Task allocation and traffic management for AMRs
A modern fulfillment warehouse doesn't run one robot, it runs dozens or hundreds of autonomous mobile robots (AMRs) simultaneously, and the hard problem shifts from controlling a single machine to coordinating a fleet. A central (or increasingly decentralized) fleet management system decides which robot handles which task based on proximity, battery level, current load, and deadline, constantly re-optimizing as new orders arrive and robots become available or busy. This is genuinely a logistics optimization problem, closer to airline scheduling than to classic robot motion control.
Traffic management adds another layer entirely: with that many robots sharing narrow warehouse aisles, the system has to prevent gridlock and collisions, sometimes by reserving path segments in advance the way a rail dispatcher reserves track blocks, sometimes through decentralized negotiation where nearby robots yield right-of-way to each other in real time. Get this wrong and the failure mode isn't subtle, robots queue up and jam entire aisles, which is exactly the kind of throughput-killing problem that makes fleet orchestration a genuine competitive advantage for companies like Amazon Robotics or Locus Robotics rather than a solved commodity.