HEROES: Unreal Engine-based Human and Emergency Robot Operation Education System
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910819268165632 |
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| author | Chaudhary, Anav Tiwari, Kshitij Bera, Aniket |
| author_facet | Chaudhary, Anav Tiwari, Kshitij Bera, Aniket |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_14508 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | HEROES: Unreal Engine-based Human and Emergency Robot Operation Education System Chaudhary, Anav Tiwari, Kshitij Bera, Aniket Robotics 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. |
| title | HEROES: Unreal Engine-based Human and Emergency Robot Operation Education System |
| topic | Robotics |
| url | https://arxiv.org/abs/2309.14508 |