Mapping and Localization in large environments is becoming increasingly important for autonomous UAV swarms. UAV swarms solving problems in disaster response, infrastructure inspection, and agriculture rely on fresh and accurate maps to make navigation decisions. SLAM methods are capable of providing highly accurate maps through visual information, but are computationally heavy and ill-suited for UAV onboard computational profiles. For this reason, UAV swarms often dedicate one or more drones to frequent mapping, while other drones use map information for planning and trajectory generation. UAV swarms also centralize heavy-weight workloads like AI inference and SLAM map combination at the edge to extend UAV battery lives at the cost of network provisioning. Both map sharing and offloading necessitate high network bandwidth, but few SLAM or planning approaches account for this. We present NetSLAM, a network assisted SLAM and planning system that builds environmental maps and UAV trajectories that meet quality of service (QoS) requirements. NetSLAM embeds network information into SLAM maps so planning can compensate for changes in network connectivity across the environment. We also present Net*, a path planning algorithm which utilizes NetSLAM maps to build trajectories that maintain QoS requirements to maximize performance. Through real-world experiments and simulation, we show that NetSLAM maps network environments with limited additional overhead compared to existing SLAM approaches. NetSLAM improves swarm QoS by 2.35x while increasing path length by less than 14.7 % compared to naive pathfinding.
Netslam: Network-Aware Path Planning for Edge-Assisted UAV Swarms
2025-05-14
829371 byte
Conference paper
Electronic Resource
English
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