  For autonomous quadruped robot navigation in various complex environments, a
typical SOTA system is composed of four main modules -- mapper, global planner,
local planner, and command-tracking controller -- in a hierarchical manner. In
this paper, we build a robust and safe local planner which is designed to
generate a velocity plan to track a coarsely planned path from the global
planner. Previous works used waypoint-based methods (e.g.
Proportional-Differential control and pure pursuit) which simplify the path
tracking problem to local point-goal navigation. However, they suffer from
frequent collisions in geometrically complex and narrow environments because of
two reasons; the global planner uses a coarse and inaccurate model and the
local planner is unable to track the global plan sufficiently well. Currently,
deep learning methods are an appealing alternative because they can learn
safety and path feasibility from experience more accurately. However, existing
deep learning methods are not capable of planning for a long horizon. In this
work, we propose a learning-based fully autonomous navigation framework
composed of three innovative elements: a learned forward dynamics model (FDM),
an online sampling-based model-predictive controller, and an informed
trajectory sampler (ITS). Using our framework, a quadruped robot can
autonomously navigate in various complex environments without a collision and
generate a smoother command plan compared to the baseline method. Furthermore,
our method can reactively handle unexpected obstacles on the planned path and
avoid them. Project page
https://awesomericky.github.io/projects/FDM_ITS_navigation/.
