Coordinated UAV-UGV trajectory planning based on load balancing in IoT data collection
To improve the efficiency of large-scale Internet of things(IoT)data collection,a coordinated trajectory plan-ning algorithm for multiple aerial and ground vehicles based on load balancing region partitioning was proposed,where unmanned aerial vehicles(UAVs)acting as aerial base stations were dispatched to gather data from IoT devices and un-manned ground vehicles(UGVs)acting as mobile battery swap stations were used to compensate for the shortage of UAV's energy.Aiming at shortening the mission completion time,the optimization task was to minimize the longest mis-sion time among a fleet of UAV-UGVs,which was formulated as a variant of min-max multi-depot vehicle routing prob-lem and solved from the load-balancing perspective.Specifically,the IoT devices were assigned to the UAV-UGVs'ser-vice zones by a load-balancing region partition algorithm,based on which the trajectory planning problem of multiple UAV and UGV was reduced to several independent route planning problems for each UAV-UGV pair.Then,a coopera-tive trajectory planning strategy was developed to optimize the route in each service zone.Numerical results validate that the proposed algorithm outperforms the compared algorithms in terms of mission completion time and balancing degree.
万方数据
维普
