DPPM: Decentralized Exploration Planning for Multi-UAV Systems Using Lightweight Information Structure
Yulin Hui, Xuewei Zhang, Hongming Shen, Hanchen Lu, Bailing Tian
Abstract
Autonomous exploration of unknown environments with multiple Unmanned Aerial Vehicles (UAVs) is a challenging problem. In this article, we present DPPM, a Decentralized exPloration Planning framework for Multi-UAV systems. To conserve communication bandwidth, a lightweight information structure with spatial structure and exploration information is developed, which can be saved as a sparse topological graph. Supported by the information structure, a hierarchical planner is performed. The local planner filters frontiers to avoid overlap exploration and refines viewpoints sampling area to separate UAVs to different areas, while the global planner re-positions UAVs to ensure complete coverage of the environment. Finally, the exploration path is optimized using model predictive path integral (MPPI) control framework to generate continuous-time trajectory. Comparative experiments are presented to validate the performance of the proposed framework.