  Planning for legged-wheeled machines is typically done using trajectory
optimization because of many degrees of freedom, thus rendering legged-wheeled
planners prone to falling prey to bad local minima. We present a combined
sampling and optimization-based planning approach that can cope with
challenging terrain. The sampling-based stage computes whole-body
configurations and contact schedule, which speeds up the optimization
convergence. The optimization-based stage ensures that all the system
constraints, such as non-holonomic rolling constraints, are satisfied. The
evaluations show the importance of good initial guesses for optimization.
Furthermore, they suggest that terrain/collision (avoidance) constraints are
more challenging than the robot model's constraints. Lastly, we extend the
optimization to handle general terrain representations in the form of elevation
maps.
