  Quadrupeds are strong candidates for navigating challenging environments
because of their agile and dynamic designs. This paper presents a methodology
that extends the range of exploration for quadrupedal robots by creating an
end-to-end navigation framework that exploits walking and jumping modes. To
obtain a dynamic jumping maneuver while avoiding obstacles,
dynamically-feasible trajectories are optimized offline through
collocation-based optimization where safety constraints are imposed. Such
optimization schematic allows the robot to jump through window-shaped obstacles
by considering both obstacles in the air and on the ground. The resulted
jumping mode is utilized in an autonomous navigation pipeline that leverages a
search-based global planner and a local planner to enable the robot to reach
the goal location by walking. A state machine together with a decision making
strategy allows the system to switch behaviors between walking around obstacles
or jumping through them. The proposed framework is experimentally deployed and
validated on a quadrupedal robot, a Mini Cheetah, to enable the robot to
autonomously navigate through an environment while avoiding obstacles and
jumping over a maximum height of 13 cm to pass through a window-shaped opening
in order to reach its goal.
