Multi-UAV Simulator
The purpose of the Multi-UAV (unmanned aerial vehicle) simulator was to create an environment where an individual could practice controlling multiple drones at once.
Summary
Students at the GRILL® worked to create a simulated environment allowing users to control 4 independent UAVs which recognized and differentiated enemies from friends.
This 2020 project included integrating validated surveys, such as the NASA-TLX a workload index, and realistic distractors within the environment.
Features and Design
Software
Unreal Engine 4
Blender
Quixel Megascans
Quixel Bridge
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Contributions
Team:
Ashton Tucker
Akhilesh Prasad
Sidhanth Verma
Mentor:
Mr. Jack Hu
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Clients:
ATLAS Lab
Dr. Summer Rebensky
Outcomes and Additional Information
The challenge included UAVs flying pre-programmed paths, each covering a quadrant of the map, and utilizing integrated surveys to record data.
The final product featured fully functional levels with four independent UAVs in a unique custom map and the ability to recognize friend from foe.
Simulator design allows it to have high cognitive fidelity while also allowing for flexibility.
Publications related to this effort:
Carmody, K., Ficke, C., Nyguyen, D., Addis, A., Rebensky, S., & Carroll, M. (2022). A qualitative analysis of trust dynamics in human-agent teams (HATS). Human Factors and Ergonomics Society (HFES) conference proceedings.
Rebensky, S., Nguyen, D., Carmody, K., Ficke, C., Carroll, M., & Bennett, W. (2021). There’s no “I” in HAT: Identifying appropriate skills for human agent teaming of varying levels of autonomy and embodiment. 2021 Interservice/Industry Training, Simulation, and Education Conference. https://www.xcdsystem.com/iitsec/proceedings/index.cfm?Year=2021&AbID=97076&CID=862#View
Rebensky, S. Carmody, K., Ficke, C., Carroll, M., & Bennet, W. (2022). Teammates instead of tools: The impacts of level of autonomy on mission performance and human-agent teaming dynamics in multi-agent distributed teams. Frontiers Robotics & AI – Ethics in Robotics and Artificial Intelligence, 9. https://doi.org/10.3389/frobt.2022.782134