Distributed optimization and control using only a germ of intelligence

Foraging can be modeled as an optimization process where an animal seeks to maximize energy obtained per unit time spent foraging. Search strategies form the basic foundation for foraging decisions. Here, the chemotactic behavior of E. coli, i.e., how it forages is explained and a computer program that emulates its foraging optimization process is presented and applied to solve a function minimization problem. Then, it is explained how biomimicry of bacterial foraging can be used to provide adaptive control strategies, and methods for distributed coordination and control of autonomous vehicles. Next, we endow our forager with higher cognitive functions (e.g., learning and planning) and discuss how this impacts coordination, control, and swarming behavior for autonomous vehicles. Foundations in optimization theory are discussed. Finally, we explain how to perform stability analysis of swarms thereby providing mathematical foundations for the study of social foraging.