H-SwarmLoc: Efficient Scheduling for Localization of Heterogeneous MAV Swarm with Deep Reinforcement Learning

Abstract

Emergency rescue scenarios are considered to be high-risk scenarios. Using a micro air vehicle (MAV) swarm to explore the environment can provide valuable environmental information. However, due to the absence of localization infrastructure and the limited on-board capabilities, it’s challenging for the low-cost MAV swarm to maintain precise localization. In this paper, a collaborative localization system for the low-cost heterogeneous MAV swarm is proposed. This system takes full advantage of advanced MAV to effectively achieve accurate localization of the heterogeneous MAV swarm through collaboration. Subsequently, H-SwarmLoc, a reinforcement learning-based planning method is proposed to plan the advanced MAV with a non-myopic objective in real-time. The experimental results show that the localization performance of our method improves 40% on average compared with baselines.

Publication
In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
Fan DANG
Fan DANG
Research Assistant Professor

My research interests include industrial intelligence and edge computing.