# Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
## 4 Experiments
### Benchmark Comparisons  
In this section, we conduct benchmark comparisons using simulation experiments to verify the effectiveness and exploration efficiency of the proposed method. Robot exploration in maze scenes is the most effective method to verify the efficiency of autonomous exploration [13]. Thus, we manually constructed two large-scale mazes, Maze-1 (\(48\times 63\times 2\) m\({}^{3}\)) and Maze-2 (\(66\times 62\times 2\) m\({}^{3}\)), in Gazebo simulation platform. The cross-sectional length of the road in Maze-1 is 4\(\sim\)6 m, and in Maze-2, 8\(\sim\)10 m. The topological maps of these mazes are generated by manually placing global targets on corners and intersections, and then connecting them according to the topology of mazes to simulate maze information [18]. But, we leave some space for the robot to explore autonomously without prior information. The mazes and their prior topological maps are shown in Figure 8.  
We employ the optimal exploration strategy provided by the CPP solver to guide robot exploration and compare it with FUEL and FAEP [12; 13]. They are state-of-the-art frontier-based methods which have been proposed in recent years, and which exhibit high exploration efficiency and have open-sourced their code to serve community. In all experiments, we utilize UAV as the exploration robot, with \(v_{max}=0.6\) m/s, \(\omega_{max}=0.9\) rad/s, and the maximum acceleration is 0.6 m/s\({}^{2}\). UAV equips depth camera to collect environmental information. FOV of depth camera is configured as \([80\times 60]\) deg, \(h_{max}\) is 4.5 m. The grid map of the local update range is \(5\times 5\times 2\) m\({}^{3}\). In all scenarios, three methods are run more than six times with the same configuration. Figure 7: Real-world experiment vehicle platform. We evaluate the performance of the above methods based on exploration time, flight distance, and coverage efficiency. FAEP also utilizes FIS to update frontier cluster information and based TSP to plan global exploration paths. Therefore, we set the frontier cluster length limit of all methods to 2 m and counted the remaining number of frontier clusters during exploration. The number could directly reflect the computational burden of the path planner: the more frontier clusters remain in scene, the more enormous the solver computation. Figure 8 displays exploration trajectories of different methods in two mazes. Our method prevents the robot from unnecessarily revisiting explored areas but ensures that the robot does not miss scenes outside prior information, even in scenes with large cross-sectional road lengths like Maze-2. In contrast, other methods re-explore already visited areas during exploration, leading to lower efficiency and inevitable resource wastage. The detailed exploration process of our method in Maze-1 is illustrated in Figure 9. The robot prioritizes exploring areas outside prior information in the given region. Subsequently, it follows the global exploration strategy to explore other areas, disregarding the direction of other frontier clusters during exploration. Finally, the robot actively loops close to cover the frontier clusters near the agent with the lowest priority. The exploration process aligns with the concept depicted in Figure 3.  
Figure 8: Benchmark comparison of exploration trajectories of the proposed method, FUEL, and FAEP, in two mazes. Figure 9: Partial exploration trajectories generated by our method in Maze-1. Figure 10 displays the process of volume coverage for all methods in two mazes. Our method demonstrates higher efficiency and nearly linear performance in conducting exploration. In contrast, other methods exhibit coverage stagnation or slower growth during exploration, meaning the robot moved towards a previously missed or visited space. The number of the remaining frontier clusters during exploration for all methods is shown in Figure 11. Compared with other methods, our method ensures a lower count of remaining frontier clusters.