## 1 Introduction  
In recent years, autonomous vehicles have gained increasing attention in numerous applications. Autonomous surveillance systems are a representative example of such vehicles [1; 2; 3]. Typically, autonomous surveillance vehicles can be either unmanned aerial vehicles (UAVs) [4], unmanned ground vehicles (UGVs) [5], or unmanned marine vehicles (UMVs) [6]. The type of vehicle is dependent on the size of the environment, the presence of obstacles, and the resources available. Additionally, the vehicles can be used individually [7] or in fleets consisting of several vehicles [8]. In our paper, UGV fleet surveillance will be the focus. Specifically, a fleet management strategy that can generate the global paths for a fleet of several UGVs is proposed. The proposed methods yield the shortest paths to scan a large area while simultaneously producing feasible routes that obey the kinematic constraints of the UGVs. In the literature on UGV path planning for surveillance, several different topics have been explored. Some researchers have explored the possibility of wirelessly controlling UGVs to conduct surveillance tasks [9; 10; 11]. In these applications, data were transmitted wirelessly through Wi-Fi, Bluetooth, or radio transmission while an operator manually controlled the vehicle to traverse the environment. This can reduce the manpower needed to perform surveillance tasks, but these approaches lack any intelligent decision-making and rely on human intervention. Therefore, other studies improved on this concept by applying some optimization techniques to find optimal paths in known environments. In one study, a heuristic path-planning algorithm was developed for road network surveillance [3]. In this study, the road network was modeled as a graph, where the sensor scanning radius was able to capture a large amount of the surrounding environment while traversing the roads. However, road network surveillance is simple when compared to surveying large open areas. In these cases, the environment should be discretized into a graph-like structure for optimization. One study focuses on this issue by developing a path-planning algorithm for autonomous surveillance that is computationally more efficient than the A* algorithm [12]. The authors also considered static obstacles in their study. Their heuristic algorithm was developed to provide the shortest path that is the furthest from obstacles to minimize the possibility of collisions. In path-planning applications, a large area is typically divided into several smaller sub-areas (decomposition), and the optimal path within each sub-area can be calculated [13; 14]. Solving for the minimum cost path within each sub-area can be modeled as the traveling salesman problem (TSP). However, in UGV surveillance applications, the sensors onboard the vehicle, such as LiDAR, typically have a large scanning range that can cover a portion of the surrounding area [15]. In these cases, the problem can be better modeled as the coverage path-planning problem (CPP). However, only a subset of nodes within an area (if it is discretized into a graph) need to be visited due to the large scanning range of the sensor. This problem is commonly referred to as the covering salesman problem (CSP) [16]. To solve the CSP within a sub-area, several methods have been proposed. One of the more trivial methods is the random walk (RW). In the RW algorithm, a linear or spiral path can be calculated for a random travel direction. Then, the next trajectory is randomly selected when the boundary of the sub-area is reached [17]. The RW typically yields sub-optimal results and lacks logical decision-making strategies. The genetic algorithm (GA) has also seen great success in solving the TSP or CSP [18; 19; 20]. By mimicking the evolutionary process through crossover and mutation, the GA uses the concept of survival of the fittest to continuously improve a population of solutions with respect to the problem's objective [21].