This paper proposes a communication protocol for Unmanned Aerial Vehicles (UAVs) using ZigBee technology. A review of the state-of-the-art of Flying Ad-hoc Networks (FANETs) and its main respective technologies is presented in detail. A comparison among Long Term Evaluation (LTE), WiFi, and ZigBee is performed, thus showing that ZigBee stands out as a good alternative to scenarios without proper infrastructure. A Raspberry Pi 3 Model B board associated with module XBEE PRO S3B 915 MHz is embedded in a UAV model DJI Phantom 3 Standard to carry out the tests. The obtained results show that the adopted protocol is capable of sending and receiving images between the UAV and the ground station. Tests are also performed using a flying aircraft, where it is demonstrated that the transmission is successfully executed for all cases and the communication protocol operates accurately. In addition, a brief analysis of the time interval required by the process is presented, as there are no significant differences among the existing scenarios.

This paper presents a communication network for a squadron of unmanned aerial vehicles (UAVs) to be used in the scanning rocket impact area for Barreira do Inferno Launch Center—CLBI (Rio Grande do Norte, Brazil), aiming at detecting intruder boats. The main features of communication networks associated with multi-UAV systems are presented. This system sends information through Wireless Sensor Networks (WSN). After comparing and analyzing area scanning strategies, it presents the specification of a data communication network architecture for a squadron of UAVs within a sensor network using XBee Pro 900HP S3B modules. A brief description is made about the initial information from the construction of the system. The embedded hardware and the design procedure of a dedicated communication antenna to the XBee modules are presented. In order to evaluate the performance of the proposed architecture in terms of robustness and reliability, a set of experimental tests in different communication scenarios is carried out. Network management software is employed to measure the throughput, packet loss and other performance indicators in the communication links between the different network nodes. Experimental results allow verifying the quality and performance of the network nodes, as well as the reliability of the communication links, assessing signal received quality, range and latency.

This work studies two models of artificial neural networks (ANN) in order to predict at the beginning of the school year the performance of students in SPAECE (Permanent System of Evaluation of Basic Education of Ceará). The factors considered as relevant to student achievements were the grade, period of study, school score and student score in elementary school, which were used as input variables to an ANN. A model of Perceptron and Multilayer Perceptron (MLP) were developed and trained using data of students of the year 2013 from schools of the 6th CREDE (State Development Regional Coordination). Test data evaluation shows that the ANN model is able to correctly predict 95% of performance of beginning students.

The planning of safe paths is an important issue for autonomous robot systems. The Probabilistic Foam method (PFM) is a planner that guarantees safe paths bounded by a sequence of structures called bubbles that provides safe regions. This method performs the planning by covering the free configuration space with bubbles, an approach analogous to a breadth-first search. To improve the propagation process and keep the safety, we present three algorithms based on Probabilistic Foam: Goal-biased Probabilistic Foam (GBPF), Radius-biased Probabilistic Foam (RBPF), and Heuristic-guided Probabilistic Foam (HPF); the last two are proposed in this work. The variant GBPF is fast, HPF finds short paths, and RBPF finds high-clearance paths. Some simulations were performed using four different maps to analyze the behavior and performance of the methods. Besides, the safety was analyzed considering the new propagation strategies.

This paper presents an improved variation of Probabilistic Foam Method (PFM) for robot path planning. In PFM, a structure named probabilistic foam, formed by bubbles propagate through the free space from initial configuration to goal as a breadth-first search, obtaining a collision-free path. Although the method is able to obtain a navigable path, it is computationally expensive. We propose a new foam propagation approach inspired on random tree growth from RRT. Results from simulation experiments using 2D and 3D map show benefits with the new method.

This paper presents an approach to simplify the path planning for an active orthosis through the application of the Probabilistic Foam Method. In this method, a structure called probabilistic foam was formed by a set of bubbles that propagates through the free space starting from an initial configuration to a goal configuration. Thus, it is possible to extract an obstacle-free path. Bubbles ensure safe region, meeting the safety requirements in maneuvering. In this paper, the method uses workspace metrics instead of configuration space metrics, enabling its application on robotics problems defined in high-dimensional spaces. Preliminary results show that the method was able to plan the safe movement to be performed by an active orthosis, transposing obstacles and performing safe movements in the proposed scenario.