Autonomous control of unmanned aerial vehicle for chemical detection using deep reinforcement learning

Abstract

The unmanned aerial vehicle (UAV) is a promising platform for remote chemical sensing to minimize human contact with toxic chemicals. Most previous studies in this field used predefined paths to search for areas based on sensor measurements at fixed points. However, operations on a predefined flight path are inefficient because in real-life scenarios, gas dispersion is stochastic and unpredictable. Thus, a model-free reinforcement-learning approach using a deep Q-network for autonomous UAV control is proposed. The UAV automatically selects its trajectory based on sensor measurements and GPS location data to map the contaminated area in real time. Path planning is simulated using Gaussian model-based gas dispersion data and its feasibility in outdoor experiments using gas simulants is proved. This study provides guidelines for developing a model that can perform autonomous chemical detection and source localization using UAV.