# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
## 5 Conclusions  
This work presents an approach for autonomous navigation in complex and dynamic environments. The proposed approach integrates the strategies-based S-DOVS method designed for dynamic scenarios as the local planner, and uses a waypoint generator for long-range navigation. S-DOVS takes into account the robot's kinodynamics and the velocity and position of obstacles to plan the robot's motion within a time-space horizon, avoiding dynamic obstacles while balancing safety and ensuring high robot velocities whenever possible. The waypoint generator generates a set of waypoints based on a static  
Figure 10: Experiment in which the robot has to avoid collisions with dynamic obstacles and follow intermediate waypoints to reach the goal. The path denoted in Algorithm 1 as \(path_{aux}\) is shown in red, while the green point in \(path_{aux}\) is the current waypoint that the robot is following.  
Figure 9: The moment before the robot senses the closed door, adds it to the cost map, and computes a new path. In the subsequent time steps, it detects that the way is blocked and computes a path through the other door, which is open.  
environment map, significantly improving the success rate and time to reach the goal with respect to the case in which the generator is not used. Multiple experiments in simulation and real-world scenarios demonstrate the effectiveness of our approach. Our system is able to navigate dense and dynamic scenarios with different shapes while avoiding both static and dynamic obstacles, performing better than the state-of-the-art techniques used for comparison. The results of our experiments demonstrate the effectiveness of this approach for use in real-world applications and populated scenarios.  
Future work will include exploring the use of different local planners based on deep reinforcement learning approaches to further improve the performance of the system. Moreover, the work could be extended to 3D navigation for use with autonomous drones that are able to navigate in the presence of people or other drones moving in the vicinity. Furthermore, approaches as [33; 34] could be used to have the actual shape of obstacles on the environment and improve the robot navigation and obstacle avoidance.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13158925/s1](https://www.mdpi.com/article/10.3390/app13158925/s1)]([https://www.mdpi.com/article/10.3390/app13158925/s1](https://www.mdpi.com/article/10.3390/app13158925/s1)), Video S1: supplementary.mp4.  
Conceptualization, D.M.-B., L.R. and L.M.; methodology, D.M.-B., L.R. and L.M.; software, D.M.-B.; validation, D.M.-B., L.R. and L.M.; formal analysis, D.M.-B., L.R. and L.M.; investigation, D.M.-B., L.R. and L.M.; resources, D.M.-B.; data curation, D.M.-B.; writing--original draft preparation, D.M.-B.; writing--review and editing, D.M.-B., L.R. and L.M.; visualization, D.M.-B.; supervision, L.R. and L.M.; project administration, L.M.; funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Spanish projects MCIN/AEI/PID2019-105390 RB-I00 and Aragon Government_FSE-T45_20R.