  Robots are still poor at traversing cluttered large obstacles required for
important applications like search and rescue. By contrast, animals are
excellent at doing so, often using direct physical interaction with obstacles
rather than avoiding them. Here, towards understanding the dynamics of
cluttered obstacle traversal, we developed a minimalistic stochastic dynamics
simulation inspired by our recent study of insects traversing grass-like beams.
The 2-D model system consists of a forward self-propelled circular locomotor
translating on a frictionless level plane with a lateral random force and
interacting with two adjacent horizontal beams that form a gate. We found that
traversal probability increases monotonically with propulsive force, but first
increases then decreases with random force magnitude. For asymmetric beams with
different stiffness, traversal is more likely towards the side of the less
stiff beam. These observations are in accord with those expected from a
potential energy landscape approach. Furthermore, we extended the single gate
in a lattice configuration to form a large cluttered obstacle field. A Markov
chain Monte Carlo method was applied to predict traversal in the large field,
using the input-output probability map obtained from single gate simulations.
This method achieved high accuracy in predicting the statistical distribution
of the final location of the body within the obstacle field, while saving
computation time by a factor of 10^5 over our dynamic simulation.
