A minimalistic stochastic dynamics model of cluttered obstacle traversal

TL;DR

This paper introduces a minimalistic stochastic dynamics model inspired by insect obstacle traversal, demonstrating how simple physical interactions can predict robot navigation success in cluttered environments efficiently.

Contribution

The study presents a novel minimalistic 2-D stochastic model and a Markov chain Monte Carlo method to predict obstacle traversal, reducing computational costs significantly.

Findings

Traversal probability increases with propulsive force.

Optimal random force magnitude maximizes traversal likelihood.

The model accurately predicts traversal outcomes in complex obstacle fields.

Abstract

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.