SCAN: Socially-Aware Navigation Using Monte Carlo Tree Search

Jeongwoo Oh, Gunmin Lee, Junseo Lee, Minjae Kang, Jeongho Park, Songhwai Oh

Abstract:

Designing a socially-aware navigation method for
crowded environments has become a critical issue in robotics. In
order to perform navigation in a crowded environment without
causing discomfort to nearby pedestrians, it is necessary to
design a global planner that is able to consider both humanrobot interaction (HRI) and prediction of future states. In
this paper, we propose a socially-aware global planner called
SCAN, which is a global planner that generates appropriate
local goals considering HRI and prediction of future states.
Our method simulates future states considering the effects of
the robot’s actions on the future intentions of pedestrians using
Monte Carlo tree search (MCTS), which estimates the quality
of local goals. For fast simulation, we execute pedestrian motion
prediction using Y-net and future state simulation using MCTS
in parallel. Neural networks are only used in Y-net and not
in MCTS, which enables fast simulation and prediction of a
long horizon of future states. We evaluate the proposed method
based on the proposed socially-aware navigation metric using
realistic pedestrian simulation and real-world experiments. The
results show that the proposed method outperforms existing
methods significantly, indicating the importance of considering
human-robot interaction for socially-aware navigation.