Simulation platform to study and optimize random search strategies. This is supplementary material for our article:
C. Garcia-Saura, E. Serrano, F.B. Rodriguez, P. Varona. 2021. Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty. Scientific Reports 11: 24509.
- Modify the parameters in
runSim.pyandsimulevy.py, adapt or uncomment the sections as needed. - Install the dependency libraries that are imported at the top of each file.
- Execute the code with
python3 runSim.py.
Note: We recommend Numpy Pickle (.npy) to export the desired parameters for their later representation.
Next step would be to simplify the setup of the simulations by implementing a command line interface or a visual GUI. Another option would be to integrate this into a standard library that can be used within other applications.
Upon use of this software please remember to cite the following publication:
- C. Garcia-Saura, E. Serrano, F.B. Rodriguez, P. Varona. 2021. Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty. Scientific Reports 11: 24509.
Other relevant publications:
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C. Garcia-Saura, E. Serrano, F.B. Rodriguez, P. Varona. 2017. Effects of Locomotive Drift in Scale-Invariant Robotic Search Strategies. Lect Notes in Comput. Sc 10384: 161-169.
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C. Garcia-Saura, F.B. Rodriguez, P. Varona. 2014. Design Principles for Cooperative Robots with Uncertainty-Aware and Resource-Wise Adaptive Behavior. Lect Notes in Comput. Sc 8608: 108-117.