Multi Agent Navigation in Unconstrained Environments using a Centralized Attention based Graphical Neural Network Controller

Abstract-- In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is to ensure that each vehicle reaches a desired target state without colliding with any other vehicle or obstacle in an unconstrained environment. The model utilizes an attention based Graphical Neural Network paradigm that takes into consideration the state of all the surrounding vehicles to make an informed decision. This allows each vehicle to smoothly reach its destination while also evading collision with the other agents. The data and corresponding labels for training such a network is obtained using an optimization based procedure. Our method also outperforms comparable graphical neural network architectures. Meanwhile, the rectangles with broken boundaries represents the desired destination/target I. INTRODUCTION We would like to produce the Data driven approaches to senorimotor control have seen a sequence of control actions such that the five vehicles safely meteoric growth with the advent of deep learning in the last reach their destination state without colliding with each other decade [1], [2], [3], [4]. Powerful neural network architectures or the circled obstacle. These control actions are produced can now be trained and deployed in real-time applications by the Attention Based Graphical Neural Network (A-GNN).