  Quadruped robots have received increasing attention for the past few years.
However, existing works primarily focus on static environments or assume the
robot has full observations of the environment. This limits their practical
applications since real-world environments are often dynamic and partially
observable. To tackle these issues, vision-based reactive planning and control
(V-RPC) is developed in this work. The V-RPC comprises two modules: offline
pre-planning and online reactive planning. The pre-planning phase generates a
reference trajectory over continuous workspace via sampling-based methods using
prior environmental knowledge, given an LTL specification. The online reactive
module dynamically adjusts the reference trajectory and control based on the
robot's real-time visual perception to adapt to environmental changes.
