Optimal Path for Finding Randomly Distributed Targets

Samuel Norman

Abstract


Swarming technology is the next big improvement in robotic systems. Corporations, private researchers, and educational leaders are investing in this technology. The NASA Swarmathon is a competition based on the development and optimization of swarming in robots. The Swarmathon is centered around developing the search algorithms for these robots to find randomly distributed targets for planetary exploration purposes. This project is based in the NASA Swarmathon Robotic Operating System simulation environment, in which different search algorithms were tested. The basic algorithm in the Swarmathon to be improved upon used a Gaussian Distribution with a random number generator to determine a new heading for the rover. The proposed search algorithm is a chaotic system of equations created by Edward Lorenz to model hydrodynamic fluid flow. The specific structure of the system seemed promising because it does not repeat values but maintains a tight structure so it would cover an area thoroughly. Three target distribution methods were used. After 18 30 minute simulations were ran the average number of targets collected was greater with the Lorenz attractor path than the Random Walk in every target distribution method. These findings prove that finding randomly distributed targets is more efficient, and consistent with the Lorenz attractor path.


Keywords


Robotic Operating System, Lorenz Attractor, Chaos

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