TDPP-Net: Achieving three-dimensional path planning via a deep neural network architecture

Abstract

Path planning plays a significant role in autonomous navigation for robots in complex environments and hence has been extensively studied for decades. However, the computational time of most existing methods are dependent on the scale and complexity of environment, which leads to the compromise between time efficiency and path quality. To tackle this challenge, deep neural network based (DNN-based) planning methods have been actively explored. However, despite the success of DNN-based 2D planner, 3D path planning, which is a significant primitive for quite a few autonomous robots, is rarely handled by DNNs. In this paper, we propose a novel end-to-end neural network architecture named Three-Dimensional Path Planning Network (TDPP-Net) to realize DNN-based 3D path planning. Embedding the action decomposition and composition concept, our network predicts 3D actions merely through 2D convolutional neural networks (CNNs). Besides, the computational time of TDPP-Net is almost independent of environmental scale and complexity for each action prediction. The experimental results demonstrate that our approach exhibits remarkable performance for planning real-time paths in unseen 3D environments.