pathing

Pathing refers to the computation of a route from a start location to a goal within a space that may be represented as a graph, grid, or continuous environment. It is a core problem in robotics, computer games, simulation, and network routing. In practice, pathing is often divided into pathfinding (discrete graphs) and motion planning (continuous spaces with dynamic constraints).

Common approaches include graph search algorithms such as Dijkstra's algorithm and A*, BFS, DFS, which produce feasible, and sometimes optimal, paths given a representation of the space and obstacles. All-pairs methods like Floyd-Warshall exist but are less scalable. Heuristics guide A* to fast, near-optimal results. In continuous spaces, sampling-based planners such as Rapidly-exploring Random Trees and Probabilistic Roadmaps are used, along with potential fields. Path quality considerations include shortest distance, safety margins, and dynamic feasibility with velocity and acceleration limits (kinodynamic planning).

Representations used for pathing include grids, graphs of waypoints, and navigation meshes (navmeshes) that approximate free space for agents. After obtaining a route, post-processing such as path smoothing, turning-angle optimization, and local obstacle avoidance may improve realism and feasibility, especially for moving agents in crowded environments. In multi-agent settings, pathing must address coordination and collision avoidance, sometimes via prioritized planning, coupling constraints, or centralized solvers.

Challenges include dynamic environments requiring replanning, large-scale maps, real-time constraints, and differing agent capabilities. Applications range from game AI and autonomous robots to network routing and logistics planning. The field emphasizes correctness, efficiency, and robustness of pathing under uncertainty.