  Robot navigation in a safe way for complex and crowded situations is studied
in this work. When facing complex environments with both static and dynamic
obstacles, in existing works unicycle nonholonomic robots are prone to two
extreme behaviors, one is to fall into dead ends formed by obstacles, and the
other is to not complete the navigation task in time due to excessive collision
avoidance.As a result, we propose the R-SARL framework, which is based on a
deep reinforcement learning algorithm and where we augment the reward function
to avoid collisions. In particular, we estimate unsafe interactions between the
robot and obstacles in a look-ahead distance and penalize accordingly, so that
the robot can avoid collisions in advance and reach its destination
safely.Furthermore, we penalize frequent excessive detours to reduce the
timeout and thus improve the efficiency of navigation.We test our method in
various challenging and complex crowd navigation tasks. The results show that
our method improves navigation performance and outperforms state-of-the-art
methods.
