# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
## 4 Experiments and Discussion
### Qualitative Experiments  
We tested the system in a variety of simulated scenarios. In Figure 7, a scenario was used in which the system was forced to replan. The robot was placed in the middle of the central room of a multi-room environment and the goal was placed in a room next to it blocked by three static obstacles. The robot senses the blocked route and adds it to the cost map, then the S-DOVS planner reduces its velocity, as it may not be able to avoid the three obstacles and the walls (i.e., the robot senses that it may become trapped). After the robot reduces its velocity and before it becomes completely stuck, the waypoint generator computes a new plan that the robot must follow to be able to reach the goal. In addition to this scenario, more experiments were performed in a newly designed environment to test the robot's navigation in a scenario with differently shaped rooms, narrow corridors, and moving obstacles, as seen in Figure 8. This scenario poses challenges to the robot that it has not experience before; nonetheless, the system is able to navigate successfully. The system was tested on a Turtlebot 2 platform equipped with a Hokuyo 2D-LIDAR sensor and an NUC with an Intel Core i5-6260U CPU and 8 GB of RAM. The replanning capability in a real-world scenario was tested in a room connected to a corridor with two doors. The robot was sent to a goal, then encountered a closed door on the original path. The system replanned and navigated by an alternative path through the other door, as shown in Figure 9.  
The robot was then tasked to navigate autonomously in a room with human occupants acting as dynamic obstacles. The robot successfully completed the task by using the whole integrated system. The goals were dynamically placed in the corners of the window, and the robot needed to avoid collisions with the humans to reach the goals. An image of this experiment can be seen in the previously shown Figure 1. In addition, an experiment was conducted in which the robot was forced to navigate through the corridor, enter the room,  
Figure 6: Comparison of the performance of different waypoint generators: (**a**) success rates and (**b**) mean time to reach the goal. exit the room, and navigate again through the corridor while avoiding pedestrians in order to explicitly show a combination of the global planner with the S-DOVS. The intermediate waypoints sent by the waypoint generator are shown in the visualization in Figure 10. Videos of every described experiment can be found in Supplementary Video S1. The successful integration of various components, including the local planner, global planner, waypoint generator, localization system, and obstacle tracker, was demonstrated in both simulated and real-world experiments, underscoring the effectiveness of this integrated approach. The S-DOVS local planner efficiently computes safe motions while maintaining high velocities whenever possible, enabling it to navigate through moving obstacles while adhering to the kinodynamic constraints of the robot. This aspect sets it apart from existing state-of-the-art systems, which often overlook such constraints. Moreover, S-DOVS adopts a strategy-based planning approach, significantly reducing the sim2 real gap by eliminating the need for real-world data during training, which is a substantial challenge in the field of robotics. Figure 8: Here, the robot is navigating in a different map among moving obstacles; it must face various challenges to reach the goal in the shortest time possible. The goal is located at the square red point, the green line is the global trajectory planned towards the goal in the workspace, and the velocity space (VS) depicts the DOVS at the instant in which the robot is at the arrow’s location in the workspace. The dark area in VS represents the velocities leading to collision with the right wall, and the green line shows the velocities to move the robot towards the next waypoint on the planned trajectory from the current velocity, i.e., the center of the rhombus.