# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
## 1 Introduction  
Autonomous navigation in dynamic environments is a challenging problem that has received significant attention in robotics research. Traditional planners have been successful in navigating in static environments where the robot is aware of the location of all obstacles and can plan a collision-free path to its destination. However, in dynamic environments where obstacles can move, the task becomes much more difficult. Dynamic obstacles have non-zero velocity and can change their position and orientation, which means that the robot needs to take into account their trajectory and predict their future movements to avoid collisions. Failure to do so can result in suboptimal behavior or even catastrophic outcomes. One approach for solving the problem of autonomous navigation in dynamic environments is the use of the Dynamic Object Velocity Space (DOVS) [1], which provides a representation of the environment in terms of the position and velocity of objects. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner uses DOVS to plan the robot's trajectory taking into account its kinodynamic constraints and the movement of dynamic obstacles. While S-DOVS has shown promise in aiding robot navigating in dynamic environments, it has limitations in navigating through large maps with unknown environments, avoiding convex objects, and handling trap situations, and is reliant on perfect knowledge of the environment. To address these limitations, we propose an improved version of the S-DOVS planner which is integrated into a full navigation stack that includes an existing localizationsystem and global planner, a modified obstacle tracker, and a new waypoint generator. Our waypoint generator connects the global planner with S-DOVS, enabling the robot to navigate through large scenarios using known map information while considering robot kinodynamic constraints and the movements of dynamic obstacles. This provides greater freedom to the local motion planner to avoid obstacles while following the global path, and has replanning capability. We compare the waypoint generator to other state-of-the-art approaches and evaluate our system through extensive quantitative and qualitative experiments in simulation and with a ground robot, as seen in Figure 1, to demonstrate its effectiveness in real-world scenarios. The contributions of this work are:  
* Modification of a state-of-the-art local motion planner used in dynamic environments (S-DOVS) to improve it and enable it to work in real-world scenarios. * A novel intermediate waypoint trajectory generator designed to be used with motion planners in dynamic environments to enhance their capabilities and enable long-range navigation. * Adaptation of an existing obstacle tracker for use with S-DOVS, along with a new waypoint generator, a localization system, and an ROS adaptation of all of the components into a full navigation stack. * Extensive qualitative and quantitative navigation results that show the system's performance in both simulated and real situations. This paper is an extended version of [2]. Compared to the previous version, the definition of the system and its theoretical development are more comprehensive. The proposed novel waypoint generator is greatly improved here by introducing a spatial horizon to publish the goals that the S-DOVS planner must follow. Moreover, this spatial horizon is variable depending on the importance of the points of the global path. Furthermore, we have refined and improved the criteria necessary for replanning and to avoid situations in which the robot is prevented from reaching its goal. In addition, we have conducted new experiments. In the simulations, we carried out new replanning experiments and used a more challenging scenario. We performed new quantitative experiments to demonstrate our waypoint generator, then compared it with other the state-of-the-art approaches and with our previous generator presented in [2].