Verification of Autonomous Drone Control Systemfor Gathering Information in Disaster Areas

Abstract

In recent years, the application field of drones has rapidly expanded, and they are attracting attention as a means of gathering information in disaster areas and as a means of disaster relief. Currently, drones are mainly controlled manually or by programmed control with pre-defined actions. Manual control limits the flight range to the transmitter's communication range and requires constant monitoring of the drone in flight, making it difficult to operate the drone when access to the disaster area is difficult or visibility is poor. In addition, since programmed control sets the drone's behavior in advance, stable control in response to changes in the flight environment due to a disaster may be difficult in emergency surveys during a disaster. The purpose of this study is to verify the applicability of model-based control, which is based on a mathematical model of the environment, and model-free control, which obtains optimal control rules from interaction with the environment, to drone’s controller for realizing autonomous drone flight in environments that are difficult to operate with existing control methods. Specifically, we compared the performance of model predictive control as model-based control and reinforcement learning as model-free control by computer experiments, and verified that the characteristics of both control system differ depending on the nature of the disturbance.