Safe deep learning-based global path planning using a fast collision-free path generator

• Published in: Robotics and autonomous systems 2023-05, Vol.163, p.104384, Article 104384
• Main Authors: Chehelgami, Shirin, Ashtari, Erfan, Basiri, Mohammad Amin, Tale Masouleh, Mehdi, Kalhor, Ahmad
• Format: Article
• Language: English
• Subjects: Deep learning in robotics and automation; Fast global path planner; Mobile robots; Recurrent neural network; Safe path generator
• ISSN: 0921-8890; 1872-793X
• DOI: 10.1016/j.robot.2023.104384

In this research, a global path planning method based on recurrent neural networks by means of a new Loss function is presented, which regardless of the complexity of the configuration space, generates the path in a relatively constant time. The new Loss function is defined in such a way that in addition to learning the input data of the network, it creates an adjustable safety margin around the obstacles and ultimately creates a safe path. Moreover, a new global path planning method is also introduced, which is used to create the dataset required to train the proposed neural network. The convergence of this method is mathematically proven and it is shown that this method can also produce a suboptimal path in a much shorter time than the common methods of global path planning reported in the literature. In short, the main purpose of this research consists in providing a method which can create a suboptimal, fast and safe path for a mobile robot from any random starting point to any random destination in a known environment. First, the proposed methods will be implemented for different two-dimensional environments consisting of convex and non-convex obstacles, considering the robot as a point-mass, and then it will be implemented in a simulation environment, AI2THOR. Compared to classical global path planning algorithms, such as RRT and A*, the proposed approach demonstrates better performance in complex and challenging environments. •Proposing a novel fast global path planner for generating the LSTM’s input path data.•A novel loss function is proposed that helps the LSTM network predict safe paths.•Comparing and evaluating proposed and existing methods in different environments.•AI2THOR simulation environment results validate the effectiveness and feasibility.