Centralized Multi-UAV Exploration and 3D Reconstruction Using Single-UAV Planners

2026-09-30

AI Summary

This study addresses the challenges of map consistency, safe navigation, and deployment strategies in multi-UAV cooperative exploration. It proposes a centralized framework that seamlessly extends a sampling-based single-robot planner to multi-robot systems for the first time, preserving its core logic with minimal adaptation. To ensure safe mapping, the approach employs Voxblox-based real-time TSDF fusion, collision avoidance, and robot self-filtering techniques. A key contribution is the demonstration that a separated-start deployment strategy significantly outperforms a joint-start configuration, effectively improving exploration speed and coverage. Overall, this work achieves efficient multi-robot cooperative exploration and 3D reconstruction through low-intrusion code reuse.

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.

Problem

• Multi-UAV Exploration
• 3D Reconstruction
• Centralized Planning
• Collision Avoidance
• Deployment Strategy

Innovation

• Multi-UAV Exploration
• Centralized Planning
• TSDF Map Fusion
• Single-UAV Planners
• Collision Avoidance