Strategies for Cooperative UAVs Using Model Predictive Control

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/738 ◽

2020 ◽

Author(s):

Amith Manoharan

Keyword(s):

Decision Making ◽

Model Predictive Control ◽

Unmanned Aerial Vehicles ◽

Predictive Control ◽

Search And Rescue ◽

Nonlinear Model Predictive Control ◽

Optimal Decision ◽

Operation Performance ◽

Aerial Vehicles ◽

Optimal Decision Making

Unmanned aerial vehicles (UAVs) have reached significant maturity over several years for safe civilian operations like mapping, search and rescue. The operation performance can be significantly improved by deploying multiple cooperating UAVs and optimal decision making. In this work, we present the use of nonlinear model predictive control (NMPC) for two different applications involving cooperative UAVs.

Download Full-text

Optimal Decision Making for Automated Vehicles Using Homotopy Generation and Nonlinear Model Predictive Control

10.1109/iv48863.2021.9575302 ◽

2021 ◽

Author(s):

Vivian Z. Patterson ◽

Francis E. Lewis ◽

J. Christian Gerdes

Keyword(s):

Decision Making ◽

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Nonlinear Model Predictive Control ◽

Optimal Decision ◽

Automated Vehicles ◽

Optimal Decision Making

Download Full-text

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

Sensors ◽

10.3390/s19194067 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4067 ◽

Cited By ~ 4

Author(s):

Fabio A. A. Andrade ◽

Anthony Hovenburg ◽

Luciano Netto de de Lima ◽

Christopher Dahlin Rodin ◽

Tor Arne Johansen ◽

...

Keyword(s):

Model Predictive Control ◽

Unmanned Aerial Vehicles ◽

Real Time ◽

Predictive Control ◽

Kinematic Model ◽

Search And Rescue ◽

International Standards ◽

Area Of Interest ◽

Aerial Vehicles ◽

Human Operators

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.

Download Full-text