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This paper addresses the problem of target detection and localisation in a limited area using multiple coordinated agents. The swarm of Unmanned Aerial Vehicles (UAVs) determines the position of the dispersion of stack effluents to a gas plume in a certain production area as fast as possible, that makes the problem challenging to model and solve, because of the time variability of the target. Three different exploration algorithms are designed and compared. Besides the exploration strategies, the paper reports a solution for quick convergence towards the actual stack position once detected by one member of the team. Both the navigation and localisation algorithms are fully distributed and based on the consensus theory. Simulations on realistic case studies are reported.
This study proposes an efficient data collection strategy exploiting a team of Unmanned Aerial Vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture,
Truss robots, or robots that consist of extensible links connected at universal joints, are often designed with modular physical components but require centralized control techniques. This paper presents a distributed control technique for truss robo
In this paper, we study the resilient vector consensus problem in networks with adversarial agents and improve resilience guarantees of existing algorithms. A common approach to achieving resilient vector consensus is that every non-adversarial (or n
In the past decade, unmanned aerial vehicles (UAVs) have been widely used in various civilian applications, most of which only require a single UAV. In the near future, it is expected that more and more applications will be enabled by the cooperation
UAVs have found an important application in archaeological mapping. Majority of the existing methods employ an offline method to process the data collected from an archaeological site. They are time-consuming and computationally expensive. In this pa