OPEN ACCESS
A framework based on a mixed reality simulator for coordinating teams of autonomous Unmanned Aerial Vehicles (UAVs) is been developed. This framework would serve as a tool to facilitate crossing the reality gap for different applications; particularly when using these UAVs teams for air pollution monitoring and measurement. The system is built on a co-evolutionary simulator that makes use of data transmitted from some real UAVs to integrate them within a team of simulated UAVs. The system allows the progressive increase of the number of real UAV in the team. This facilitates the setting-up of a single UAV control system and also of the UAV collaboration schemes for different scenarios. A specific implementation of this system focussed on mapping the pollutant dispersion of a plume in the atmosphere is presented. Implementing an appropriate pollution dispersion model within the simulator is a key aspect of the system. This model should require few computational resources, should be easy to adapt in real time to ambient changes, and it should have a fair accuracy.
mixed reality, plume dispersion, unmanned aerial vehicles
[1] Jakobi, N., Husbands, P. & Harvey, I., Noise and the reality gap: The use of simulation in evolutionary robotics. Proceeding of the Third European Conference on Artificial Life, Springer-Verlag: London, UK, pp. 704–720, 1995.
[2] Nolfi, S. & Floreano, D., Evolutionary Robotics. MIT Press: Cambridge MA & London, p. 384, 2000.
[3] Varela, G., Caamaño, P., Orjales, F., Deibe, A., López Peña, F. & Duro, R.J., Autonomous UAV based search operations using constrained sampling evolutionary algorithms. Neurocomputing, 132, pp. 54–67, 2014.
[4] Perry, A.R., FlightGear flight simulator, available at http://www.flightgear.org/Papers/UseLinux-2004/fgfs.pdf
[5] Briggs, G.A., Diffusion estimation for small emissions. Technical report, air resources atmospheric turbulence and diffusion laboratory, NOAA, Oak Ridge, Tennessee, USA, 1974.
[6] Pasquill, F., The estimation of the dispersion of windborne material. The Meteorological Magazine, 90(1063), pp. 33–49. 1961.
[7] Varela, G., Caamano, P., Orjales, F., Deibe, A., Lopez-Pena, F. & Duro, R.J., Differential evolution in constrained sampling problems. Proceeding 2014 IEEE Congress on Evolutionary Computation (CEC), Piscataway, N.J., pp. 2375–2382, 2014.
[8] Storn, R. & Price, K., Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), pp. 341–359, 1997. http://dx.doi.org/10.1023/A:1008202821328
[9] Caamaño, P., Bellas, F., Becerra, J.A. & Duro, R.J., Evolutionary algorithm characterization in real parameter optimization problems. Applied Soft Computing, 13(4), pp. 1902–1921, 2013. http://dx.doi.org/10.1016/j.asoc.2013.01.002