Abstract
Unmanned Aerial Vehicle (UAV) has been widely used in a variety of application, and the target search is one of the hot issues in the UAV research fields. Compared with the single UAV, the multi-UAV system can be competent for more complex tasks, with higher execution efficiency and stronger robustness. However, there exist some new challenges in the multi-UAV cooperative search, such as collaborative control and search area covering problems. To complete these tasks efficiently, the cooperative search problem is modeled as a potential game, and a modified binary log linear learning (BLLL) algorithm is proposed in this paper, to solve the covering problem using multiple UAVs. Furthermore, to improve the cooperative control performance based on potential game theory, a novel action selection strategy for UAVs is proposed. This strategy can avoid a UAV wandering around at the zero utility area by exchanging the information with neighbors. Finally, various simulations are carried out. The experimental results show that the proposed method can effectively complete cooperative search tasks and has better performance than the original BLLL algorithm.
Highlights
Target search is a research hot spot in the field of robotics [1]–[6]
Due to the limitations of single Unmanned Aerial Vehicle (UAV) application in some large scale environments, the systems that contain multiple UAVs are widely used in many fields including military, public and civil applications, such as surveying and mapping, surveillance and search, etc [10]–[12]
Based on those descriptions above, we can know that, if the individual utility function of UAV in the multi-UAV coverage search problem is defined as Equation (11), the multi-UAV cooperative coverage search problem can be modeled as an exact potential game (EPG) problem according to the definition of EPG, and the potential function of this potential game could be described as Equation (8)
Summary
Target search is a research hot spot in the field of robotics [1]–[6]. Recently, there have been an increase in the research and application of UAVs [7]–[9]. The computational complexity of the model predictive approach will increase with the number of individual UAVs used in the search task Li and Duan [23] modeled the multi-UAV collaborative searching and monitoring problem as the potential game problem, and used the binary log linear learning algorithm to solve the potential game model. To complete the cooperative search task by multi-UAVs efficiently, an improved potential game theory based method is presented, and a modified binary log linear learning (BLLL) algorithm is proposed to solve this exact potential game (EPG) model in this paper.
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