  Navigating networked robot swarms often requires knowing where to go, sensing
the environment, and path-planning based on the destination and barriers in the
environment. Such a process is computationally intensive. Moreover, as the
network scales up, the computational load increases quadratically, or even
exponentially. Unlike these man-made systems, most biological systems scale
linearly in complexity. Furthermore, the scale of a biological swarm can even
enable collective intelligence. One example comes from observations of golden
shiner fish. Golden shiners naturally prefer darkness and school together. Each
individual golden shiner does not know where the darkness is. Neither does it
sense the light gradients in the environment. However, by moving together as a
school, they always end up in the shady area. We apply such collective
intelligence learned from golden shiner fish to navigating robot swarms. Each
individual robot's dynamic is based on the gold shiners' movement strategy---a
random walk with its speed modulated by the light intensity and its direction
affected by its neighbors. The theoretical analysis and simulation results show
that our method 1) promises to navigate a robot swarm with little situational
knowledge, 2) simplifies control and decision-making for each individual robot,
3) requires minimal or even no information exchange within the swarm, and 4) is
highly distributed, adaptive, and robust.
