Abstract
This paper proposes an optimization strategy for searching moving targets’ locations using cooperative unmanned aerial vehicles (UAVs) in an unknown environment. Such a strategy aims at reducing the overall search time and impact of uncertainties caused by the motion of targets, as well as improving the detection efficiency of UAVs. Specifically, we report, based on the UAV’s scan of a location and taking into account (i) the detection and communication coverage limitations, and (ii) either a false alarm or inaccurate detection of the target, either the existence or the absence of the target. Moreover, leveraging a cooperative and competitive particle swarm optimization (PSO) algorithm, a decentralized target search model, relying on a real-time dynamic construction of cooperative UAV local sub-swarms (LoPSO), is proposed. Each sub-swarm strives to validate quickly the target location, updated based on the Bayesian theory. In such a strategy, each UAV operates in two flight modes, namely, either in swarm mode or in Greedy mode, and takes into consideration the received data from other UAVs to improve the overall environmental information. The simulation results revealed that the LoPSO outperforms other well-known searching methods of target methods for target search in unknown environments in terms of both performance and computational complexity.
Highlights
The cooperative control of significant small unmanned aerial vehicles (UAVs) groups is less expensive and of great interest in military and civilian domains, such as space exploration, forest fire watch, patrols, and search and rescue
CONTRIBUTION Motivated by the foregoing, we present an efficient approach based on a dynamic cluster structure that combines the characteristics of a distributed and centralized approach, with the goal of moving the cooperative and controlled autonomously UAVs to undeveloped areas, as well as finding and tracking moving targets in restricted areas
We assume that the target can be detected by the UAV as long as it appears within its detection range
Summary
The cooperative control of significant small unmanned aerial vehicles (UAVs) groups is less expensive and of great interest in military and civilian domains, such as space exploration, forest fire watch, patrols, and search and rescue. Relying on the PSO algorithm, we propose an efficient hybrid approach based on a local sub-swarm structure (LoPSO) [18] to exchange and update efficiently the search information to reduce the overall search time for finding all missed targets. Such information is updated with the aid of target existence probabilities provided by the UAVs, as well as a mapping of the uncertainty. To gain more insights into the performance and the computational complexity, we demonstrate that our proposed algorithm outperforms the robust well-known similar algorithms
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