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

TL;DR

This paper introduces a centralized attention-based Graphical Neural Network controller for multi-agent navigation that ensures collision-free movement in unconstrained environments, demonstrating strong generalization and superior performance over existing methods.

Contribution

The work presents a novel neural model that integrates attention-based GNNs for multi-vehicle control, trained via optimization, with improved generalization and performance.

Findings

Model generalizes to more vehicles than training data

Outperforms existing GNN architectures

Ensures collision-free navigation in unconstrained environments

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. Experimental results demonstrates that our model is powerful enough to generalize even to situations with more vehicles than in the training data. Our method also outperforms comparable graphical neural network architectures. Project page which includes the code and supplementary information can be found at https://yininghase.github.io/multi-agent-control/