Unmanaged AGV charging creates a critical bottleneck, directly impacting warehouse throughput and labor costs. When robots deplete their batteries during peak picking waves, operations stall, and manual intervention is required to swap or recharge units, leading to missed SLAs and increased overtime. A custom multi-agent system automates this by ingesting real-time battery telemetry, forecasting demand from the WMS, and orchestrating charging sessions during predicted lulls. This predictive scheduling prevents robots from dropping offline when they are needed most, ensuring continuous material flow.




