Social navigation is beneficial for mobile robots in human inhabited areas. In this paper, we focus on smooth path tracking and handling disruptions during plan execution in social navigation. We extended the social force model (SFM)-based local planner to achieve smooth and effective social path following. The SFM-based local motion planner is used with the A* global planner, to avoid getting stuck in local minima, while incorporating social zones for human comfort. It is aimed at providing smooth path following and reducing the number of unnecessary re-plannings in evolving situations and a waypoint selection algorithm is proposed. The whole plan is not directly assigned to the robot since the global path has too many grid nodes and it is not possible to follow the path easily in such a dynamic and uncertain environment inhabitated by humans. Therefore, the extracted waypoints by the proposed waypoint selection algorithm are incrementally sent to the robot for smooth and legible robot navigation behavior. A corridor like scenario is tested in a simulated environment for the evaluation of the system and the results demonstrated that the proposed method can create paths that respect people's social space while also eliminating unnecessary replanning and providing that plans are carried out smoothly. The study presented an improvement in the number of replannings, path execution time, path length, and path smoothness of 90.4%, 53.7%, 8.3%, 55, 2%, respectively.