GNB-UAM/RoboSearcher

Simulation platform to study and optimize random search strategies

RoboSearcher

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.

Instructions

1. Modify the parameters in runSim.py and simulevy.py, adapt or uncomment the sections as needed.
2. Install the dependency libraries that are imported at the top of each file.
3. Execute the code with python3 runSim.py.

Note: We recommend Numpy Pickle (.npy) to export the desired parameters for their later representation.

Future work

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.

How to cite

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:

• 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.

• 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.