Improving Local Motion Planning with a Reinforcement Learning Approach

Abstract

This paper introduces a new Reinforcement Learning (RL) based local motion planning (RL-LMP) approach for mobile robots. This method enables virtual or real mobile platforms, to follow a path and navigate from location A to B. The method consists of two stages: a training stage and an online stage. The training stage consists in the robot iteratively learning to follow previously defined paths in a simulation environment. New RL state representations are proposed as well as a strategy to deal with the delayed reward problem and an approach to guide the exploration-exploitation of the RL model considering prior knowledge. Through training in a virtual environment and assimilation of human driving behaviors (using a gamepad), an RL model is obtained and used in the online stage, enabling a mobile platform, in a simulation or real environment, to move along a path avoiding obstacles. A set of tests and experiments were performed in different scenarios in virtual and real environments in order to assess the performance of the local motion planning approach. Validation of the RL-LMP approach, in a real environment, was carried out in the ISR-InterBot platform with the algorithm developed in ROS. The obtained results show that the RL-LMP approach provides a valid solution to the local motion planning problem (small lateral error while following paths) and suggests promising perspectives for improvement in the future.