Abstract
This paper addresses a vision-based cooperative search for multiple mobile ground targets by a group of unmanned aerial vehicles (UAVs) with limited sensing and communication capabilities. The airborne camera on each UAV has a limited field of view and its target discriminability varies as a function of altitude. First, by dividing the whole surveillance region into cells, a probability map can be formed for each UAV indicating the probability of target existence within each cell. Then, we propose a distributed probability map updating model which includes the fusion of measurement information, information sharing among neighboring agents, information decay and transmission due to environmental changes such as the target movement. Furthermore, we formulate the target search problem as a multi-agent cooperative coverage control problem by optimizing the collective coverage area and the detection performance. The proposed map updating model and the cooperative control scheme are distributed, i.e., assuming that each agent only communicates with its neighbors within its communication range. Finally, the effectiveness of the proposed algorithms is illustrated by simulation.
Highlights
With the fast development of high resolution imaging devices and processing technologies, unmanned aerial vehicles (UAVs) with air-borne cameras are increasingly employed in civil and military applications such as environmental monitoring, battlefield surveillance and map building, where ground-target search is one of the major applications [1,2]
In [7], an online planning and control method is proposed for cooperative search by a group of UAVs, where each agent keeps an individual probability map for the whole region updated according to the Dempster-Shafer theory
The main contribution of this paper is that a distributed strategy of information fusion and cooperative control is proposed for searching multiple mobile targets using multi-agent networks based on probabilistic detections
Summary
With the fast development of high resolution imaging devices and processing technologies, unmanned aerial vehicles (UAVs) with air-borne cameras are increasingly employed in civil and military applications such as environmental monitoring, battlefield surveillance and map building, where ground-target search is one of the major applications [1,2]. In [7], an online planning and control method is proposed for cooperative search by a group of UAVs, where each agent keeps an individual probability map for the whole region updated according to the Dempster-Shafer theory. The coverage control method aims to maximize the joint detection probability of random events and the probability of target existence is updated by measurements based on the Bayesian rule. In our recent work [11], a distributed iterative map updating model is proposed to fuse the information from measurements and the maps of neighbors based on a logarithmic transformation of the Bayesian rule. The main contribution of this paper is that a distributed strategy of information fusion and cooperative control is proposed for searching multiple mobile targets using multi-agent networks based on probabilistic detections.
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