Abstract
Advances in the field of unmanned aerial vehicles (UAVs) have led to an exponential increase in their market, thanks to the development of innovative technological solutions aimed at a wide range of applications and services, such as emergencies and those related to fires. In addition, the expansion of this market has been accompanied by the birth and growth of the so-called UAV swarms. Currently, the expansion of these systems is due to their properties in terms of robustness, versatility, and efficiency. Along with these properties there is an aspect, which is still a field of study, such as autonomous and cooperative navigation of these swarms. In this paper we present an architecture that includes a set of complementary methods that allow the establishment of different control layers to enable the autonomous and cooperative navigation of a swarm of UAVs. Among the different layers, there are a global trajectory planner based on sampling, algorithms for obstacle detection and avoidance, and methods for autonomous decision making based on deep reinforcement learning. The paper shows satisfactory results for a line-of-sight based algorithm for global path planner trajectory smoothing in 2D and 3D. In addition, a novel method for autonomous navigation of UAVs based on deep reinforcement learning is shown, which has been tested in 2 different simulation environments with promising results about the use of these techniques to achieve autonomous navigation of UAVs.
Highlights
Recent advances, both in the field of Unmanned Aerial Vehicles (UAVs) and MultiRobot Systems (MRS), have led to the expansion in the use of this type of system in civilian and military tasks [1,2]
This work presents a software architecture in which a set of methods are combined to allow autonomous and coordinated navigation of a swarm of UAVs in environments of various kinds, such as fires declared in forest or urban environments
The main contribution of this work is to develop a software architecture consisting of multiple layers in which, through the implementation of a set of methods and algorithms we seek to generate solutions that allow the autonomous and coordinated navigation of a swarm of UAVs in order to be able to carry out a joint mission
Summary
Both in the field of Unmanned Aerial Vehicles (UAVs) and MultiRobot Systems (MRS), have led to the expansion in the use of this type of system in civilian and military tasks [1,2]. The set of properties described in the previous paragraph leads to different lines of research, such as this work, studying, analyzing and establishing the development and implementation of methods, encompassed within a software architecture, that allow these swarms to acquire, in addition, the possibility of navigating and undertaking work autonomously, without the need for human supervision deployed in the affected area, reducing the exposure of people to danger and reducing the possibility of personal harm In this way, achieving the objectives of this work allows to have a highly powerful and effective tool such as a swarm of UAVs capable of navigating autonomously and coordinated through a multi-layered architecture that gives each agent sufficient intelligence for decision-making to allow it to establish safe navigation paths through the environment
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