ARORA and NavSim: a simulator system for training autonomous agents with geospecific data

Armando Fandango; Alexander Gutierrez; Clive Hoayun; Jonathan Hurter; Dean Reed

doi:10.1117/12.2647733

28 October 2022 ARORA and NavSim: a simulator system for training autonomous agents with geospecific data

Armando Fandango, Alexander Gutierrez, Clive Hoayun, Jonathan Hurter, Dean Reed

Proceedings Volume 12276, Artificial Intelligence and Machine Learning in Defense Applications IV; 122760C (2022) https://doi.org/10.1117/12.2647733
Event: SPIE Security + Defence, 2022, Berlin, Germany

Abstract

This study accompanies the initial public release of the software for ARORA, or A Realistic Open environment for Rapid Agent training, and marks a high point of several years of work for the mature and completely open ARORA simulator. The purpose of ARORA is to support the training of an autonomous agent for tasks associated with a large-scale and geospecific outdoor urban environment, including the task of navigation as a car. The study elaborates on the simulator's architecture, agent, and environment. For the environment, ARORA provides an improvement on similar simulators through an unconstrained geospecific environment with detailed semantic annotation. The agent is represented as a car available with four different options of physics fidelity. The agent also has sensors available: a pose sensor, a camera sensor, and a set of three proximity sensors. Future use cases from training extend to both civilians and militaries (including human training and wargaming), in terms of training autonomous agents in outdoor urban environments. The study also presents a brief description of NavSim, a Python-based companion tool. The purpose of NavSim is to connect to ARORA (or any other similar simulator) and train an agent using reinforcement-learning algorithms. The study also provides challenges in development and subsequent work-arounds and solutions. The goal of the ARORA & NavSim system is to provide communities with a high-fidelity, publicly available, free, and open-source system for training an autonomous agent as a car.

Conference Presentation

Citation Download Citation

Armando Fandango, Alexander Gutierrez, Clive Hoayun, Jonathan Hurter, and Dean Reed "ARORA and NavSim: a simulator system for training autonomous agents with geospecific data", Proc. SPIE 12276, Artificial Intelligence and Machine Learning in Defense Applications IV, 122760C (28 October 2022); https://doi.org/10.1117/12.2647733

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available