# Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement
## 1 Introduction  
In recent years, unmanned aerial vehicle (UAV)-related research has attracted much attention. UAVs have many potential applications, such as area surveillance, and search and rescue [1, 2, 3, 4, 5]. Collaborative multi-UAV systems can significantly improve the work efficiency, especially when they perform challenging and complex tasks, such as target search in dangerous environments, i.e., a seismic region, or monitoring a large area-of-interest [6, 7, 8, 9]. Area coverage is a typical fundamental application problem for multi-UAV systems, and the coverage problem is optimizing notions of quality-of-service (QoS) provided by an adaptive sensor network in a dynamic environment [10, 11]. In real applications, dynamic environments, uncertain UAV locations, etc. will make the area coverage task more difficult [12, 13, 14, 15, 16]. The pioneer work in distributed coverage control was performed by [PERSON] [1]. In this work, the authors formulated the coverage control as a geographical optimization problem and derive the optimal decentralized. In [14], to make multi-UAVs cooperatively complete the coverage task more efficiently, the authors proposed a distributed coverage algorithm for a group of UAVs to cover a convex region with average Voronoi partition, which aims to ensure that the areas of all Voronoi partitions are identical. In [15], a distributed probability-based cooperative search method with Bayesian updating rule was addressed. Within that, a consensus-like fusion scheme was proposed for multiagent map fusion. The authors proved that all the individual probability maps converge to the same one that reflects the true existence or nonexistence of targets within each cell. In [17], [PERSON] et al. studied the coverage control problem for mobile sensor networks with limited communication ranges. The goal of the problem was to minimize a coverage cost function,which indicates the largest arrival time from the mobile sensor network to the points on a circle. In [18], the authors considered the heterogeneous coverage control problem, where the different density functions serve as a way to both abstract and encapsulate different sensing capabilities. In the case that the UAVs' location is not known a priori, the techniques of positioning already exist in both simulation and actual flight [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. In [19], the authors proposed a method of passive detection of a moving aerial target by using heterogeneous satellites. In [20], methods of mode transition in the integrated navigation based on heterogeneous sensor integration were presented. In [21], the authors developed a method to reliably track many small rigid bodies with identical motion-capture marker arrangements. In [22], the authors presented a technique for multi-UAV localization using ranging measurements from Two-Way Time-Of-Flight UWB transceivers. However, the agents here must depend on some external infrastructure for positioning, relative localization techniques can be adopted to solve the location-based tasks, and the techniques do not need external infrastructure. In [23], the authors proposed a visual relative location (RL) algorithm, which is based on pattern detection using onboard cameras, to estimate the position of their agents. Their algorithm uses a combination of flood-fill techniques, which provide both high speed and precision for RL, but the performance of the algorithm is limited by the range to the target and lighting condition. In [24], the authors proposed an RL algorithm by using distance and odometry measurements to study the single landmark docking problem. The proposed method can achieve both autonomous approaching and landing tasks in GPS-denied environments. This experiment proves the feasibility of the RL algorithm for position estimation by using a single landmark. In [25], the relative position between neighboring UAVs is obtained by using UWB and visual odometry.