Multi-Agent Pathfinding
Without Per-Agent Cost

The Core Problem

Traditional multi-agent pathfinding works like this: for each agent, compute a path, then avoid other agents, then recompute when blocked. This creates exponential CPU cost, collision complexity, and unstable frame times. At scale, the system becomes the bottleneck.

Why A* Fails for Multi-Agent Systems

A* is designed for single-agent optimization. In multi-agent environments, agents interfere with each other, paths become invalid constantly, and replanning is required every tick. Standard mitigation — RVO (Reciprocal Velocity Obstacles) — treats each agent pair independently, creating jittering, deadlocks, and corridor congestion.

The StrataNav Approach

StrataNav eliminates per-agent solving. A navigation field is precomputed once. All agents query the same field. Movement is coordinated by the field structure itself — agents naturally distribute without per-pair collision logic.

Agents don't solve paths. They follow the field.

Coordinated Movement

StrataNav's multi-agent coordination provides:

→ Smooth group movement without jittering
→ Natural distribution at doorways and corridors
→ No deadlocks from mutual blocking
→ Stable behavior under load — no degradation as agent count increases

Performance at Scale

With 100+ agents: stable frame times. No pathfinding spikes. Near-zero replanning cost. Performance scales with queries, not agents.

Use Cases

Multi-agent navigation is critical for RTS games (large armies), tower defense (waves of units), MOBAs (continuous team movement), crowd simulation, and warehouse/robotics simulation.