Robot Path Planning by LSTM Network Under Changing Environment

Abstract

Path planning is an important function for executing autonomous moving robots, and many path planning methods that satisfy various constraints, such as avoiding obstacles and energy efficiency, have been proposed. However, these conventional methods have several difficulties for apply to the actual applications, such as the instability, low reproducibility, huge training data set required. Therefore, we propose a novel robot path planning method that combines the rapidly exploring random tree (RRT) and long short-term memory (LSTM) network. In this method, numerous and good paths are generated in the robot configuration space by the RRT method, a convolutional autoencoder and LSTM combination network is trained by them. The proposed method overcomes the difficulty of general methods with neural networks, i.e., “the acquisition of a large amount of training data.” Moreover, the difficulty of general random based methods, i.e., “the reproducible path generation” is resolved with high-speed.