A LSTM Neural Network applied to Mobile Robots Path Planning

Abstract

Mobile robots path planning is a central problem in every situation where human intervention is not desired or not possible to accept: full automated industrial warehouses or general stocking areas and every domestic application that involves a mobile robot and special cases where environment is prohibited for human accessing like toxic wastes and bombs defusing [1]. Currently, neural networks are applied to problems related to mobile robot navigation. However, they are not as popular as in applications like image processing, speech recognition or machine translation, where they are commercially relevant. In this paper we propose a Long Short-Term Memory (LSTM) neural network as an online search agent to tackle the problem of mobile robots path planning in unknown environments, meaning that the agent relies only on local map awareness realized with a LRF sensor and relative information between robot and goal position. Specifically, a final structure of LSTM network is analyzed and its performance is compared with the A* algorithm, a widely known method that follows the best-first search approach. Subsequently, an analysis of the method developed on a real robot is described.