Vehicle Networks for Gradient Descent in a Sampled Environment
Ralf Bachmayer and Naomi Ehrich Leonard
41st IEEE Conference on Decision and Control,
2002
Fish in a school efficiently find the densest source of food by
individually responding not only to local environmental stimuli
but also to the behavior of nearest neighbors. It is of great
interest to enable a network of autonomous vehicles to function
similarly as an intelligent sensor array capable of climbing or
descending gradients of some spatially distributed signal. We
formulate and study a coordinated control strategy for a group of
autonomous vehicles to descend or climb an environmental gradient
using measurements of the environment together with relative
position measurements of nearest neighbors. Each vehicle is driven
by an estimate of the local environmental gradient together with
control forces, derived from artificial potentials, that maintain
uniformity in group geometry.