2021/12/31 by Bokun Zheng, Qihan Xuan, Chen Li · 5 citations
Computer Science · Engineering · #Algorithm #Biomimetic flight and propulsion mechanisms #Computer science #Machine learning #Markov chain #Obstacle #Physics #Robotic Locomotion and Control #Robotic Path Planning Algorithms #Simulation #Statistical physics #Traverse #Tree traversal #cs.RO
paper · pdf · doi:10.1109/lra.2022.3150831
published in IEEE Robotics and Automation Letters 7(2), 5119-5126 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2022/02/14 · arxiv created 2022/03/12 · arxiv updated 2022/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 105 over our dynamic simulation.