Investigation of Autonomous Multi-UAV Systems for Target Detection in Distributed Environment: Current Developments and Open Challenges

Uncrewed aerial vehicles (UAVs), also known as drones, are ubiquitous and their use cases extend today from governmental applications to civil applications such as the agricultural, medical, and transport sectors, etc. In accordance with the requirements in terms of demand, it is possible to carry out various missions involving several types of UAVs as well as various onboard sensors. According to the complexity of the mission, some configurations are required both in terms of hardware and software. This task becomes even more complex when the system is composed of autonomous UAVs that collaborate with each other without the assistance of an operator. Several factors must be considered, such as the complexity of the mission, the types of UAVs, the communication architecture, the routing protocol, the coordination of tasks, and many other factors related to the environment. Unfortunately, although there are many research works that address the use cases of multi-UAV systems, there is a gap in the literature regarding the difficulties involved with the implementation of these systems from scratch. This review article seeks to examine and understand the communication issues related to the implementation from scratch of autonomous multi-UAV systems for collaborative decisions. The manuscript will also provide
a formal definition of the ecosystem of a multi-UAV system, as well as a comparative study of UAV types and related works
that highlight the use cases of multi-UAV systems. In addition to the mathematical modeling of the collaborative target
detection problem in distributed environments, this article establishes a comparative study of communication architectures
and routing protocols in a UAV network. After reading this review paper, readers will benefit from the multicriteria decisionmaking
roadmaps to choose the right architectures and routing protocols adapted for specific missions. The open challenges
and future directions described in this manuscript can be used to understand the current limitations and how to overcome
them to effectively exploit autonomous swarms in future trends.