Networks of UAVs of Low-Complexity for Time-Critical Localization

Abstract

Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization performance (e.g., tracking the state of a malicious user). Since there is the need to preserve low UAV complexity while tackling the challenging goals of missions in effective ways, one key aspect is the UAV intelligence (UAV-I). The UAV's intelligence includes the UAV's capability to process information and to make decisions, e.g., to decide where to sense and whether to delegate some tasks to other network entities. In this paper, we provide an overview of possible solutions for the design of UAVs of low complexity, showing some of the needs of the UAVs for running efficient localization operations, performed either as a team or individually. Further, we focus on different network configurations, which possibly include assistance with edge computing. We also discuss open problems and future perspectives for these settings.

Autonomous Unmanned Aerial Vehicle navigation using Reinforcement Learning: A systematic review

“…• First stage: The authors identified all studies that strictly applied RL to UAV Navigation and acknowledged that model-free RL is typically utilized to tackle UAV navigation challenges, except for a single article [5] that employs model-based RL. Therefore, the authors choose to concentrate on model-free RL and exclude research irrelevant to UAV Navigation, such as UAV networks and traditional optimization tools and techniques [6][7][8][9][10]. • Second stage: The authors listed all RL algorithms based on authors' knowledge of the most prominent algorithms, the references of recognized algorithms, then identified the corresponding paper of each algorithm.…”

Reinforcement Learning for Joint Detection and Mapping Using Dynamic UAV Networks

“…A step forward has been introducing flying dynamic sensor networks where sensors are integrated onboard unmanned aerial vehicles (UAVs) [4], [5]. A recent review on the use of UAVs for remote sensing, spanning from precision agriculture (e.g., forest monitoring), urban environment and management (e.g., air traffic control) to disaster hazards and rescue (e.g., post-disaster assessment), can be found in [6] and the references therein.…”

“…In this domain, an important line of research is the optimization of the UAV trajectory [12], [13]. In fact, unlike terrestrial sensors, all tasks and navigation must be optimized so as not to waste time flying over areas of little interest from the mission perspective because of the frequent need to recharge batteries [4]. Moreover, UAVs must complete their mission within a finite time horizon, especially if they operate for time-critical applications.…”

Multi-Agent Q-Learning in UAV Networks for Target Detection and Indoor Mapping

“…Commonly, a major challenge of UAV networks is the limited time the UAVs have to complete tasks because of the need that their batteries be frequently recharged. Unlike terrestrial sensors, all tasks and navigation must be optimized as not to waste time flying over areas of little interest from the mission perspective [4]. A possible solution to shorten the mission time is to leverage over UAV cooperation that can also be orchestrated by the higher layers of the communication infrastructure, e.g., the edges.…”

“…In this paper, we aim to study a navigation approach for multi-target detection (primary task) and for improving the ambient awareness by reproducing an occupancy map of the environment [4]. Differently from classical offline optimization, the UAVs discover the environment in real-time and in absence of any pre-defined fixed waypoint.…”