A multilevel mapping based pedestrian model for social robot navigation tasks in unknown human environments

Social robot navigation aims to generate human-friendly paths in human-robot interactive environments. This paper focuses on maintaining humans' physical safety and mental comfort during robot navigation in an unknown dynamic environment. To achieve this goal, we use a variant of a pedestrian model that is particularly developed for low or average density environments. Design decisions on the representation of the obstacle and pedestrian are important for smooth motion planning. Limiting the local obstacles as a region centered at the robot would be taken into consideration has weaknesses in terms of time complexity because a much detailed map has a great number of cells to be evaluated. The study contributes to the theoretical field with extensions such as the development of the obstacle representation model which aims to overcome the computational cost of the current solutions for smooth motion planning which can be a bottleneck for the entire system. The proposed method is tested on a physical mobile robot in hallway scenario both in real-world environment and simulation, and its success is experimentally shown.