Efficient 3D Exploration with Distributed Multi-UAV Teams: Integrating Frontier-Based and Next-Best-View Planning
André Ribeiro, Meysam Basiri
Abstract
Autonomous exploration of unknown environments poses many challenges in robotics, particularly when dealing with vast and complex landscapes. This paper presents a novel framework tailored for distributed multi-robot systems, harnessing the 3D mobility capabilities of Unmanned Aerial Vehicles (UAVs) equipped with advanced LiDAR sensors for the rapid and effective exploration of uncharted territories. The proposed approach uniquely integrates the robustness of frontier-based exploration with the precision of Next-Best-View (NBV) planning, supplemented by a distance-based assignment cooperative strategy, offering a comprehensive and adaptive strategy for these systems. Through extensive experiments conducted across distinct environments using up to three UAVs, the efficacy of the exploration planner and cooperative strategy is rigorously validated. Benchmarking against existing methods further underscores the superiority of the proposed approach. The results demonstrate successful navigation through complex 3D landscapes, showcasing the framework’s capability in both single- and multi-UAV scenarios. While the benefits of employing multiple UAVs are evident, exhibiting reductions in exploration time and individual travel distance, this study also reveals findings regarding the optimal number of UAVs, particularly in smaller and wider environments.