For my 8-Week TGA school project, I built an RTS-style AI inspired
by
Age of Empires. The AI is driven by a
GOAP system and a custom planner that picks the
most effective actions based on the current world state.
Farmers follow these plans using
A*-influenced movement, avoiding enemies while
gathering resources. Soldiers respond to
enemy presence or patrol less protected areas.
Goblins wander randomly, targeting low-defense spots.
The world is grid-based with procedurally placed
resources, and all units share a simple
entity system. Visuals are handled with
sprite batching and a clean UI.
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Steering Behaviors, Behavior Tree and Pooling
For this a TGA project, I implemented enemy AI using
steering behaviors and a shared
blackboard system. Each enemy type responds to its
surroundings through
Seek, Arrival, Separation, Spread and
Object Avoidance.
Movement and behavior are reactive and data-driven. Enemies stay in
formation, avoid obstacles, and use
behavior trees for decicionmaking.
The system is highly tweakable via ImGui controls,
allowing full live adjustment of enemy speed, mass, and force, plus
preset ray layouts for precise steering response.
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Recast Integration, Grid Mapping, and Rendering
This system uses Recast Navigation to automatically
generate walkable areas based on level geometry, receiving a
Detailed & a less detailed
Polymesh Navmesh. The result is a clean and
optimized polygon mesh used for real-time
pathfinding.
I convert Recast data into a custom grid format,
enabling fast lookup of node centers and connections. This grid
structure is also used by AI behaviors like
edge steering to keep enemies aligned.
The entire navmesh is rendered visually, showing
both geometry and node relationships. It's fully customizable to
support create the best suited mesh for the AI use cases and map
layouts.
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