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 GNSSim functions are linked together.

Autonomous aerial vehicle, drone formation control, drone swarm, goal location, information sharing, localization, location, path planning, quadcopter
2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
School of Computer Science

Barbeau, M, Garcia-Alfaro, J. (Joaquin), & Kranakis, E. (2019). Geocaching-inspired Resilient Path Planning for Drone Swarms. In INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019 (pp. 620–625). doi:10.1109/INFCOMW.2019.8845318