A Mobile Robot Experiment System with Lightweight Simulator Generator for Deep Reinforcement Learning Algorithm.

More and more researchers are trying to use deep reinforcement learning (DRL) for mobile robot tasks due to its powerful inference capability. However, deep reinforcement learning requires a large amount of data for DRL training in the pre-experimental stage, which hinders the application of the algorithm. On the other hand, the inconsistency between the ROS data interface and DRL GYM-Like data interface leads to a high cost of migration of the algorithm verification. This paper proposes a fast simulator generation method using linear approximate kinematics model and bake-based lidar rendering methods to generate a fast approximate simulator used in the pre-experiment stage to solve the problem of data cost. At the same time, an experimental system design scheme that converts the ROS interface into a GYM-like interface is also proposed to simplify the deployment process of deep reinforcement learning. We evaluate our proposed method on collision avoidance tasks in a variety of kinematics models and lidar scenarios. Our Method achieves about 14.2 times kinematics simulation speedup and 2.56 times lidar rendering speedup. We open-sourced our simulation environment and robot system software at https://github.com/efc-robot/MultiVehicleEnv and https://github.com/efc-robot/NICS_MultiRobot_Platform