RRT-SMP: Socially-encoded Motion Primitives for Sampling-based Path Planning

Abstract

In this paper we propose a new method of encoding social norms and rules into sampling-based motion planners. Inspired from the social force model (SFM), we modify and use it as a social intention model (SIM) to reshape the motion primitives (MP) of the rapidly-exploring random tree (RRT) motion planner for the socially aware robot navigation. We also introduce a new benchmark for evaluating social planning performance, so called as the social effort index (SEI). The experimental results show that the socially-guided motion primitives-based RRT increases safe and social interactions between the robot and human agents about 50% compared to the typical RRT-embedded MP (RRT-MP) in human populated environments.