  In this paper, we present an approach for dynamic exploration and mapping of
unknown environments using a swarm of biobotic sensing agents, with a
stochastic natural motion model and a leading agent (e.g., an unmanned aerial
vehicle). The proposed robust mapping technique constructs a topological map of
the environment using only encounter information from the swarm. A sliding
window strategy is adopted in conjunction with a topological mapping strategy
based on local interactions among the swarm in a coordinate-free fashion to
obtain local maps of the environment. These maps are then merged into a global
topological map which can be visualized using a graphical representation that
integrates geometric as well as topological feature of the environment.
Localized robust topological features are extracted using tools from
topological data analysis. Simulation results have been presented to illustrate
and verify the correctness of our dynamic mapping algorithm.
