## 5 Conclusions  
In this research, a two-stage routing algorithm was proposed to generate optimal paths for a fleet of autonomous surveillance vehicles: the lower-level and higher-level path generation algorithms. First, the total area was divided into several sub-areas for logistical purposes. Then, optimal paths were pre-processed for several different start points within each sub-area using a modified version of the ACO algorithm fit for the CSP problem. This process is called lower-level path generation. Then, a GA was used to assign the vehicles to the sub-areas while selecting the optimal starting point such that the total distance was minimized. This process is called higher-level path generation. The paths generated by these approaches were initially infeasible due to the harsh turn angles. However, a feasible path generator was developed to modify the paths such that the turn radius of the vehicle was properly accounted for. To do so, a corner-cutting method was used for less sharp angles, and the Reeds-Shepp path was used for more harsh angles. Additionally, breakdown scenarios can be effectively handled by the higher-level path generation. This was achieved by updating the remaining portion of all paths and redistributing them amongst the operating vehicles. A study was conducted in Goheung, South Korea, for a fleet of autonomous surveillance vehicles. Several different scenarios were proposed that involved different start locations, numbers of vehicles, and vehicle breakdowns. The results presented in this article highlight the effectiveness of the lower-level, higher-level, and feasible path generator for autonomous UGV applications. Figure 16: Routes produced in Scenarios 1 to 6 in (**a**–**f**), respectively. In each scenario, the different colored lines represent the paths assigned to different vehicles. In this study, several novelties were achieved. Firstly, this study is one of the first to successfully use the ACO algorithm for the CSP model. This was achieved by modeling the sensor scanning radius such that the nodes surrounding the vehicles' current locations are also visited. This resulted in paths that do not need to physically visit all nodes, but instead, only a subset of nodes should be visited to achieve the desired coverage. Secondly, this study proposes a method of real-time scheduling for breakdown scenarios that redistributes the workload amongst all vehicles upon vehicle breakdown. This was achieved by executing the higher-level path generation algorithm with the remaining vehicles and their respective paths at the point of a breakdown. Finally, a path refinement algorithm was proposed to make the vehicle paths feasible with respect to the kinematic constraints. This was achieved by using a corner-cutting method (with respect to the vehicle turn radius) at each corner where this operation would not cause too much simplification. Then, the Reeds-Shepp algorithm was used where the corner-cutting method could not be applied. The Reeds-Shepp path would add some additional maneuvers such that the shortest forward and backward path can provide a smooth transition at sharp corners. Existing research has proposed some solutions to this issue; however, the corner-cutting and Reeds-Shepp combination is one of the novel approaches developed in this research. For this research, the vehicle battery was not considered. This is because the operation time for the battery is 4 hr. The results presented in this article show that the longest operation time was 12 min; thus, there will always be enough power for the selected study. However, this may not always be the case for different studies. Therefore, future studies can incorporate an effective constraint-handling strategy into the higher-level path generation. Also, an arbitrary depot location was selected for this study (to test the feasibility of the proposed methods). This can be modified in future studies to model the appropriate ending location for all vehicles.