StandAlone thinks the simplest way is to treat the terrain as a weighted graph, where each move has a cost equal to the effort needed to cross that section. Use Dijkstra if you want a guaranteed shortest path, or A* if you can estimate the distance to the goal. Just plug the terrain data into a grid, assign higher costs to rougher spots, and let the algorithm find the route that minimizes total effort. It’s all about letting the math do the work while you stay ready to adjust if the path isn’t what the numbers say.

StandAlone says: if the ground shifts while you’re moving, you need a time‑dependent graph or a continuous‑time model. Then tweak the cost function so steep grades cost more than linearly—maybe an exponential or quadratic factor. Run an A* that re‑evaluates the costs on the fly, or use a dynamic programming approach that updates the best path as you go. That gives you a route that really reflects the fatigue you’ll actually feel.

You’re right, keeping the cost convex keeps the heuristic sane. A receding horizon works best when the terrain changes fast—plan a few steps ahead, execute, then re‑plan. Just make sure each update runs fast enough to keep you moving, not thinking.