Centralized system improves multi-drone 3D mapping with safer navigation
Centralized Multi-UAV Exploration and 3D Reconstruction Using Single-UAV Planners
Robotics
Summary
Exploring unknown places with multiple drones can be tricky because the drones need to share maps, avoid crashing, and plan their routes. The authors created a system that lets several drones work together safely by sharing one 3D map and using planning methods designed for a single drone. Their system also stops drones from mistaking each other for obstacles and helps them avoid collisions. Tests show that sending drones from different starting points leads to faster and more complete exploration.
What this means in practice
- •For drone operators: Coordinate multiple drones in unknown areas faster while avoiding collisions using a shared 3D map and centralized planning system.
- •For disaster response teams: Improve the speed and coverage of area surveying by deploying multiple drones using a coordinated system that preserves efficient single-drone planning techniques.
Authors
João Félix Mendes, Meysam Basiri, Rodrigo Ventura
Abstract
Extending single Unmanned Aerial Vehicles (UAVs) exploration methods to multi-UAV teams can improve coverage speed and robustness, but introduces challenges such as consistent mapping, safe navigation, and deployment strategy. In this work, we present a centralized multi-UAV exploration framework that enables the use of existing single-UAV sampling-based planners in a multi-UAV setting. The proposed architecture allows multiple UAVs to collaboratively explore unknown environments using a shared global Truncated Signed Distance Field (TSDF) map and centralized planning. Building on the voxblox library, we adapt its mapping pipeline to support real-time fusion of depth measurements from multiple UAVs into a common TSDF representation. In addition, inter-UAV collision avoidance and robot self-filtering mechanisms are integrated into the system to ensure safe navigation and prevent reconstruction of other UAVs as static obstacles. The framework is evaluated in simulation using four sampling-based exploration planners - RH-NBVP, KRH-NBVP, AEP, and KAEP - whose core sampling logic is preserved, with only system-level adaptations for multi-UAV operation. Experiments are conducted across multiple environments and under two deployment configurations: Joint Start (JS), where UAVs are initialized in close proximity, and Separated Start (SS), where UAVs are initialized in distinct locations. Results show that SS deployments consistently achieve faster exploration and improved coverage across all planners, highlighting the importance of the deployment strategy in multi-UAV exploration performance.