Robot Path Planning via Neural-Network-Driven Prediction

Abstract

This article presents a novel heuristic method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neural-network-driven prediction (NEED)</i> for robot path planning problems. Different from classical heuristic methods, NEED is not designed for some specific environments, which can be applied to various environments. NEED has an encoder–decoder structure, which consists of three modules: convolutional neural network backbone, spatial pooling module, and decoder module. By learning from a number of successful path planning cases, NEED can learn to analyze the environment structure and predict the promising search region for the new path planning problem. This predicted region serves as a heuristic to guide the search direction of path planning algorithms. A series of simulation experiments are conducted to demonstrate that NEED significantly improves the algorithm performance on solving new path planning problems. Meanwhile, NEED can also be applied to highly dynamic environments since it can output the prediction results at a speed of over 100 Hz on a laptop.