Optimotaxis: A Stochastic Multi-agent Optimization Procedure with Point Measurements

Authors:
Alexandre R. Mesquita;João P. Hespanha;Karl Åström
Affiliations:
Center for Control, Dynamical Systems and Computation, University of California, Santa Barbara, CA 93106;Center for Control, Dynamical Systems and Computation, University of California, Santa Barbara, CA 93106;Center for Control, Dynamical Systems and Computation, University of California, Santa Barbara, CA 93106
Venue:
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Year:
2008

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Abstract

We consider the problem of seeking the maximum of a scalar signal using a swarm of autonomous vehicles equipped with sensors that can take point measurements of the signal. Vehicles are not able to measure their current position or to communicate with each other. Our approach induces the vehicles to perform a biased random walk inspired by bacterial chemotaxis and controlled by a stochastic hybrid automaton. With such a controller, it is shown that the positions of the vehicles evolve towards a probability density that is a specified function of the spatial profile of the measured signal, granting higher vehicle densities near the signal maxima.