Warehouse AMR fleets hit a congestion ceiling at ~80% grid capacity, causing nonlinear throughput collapse as robot count increases

When warehouse autonomous mobile robot (AMR) fleets exceed approximately 80% of grid capacity, adding more robots actually decreases overall system throughput because decentralized path-planning algorithms produce cascading deadlocks and oscillating route conflicts. Amazon discovered this when scaling beyond 4,000 robots per fulfillment-center floor: throughput initially rose linearly with robot count, then plateaued and declined as robots began interfering with each other in aisle intersections and pod-storage zones. Why it matters: AMR fleets cannot fill the grid beyond ~80% capacity, so warehouse operators must over-provision floor space by 20%+ to avoid congestion, so fulfillment centers require larger and more expensive real estate footprints than necessary, so the per-unit cost of robotic picking stays stubbornly high and erodes the ROI case for automation, so warehouse operators delay further AMR deployments and revert to manual labor for peak-season surges, so the entire promise of lights-out warehouse automation remains unfulfilled despite over 750,000 Amazon robots already deployed. The structural root cause is that centralized fleet planners cannot solve the multi-agent path-finding (MAPF) problem optimally in real time for thousands of agents on a shared grid, while decentralized planners lack global visibility and produce locally optimal but globally conflicting routes, and no hybrid architecture has yet achieved both the scalability of decentralized planning and the deadlock-freedom guarantees of centralized planning at fleet sizes above ~800 units.