PathfindingAI

C++20 · SDL2 · Algorithms · Behaviour Trees

Project Overview

PathfindingAI is an interactive pathfinding visualizer with autonomous agent simulation. Watch classic graph search algorithms expand across a maze in real time, or switch to AI Agents mode where behaviour-tree-driven entities pick random goals and navigate using the same pathfinders.

Built with SDL2 and Dear ImGui — dependencies are fetched automatically via CMake FetchContent, so no manual library setup is required.

Key Features

Visualizer mode — BFS, DFS, A*, and Jump Point Search with step-by-step frontier animation
Procedural mazes — recursive backtracker generation; draw and erase walls with the mouse
AI Agents mode — three agents wander a shared maze using behaviour trees
Live BT debug — ImGui panel shows node status (Success / Running / Failure)
Runtime algorithm swap — change pathfinder without restarting

Architecture

All pathfinders implement a common IPathfinder interface returning paths, explored cells, and timing data. The grid supports 8-connected neighbours. Agents use a selector-based behaviour tree (wait at goal → follow path → find new goal) with a blackboard pattern similar to game engines like Unreal.

My Role

Solo developer. I was responsible for:

Implementing BFS, DFS, A*, and Jump Point Search
Building the grid, maze generator, and visualizer UI
Designing behaviour trees and autonomous agent logic
Integrating SDL2 rendering and Dear ImGui controls
Setting up CMake FetchContent for zero-install dependencies

Technologies Used

C++20 SDL2 Dear ImGui CMake Behaviour Trees A* / JPS

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Repository

Synced from PathfindingAI — click folder names or ▶ arrows to expand. Use Expand all / Collapse all in the toolbar.