Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control.

Fabio A A Andrade,Diego Barreto Haddad,Luciano Netto De De Lima,Rune Storvold,Christopher Dahlin Rodin,Anthony Hovenburg,Carlos A M Correia,Tor Arne Johansen

doi:10.3390/s19194067

Abstract

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.

Highlights

Search and Rescue (SAR) is one of the fields where the employment of Unmanned Aerial Vehicles (UAVs) brings many advantages over manned missions, such as its reduced costs, lower use of human resources, and mental and perception limitations of human operators
The aircraft sensor data are simulated by JSBSim [29], an open source Flight Dynamics Model
It is able to compute the UAV dynamics according to the actuator controls given by the Ardupilot code

Summary

Introduction

Search and Rescue (SAR) is one of the fields where the employment of UAVs brings many advantages over manned missions, such as its reduced costs, lower use of human resources, and mental and perception limitations of human operators. Reference [1] was one of the first works to perform experimental tests of a complete autonomous single UAV SAR solution. A probability density function (PDF) that expressed the likelihood of the target’s location was one of the main inputs of the system. Video data from the UAV was transmitted to the ground station that processed it in real time using computer vision techniques to detect the presence of the target and update the PDF. Paths were generated by the ground station to maximize the probability of finding the targeted object. The experimental flights showed satisfactory results in searching and detecting the target.

Methods

Results

Conclusion