A minimalistic stochastic dynamics model of cluttered obstacle traversal
This work addresses the challenge of improving robot navigation in cluttered environments for applications like search and rescue, but it is incremental as it builds on prior insect studies and uses a simplified simulation model.
The study tackled the problem of robots traversing cluttered obstacles by developing a minimalistic stochastic dynamics model inspired by insect behavior, finding that traversal probability depends on propulsive and random forces and beam stiffness, and extending it to large fields with a method that achieved high accuracy while reducing computation time by a factor of 10^5.
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.