Sounds like a classic combinatorial problem. Start by defining a clear state space: each unit’s position, orientation, and status. Then decide on the constraints—range of fire, line‑of‑sight, terrain penalties, supply limits, etc. Once you have that, pick an objective: minimize casualties, maximize coverage, or keep the supply line intact. For the optimization, a weighted cost function that aggregates all those penalties works well. You can model it as a graph where nodes are potential positions and edges encode movement or attack links. Then run a search or use a solver: A* if you have a good heuristic, or a genetic algorithm if the space is huge. If you want real‑time decision‑making in VR, a lightweight approximate method like a rule‑based planner with periodic re‑evaluation usually does the trick. Wrap everything in a modular layer: a physics engine for collisions, a path‑finding library for unit movement, and a separate constraint‑checker that you can tweak without touching the core logic. That keeps the chaos from blowing up the codebase, and you can iterate on the model while still seeing the VR output. Happy modeling!

Sounds solid—pre‑computing diagonals and caching LOS will shave a lot of runtime. Just make sure the fallback logic keeps the units in a sane state, otherwise you risk a quick “panic mode” that feels off. Keep the modules loosely coupled and you’ll have a smooth VR flow.