Training and preparing first responders and humanitarian robots for Mass
Casualty Incidents (MCIs) often poses a challenge owing to the lack of
realistic and easily accessible test facilities. While such facilities can
offer realistic scenarios post an MCI that can serve training and educational
purposes for first responders and humanitarian robots, they are often hard to
access owing to logistical constraints. To overcome this challenge, we present
HEROES- a versatile Unreal Engine simulator for designing novel training
simulations for humans and emergency robots for such urban search and rescue
operations. The proposed HEROES simulator is capable of generating synthetic
datasets for machine learning pipelines that are used for training robot
navigation. This work addresses the necessity for a comprehensive training
platform in the robotics community, ensuring pragmatic and efficient
preparation for real-world emergency scenarios. The strengths of our simulator
lie in its adaptability, scalability, and ability to facilitate collaboration
between robot developers and first responders, fostering synergy in developing
effective strategies for search and rescue operations in MCIs. We conducted a
preliminary user study with an 81% positive response supporting the ability of
HEROES to generate sufficiently varied environments, and a 78% positive
response affirming the usefulness of the simulation environment of HEROES