Coordinated motion of UGVs and a UAV

Coordination of autonomous mobile robots has received significant attention during the last two decades. Coordinated motion of heterogenous robot groups are more appealing due to the fact that unique advantages of different robots might be combined to increase the overall efficiency of the system. In this paper, a heterogeneous robot group composed of multiple Unmanned Ground Vehicles (UGVs) and an Unmanned Aerial Vehicle (UAV) collaborate in order to accomplish a predefined goal. UGVs follow a virtual leader which is defined as the projection of UAV’s position onto the horizontal plane. The UAV broadcasts its position at certain frequency. The position of the virtual leader and distances from the two closest neighbors are used to create linear and angular velocity references for each UGV. Several coordinated tasks have been presented and the results are verified by simulations where certain amount of communication delay between the vehicles is also considered. Results are quite promising

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

In this paper, we outline the functionalities of a system that integrates and controls a fleet of Unmanned Aircraft Vehicles (UAVs). UAVs have a set of payload sensors employed for territorial surveillance, whose outputs are stored in the system and analysed by the data exploitation functions at different levels. In particular, we detail the second level data exploitation function whose aim is to improve the sensors data interpretation in the post-mission activities. It is concerned with the mosaicking of the aerial images and the cartography enrichment by human sensors—the social media users. We also describe the software architecture for the development of a mash-up (the integration of information and functionalities coming from the Web) and the possibility of using human sensors in the monitoring of the territory, a field in which, traditionally, the involved sensors were only the hardware ones.JRC.H.6-Digital Earth and Reference Dat

Desarrollo de un sistema de transmisión de datos a larga distancia para WSN

Este trabajo consiste en el desarrollo e implementación de un sistema de adquisición y procesamiento de datos para una red WSN, la cual comunica los datos obtenidos de la red inalámbrica de sensores a un ordenador que recolecta los datos a través de la tecnología GSM/GPRS. Para ello se ha diseñado tanto el hardware y software del sistema receptor conectado al PC como el que permite al nodo coordinador de la WSN enviar los datos al sistema central de procesamiento. El dispositivo emisor que transmite los datos de la red debe adaptarse de manera adecuada al nodo sensor desarrollado en el GDE. El sistema receptor está formado por el hardware necesario para comunicar el módulo GSM/GPRS con un ordenador personal y por la fuente de alimentación del mismo. Además, se ha desarrollado un entorno gráfico en Matlab que realiza la interpretación de los datos y los muestra en pantalla en tiempo real

The Effects of Sensor Performance as Modeled by Signal Detection Theory on the Performance of Reinforcement Learning in a Target Acquisition Task

Unmanned Aerial Systems (UASs) today are fulfilling more roles than ever before. There is a general push to have these systems feature more advanced autonomous capabilities in the near future. To achieve autonomous behavior requires some unique approaches to control and decision making. More advanced versions of these approaches are able to adapt their own behavior and examine their past experiences to increase their future mission performance. To achieve adaptive behavior and decision making capabilities this study used Reinforcement Learning algorithms. In this research the effects of sensor performance, as modeled through Signal Detection Theory (SDT), on the ability of RL algorithms to accomplish a target localization task are examined. Three levels of sensor sensitivity are simulated and compared to the results of the same system using a perfect sensor. To accomplish the target localization task, a hierarchical architecture used two distinct agents. A simulated human operator is assumed to be a perfect decision maker, and is used in the system feedback. An evaluation of the system is performed using multiple metrics, including episodic reward curves and the time taken to locate all targets. Statistical analyses are employed to detect significant differences in the comparison of steady-state behavior of different systems

UAV based group coordination of UGVs

Coordination of autonomous mobile robots has received significant attention during the last two decades with the emergence of small, lightweight and low power embedded systems. Coordinated motion of heterogenous robots is important due to the fact that unique advantages of di erent robots might be combined to increase the overall task efficiency of the system. In this thesis, a new coordination framework is developed for a heterogeneous robot system, composed of multiple Unmanned Ground Vehicles (UGVs) and an Unmanned Aerial Vehicle (UAV), that operates in an environment where individual robots work collaboratively in order to accomplish a predefined goal. UAV, a quadrotor, detects the target in the environment and provides a feasible trajectory from an initial configuration to a final target location. UGVs, a group of nonholonomic wheeled mobile robots, follow a virtual leader which is created as the projection of UAV's 3D position onto the horizontal plane. The UAV broadcasts its position at certain frequency to all UGVs. Two different coordination models are developed. In the dynamic coordination model, reference trajectories for each robot is generated from the motion of nodal masses located at each UGV and connected by virtual springs and dampers. Springs have adaptable parameters that allow the desired formation to be achieved In the kinematic coordination model, the position of the virtual leader and distances from the two closest neighbors are directly utilized to create linear and angular velocity references for each UGV. Several coordinated tasks are presented and the results are verified by simulations where different number of UGVs are employed and certain amount of communication delays between the vehicles are also considered. Simulation results are quite promising and form a basis for future experimental work on the topic