Belief Aided Navigation using Bayesian Reinforcement Learning for
  Avoiding Humans in Blind Spots
Jinyeob Kim Daewon Kwak Hyunwoo Rim Donghan Kim
Recent research on mobile robot navigation has focused on socially aware
navigation in crowded environments. However, existing methods do not adequately
account for human robot interactions and demand accurate location information
from omnidirectional sensors, rendering them unsuitable for practical
applications. In response to this need, this study introduces a novel
algorithm, BNBRL+, predicated on the partially observable Markov decision
process framework to assess risks in unobservable areas and formulate movement
strategies under uncertainty. BNBRL+ consolidates belief algorithms with
Bayesian neural networks to probabilistically infer beliefs based on the
positional data of humans. It further integrates the dynamics between the
robot, humans, and inferred beliefs to determine the navigation paths and
embeds social norms within the reward function, thereby facilitating socially
aware navigation. Through experiments in various risk laden scenarios, this
study validates the effectiveness of BNBRL+ in navigating crowded environments
with blind spots. The model's ability to navigate effectively in spaces with
limited visibility and avoid obstacles dynamically can significantly improve
the safety and reliability of autonomous vehicles.