  Inspired by human behavior when traveling over unknown terrain, this study
proposes the use of probing strategies and integrates them into a
traversability analysis framework to address safe navigation on unknown rough
terrain. Our framework integrates collapsibility information into our existing
traversability analysis, as vision and geometric information alone could be
misled by unpredictable non-rigid terrains such as soft soil, bush area, or
water puddles. With the new traversability analysis framework, our robot has a
more comprehensive assessment of unpredictable terrain, which is critical for
its safety in outdoor environments. The pipeline first identifies the terrain's
geometric and semantic properties using an RGB-D camera and desired probing
locations on questionable terrains. These regions are probed using a force
sensor to determine the risk of terrain collapsing when the robot steps over
it. This risk is formulated as a collapsibility metric, which estimates an
unpredictable region's ground collapsibility. Thereafter, the collapsibility
metric, together with geometric and semantic spatial data, is combined and
analyzed to produce global and local traversability grid maps. These
traversability grid maps tell the robot whether it is safe to step over
different regions of the map. The grid maps are then utilized to generate
optimal paths for the robot to safely navigate to its goal. Our approach has
been successfully verified on a quadrupedal robot in both simulation and
real-world experiments.
