Unmanned-Aircraft-System-Assisted Early Wildfire Detection with Air Quality Sensors †

Numerous Hectares of Land Are Destroyed by Wildfires Every Year, Causing Harm to the Environment, the Economy, and the Ecology. More Than Fifty Million Acres Have Burned in Several States as a Result of Recent Forest Fires in the Western United States and Australia. According to Scientific Predictions, as the Climate Warms and Dries, Wildfires Will Become More Intense and Frequent, as Well as More Dangerous. These Unavoidable Catastrophes Emphasize How Important Early Wildfire Detection and Prevention Are. the Energy Management System Described in This Paper Uses an Unmanned Aircraft System (UAS) with Air Quality Sensors (AQSs) to Monitor Spot Fires Before They Spread. the Goal Was to Develop an Efficient Autonomous Patrolling System that Detects Early Wildfires While Maximizing the Battery Life of the UAS to Cover Broad Areas. the UAS Will Send Real-Time Data (Sensor Readings, Thermal Imaging, Etc.) to a Nearby Base Station (BS) When a Wildfire is Discovered. an Optimization Model Was Developed to Minimize the Total Amount of Energy Used by the UAS While Maintaining the Required Levels of Data Quality. Finally, the Simulations Showed the Performance of the Proposed Solution under Different Stability Conditions and for Different Minimum Data Rate Types

Early wildfire detection by air quality sensors on unmanned aerial vehicles: Optimization and feasibility

“Millions of acres of forests are destroyed by wildfires every year, causing ecological, environmental, and economical losses. The recent wildfires in Australia and the Western U.S. smothered multiple states with more than fifty million acres charred by the blazes. The warmer and drier climate makes scientists expect increases in the severity and frequency of wildfires and the associated risks in the future. These inescapable crises highlight the urgent need for early detection and prevention of wildfires. This work proposed an energy management framework that integrated unmanned aerial vehicle (UAV) with air quality sensors for early wildfire detection and forest monitoring. An autonomous patrol solution that effectively detects wildfire events, while preserving the UAV battery for a larger area of coverage was developed. The UAV can send real-time data (e.g., sensor readings, thermal pictures, videos, etc) to nearby communications base stations (BSs) when a wildfire is detected. An optimization problem that minimized the total UAV’s consumed energy and satisfied a certain quality-of-service (QoS) data rate were formulated and solved. More specifically, this study optimized the flight track of a UAV and the transmit power between the UAV and BSs. Finally, selected simulation results that illustrate the advantages of the proposed model were proposed”--Abstract, page iii

Choosing the Best Embedded Processing Platform for On-Board UAV Image Processing

Nowadays, complex image processing algorithms are a necessity to make UAVs more autonomous. Currently, the processing of images of the on-board camera is often performed on a ground station, thus severely limiting the operating range. On-board processing has numerous advantages, however determining a good trade-off between speed, power consumption and weight of a specific hardware platform for on-board processing is hard. Many hardware platforms exist, and finding the most suited one for a specific vision algorithm is difficult. We present a framework that automatically determines the most-suited hardware platform given an arbitrary complex vision algorithm. Our framework estimates the speed, power consumption and flight time of this algorithm for multiple hardware platforms on a specific UAV. We demonstrate this methodology on two real-life cases and give an overview of the present top performing CPU-based platforms for on-board UAV image processing.Hulens D., Verbeke J., Goedemé T., ''Choosing the best embedded processing platform for on-board UAV image processing'', Computer vision, imaging and computer graphics theory and applications, 10th international joint conference, VISIGRAPP 2015, revised selected papers - communications in computer and information science series, vol. 598, pp. 455-472, 2016, Springer (10th international conference on computer vision theory and applications - VISAPP 2015, March 11-14, 2015, Berlin, Germany).status: publishe

Diseño de módulo para la navegación autónoma de un vehículo aéreo no tripulado en espacios interiores

En la actualidad, la necesidad por automatizar procesos en diferentes áreas ha conducido al uso de vehículos aéreos no tripulados para la realización de tareas de forma autónoma y ágil. En este trabajo se desarrolla un método para la navegación autónoma de un vehículo aéreo no tripulado en espacios interiores privados de servicio de geolocalización. Para dicho método se diseña un módulo electrónico capaz de procesar imágenes para detectar códigos QR con lo cual tener información para realizar un plan de vuelo autónomo. La implementación se logra gracias a una computadora embebida (Raspberry Pi 3) que es capaz de realizar las tareas de comunicación y procesamiento en tiempo real para la navegación. También se hace uso del controlador de vuelo Pixhawk, un LIDAR para el control de la altitud de vuelo. En cuanto a software se hace uso de las librerías OpenCV y Dronekit para el lenguaje de programación Python. En los resultados se muestran el desempeño del módulo y las configuraciones necesarias para el óptimo funcionamiento del método de navegación.Tesi