  Quadruped robots have the distinct ability to adapt their body and step
height to navigate through cluttered environments. Nonetheless, for these
robots to utilize their full potential in real-world scenarios, they require
awareness of their environment and obstacle geometry. We propose a novel
multi-agent robotic system that incorporates cutting-edge technologies. The
proposed solution features a 3D neural reconstruction algorithm that enables
navigation of a quadruped robot in both static and semi-static environments.
The prior areas of the environment are also segmented according to the
quadruped robots' abilities to pass them. Moreover, we have developed an
adaptive neural field optimal motion planner (ANFOMP) that considers both
collision probability and obstacle height in 2D space.Our new navigation and
mapping approach enables quadruped robots to adjust their height and behavior
to navigate under arches and push through obstacles with smaller dimensions.
The multi-agent mapping operation has proven to be highly accurate, with an
obstacle reconstruction precision of 82%. Moreover, the quadruped robot can
navigate with 3D obstacle information and the ANFOMP system, resulting in a
33.3% reduction in path length and a 70% reduction in navigation time.
