  We present MLNav, a learning-enhanced path planning framework for
safety-critical and resource-limited systems operating in complex environments,
such as rovers navigating on Mars. MLNav makes judicious use of machine
learning to enhance the efficiency of path planning while fully respecting
safety constraints. In particular, the dominant computational cost in such
safety-critical settings is running a model-based safety checker on the
proposed paths. Our learned search heuristic can simultaneously predict the
feasibility for all path options in a single run, and the model-based safety
checker is only invoked on the top-scoring paths. We validate in high-fidelity
simulations using both real Martian terrain data collected by the Perseverance
rover, as well as a suite of challenging synthetic terrains. Our experiments
show that: (i) compared to the baseline ENav path planner on board the
Perserverance rover, MLNav can provide a significant improvement in multiple
key metrics, such as a 10x reduction in collision checks when navigating real
Martian terrains, despite being trained with synthetic terrains; and (ii) MLNav
can successfully navigate highly challenging terrains where the baseline ENav
fails to find a feasible path before timing out.
