault-tolerant and resilient to drones falling victim of attacks to their positioning system. It is shown that correctly functioning drones navigate the path provided that the number of faulty drones is less than $\frac{n-d}{2}$, where $n$ is the total number of drones and $d$, equal to two or three, is the dimension of the space navigated by the drones. We validate the algorithm with appropriate simulations, implemented over OMNeT++ and GNSSim, which allow building network simulations including GPS attacks (e.g., jamming and spoofing attacks). The OMNeT++ models and the GNSSim functions are linked together, to validate our work.

Domaines: Cryptographie et sécurité [cs.CR]

Michel Barbeau, Joaquin Garcia-Alfaro, Evangelos Kranakis. Geocaching-inspired resilient path planning for drone swarms. MISARN 2019: Mission-Oriented Wireless Sensor, UAV and Robot Networking, Apr 2019, Paris, France. pp.620 - 625, ⟨10.1109/INFCOMW.2019.8845318⟩. ⟨hal-02127604⟩