  The major challenges of collision avoidance for robot navigation in crowded
scenes lie in accurate environment modeling, fast perceptions, and trustworthy
motion planning policies. This paper presents a novel adaptive environment
model based collision avoidance reinforcement learning (i.e., AEMCARL)
framework for an unmanned robot to achieve collision-free motions in
challenging navigation scenarios. The novelty of this work is threefold: (1)
developing a hierarchical network of gated-recurrent-unit (GRU) for environment
modeling; (2) developing an adaptive perception mechanism with an attention
module; (3) developing an adaptive reward function for the reinforcement
learning (RL) framework to jointly train the environment model, perception
function and motion planning policy. The proposed method is tested with the
Gym-Gazebo simulator and a group of robots (Husky and Turtlebot) under various
crowded scenes. Both simulation and experimental results have demonstrated the
superior performance of the proposed method over baseline methods.
