  We extend the behaviour of generic sample-based motion planners to support
obstacle avoidance during long-range path following by introducing a new
edge-cost metric paired with a curvilinear planning space. The resulting
planner generates naturally smooth paths that avoid local obstacles while
minimizing lateral path deviation to best exploit prior terrain knowledge from
the reference path. In this adaptation, we explore the nuances of planning in
the curvilinear configuration space and describe a mechanism for natural
singularity handling to improve generality. We then shift our focus to the
trajectory generation problem, proposing a novel Model Predictive Control (MPC)
architecture to best exploit our path planner for improved obstacle avoidance.
Through rigorous field robotics trials over 5 km, we compare our approach to
the more common direct path-tracking MPC method and discuss the promise of
these techniques for reliable long-term autonomous operations.
