The modern battlefield scenario presents a number of challenges that highlights the importance of data gathering and information flow as crucial to the success of a military operation. In this context, network-centric warfare systems play an important role to bridge the gap between data and information sources and consumers. These systems are generally composed of different types of networks both deployed on the field and on back-end facilities. A paramount problem faced when conceiving the interconnection between the networked devices is how to provide robust and dependable connections to the devices on the field. This work tackles this issue proposing the use of the self-organizing paradigm to design efficient UAV relay networks to support military operations. As a proof of concept, realistic simulation experiments are performed providing clear indications of the claimed benefits of the self-organizing paradigm applied to these military systems.

Efficient exploration of unknown environments is a fundamental precondition for modern autonomous mobile robot applications. Aiming to design robust and effective robotic exploration strategies, suitable to complex real-world scenarios, the academic community has increasingly investigated the integration of robotics with reinforcement learning (RL) techniques. This survey provides a comprehensive review of recent research works that use RL to design unknown environment exploration strategies for single and multirobots. The primary purpose of this study is to facilitate future research by compiling and analyzing the current state of works that link these two knowledge domains. This survey summarizes: what are the employed RL algorithms and how they compose the so far proposed mobile robot exploration strategies; how robotic exploration solutions are addressing typical RL problems like the exploration-exploitation dilemma, the curse of dimensionality, reward shaping, and slow learning convergence; and what are the performed experiments and software tools used for learning and testing. Achieved progress is described, and a discussion about remaining limitations and future perspectives is presented.

An important problem in Wireless Sensor Networks (WSN) is the occurrence of failures that lead to the disconnection of parts of the network, compromising the final results achieved by the WSN operation. A way to overcome such problem is to provide a reliable connection to support the connectivity via other types of nodes that communicate with the sensor nodes. This paper proposes the usage of a network composed by Unmanned Aerial Vehicles (UAVs) as a relay network to guarantee the delivery of data produced by WSN nodes on the ground to the users. Results from simulations of the proposed technique are provided and discussed.

This article presents the development of a multi-unmanned aerial vehicle (UAV) based crowd monitoring system, demonstrating a system that uses UAVs to periodically monitor a group of moving walking individuals. Using auction paradigms to distribute targets among UAVs and genetic algorithms to calculate the best order to visit the targets, the system has shown capabilities to efficiently perform the surveillance, visiting all the targets during a surveillance period and minimizing the time between the visits made to each target. Moreover, the system showed robustness keeping the good performance under a variety of situations.