TY - GEN
T1 - Geocaching-inspired Resilient Path Planning for Drone Swarms
AU - Barbeau, Michel
AU - Garcia-Alfaro, Joaquin
AU - Kranakis, Evangelos
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - Autonomous aerial vehicle
KW - drone formation control
KW - drone swarm
KW - goal location
KW - information sharing
KW - localization
KW - location
KW - path planning
KW - quadcopter
U2 - 10.1109/INFCOMW.2019.8845318
DO - 10.1109/INFCOMW.2019.8845318
M3 - Conference contribution
AN - SCOPUS:85073260355
T3 - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
SP - 620
EP - 625
BT - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
Y2 - 29 April 2019 through 2 May 2019
ER -