User Perceive Realism of Machine Learning-based Drone Dynamic Simulator

The drone will be a commonly used technology by a significant portion of society, and simulating a given drone dynamic will be an essential requirement. There are drone dy-namic simulation models to simulate popular commercial drones. In addition, there are many Newtonian and fluid dynamics-based generic drone dynamic models. However, these models consist of many model parameters, and it is impracticable to evaluate the required model parameters to simulate a custom-made drone. A simple method to develop a machine learning-based dynamic drone simulation model to simulate custom- made drones mitigates the issues mentioned above. Specifically, the authors’ research is associated with the development of a machine learning-based drone dynamic model integrated with a virtual reality environment and validation of the user-perceived physical and behavioural realism of the entire solution. A figure of eight manoeuvring patterns was used to collect the data related to drone behaviour and drone pilot inputs. A Neural Network-based approach was employed to develop the machine learning-based drone dynamic model. Validations were done against real-world drone manoeuvres and user tests. Validation results show that the simulations provided by machine learning are accurate at the beginning and it decreases the accuracy with time. However, users also make mistakes/misjudgments while perceiving the real-world or virtual world. Hence, we explored the user perceive motion prediction accuracy of the simulation environment which is associated with the behavioural realism of the simulation environment. User tests show that the entire simulation environment maintains substantial physical realism.

@article{Sandaruwan2023,
  title     = {User Perceive Realism of Machine Learning-based Drone Dynamic Simulator},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {1},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Damitha Sandaruwan and Nihal Kodikara and Piyumi Radeeshani and K.T.Y. Mahima and Chathura Suduwella and Sachintha Pitigala and Mangalika Jayasundara},
  doi       = {10.14569/IJACSA.2023.0140196},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140196}
}