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Fleet Battery Management: Predictive Recharge vs Stranding

Two identical robot fleets work a floor of scattered tasks while their batteries drain. One fleet runs every robot until it dies mid-task and gets stranded; the other predicts each robot's remaining battery against its remaining task time, proactively sends it to recharge before it would strand, and reassigns its unfinished task to another robot. Compare live throughput and stranded incidents as fleet size and task load scale up.

Robotics & Kinematics3DModerate60 FPS

Every mobile robot in a fleet has a battery that drains as it works — the question is what happens when it gets low. This simulation runs two identical robot fleets on separate floors, each working the same kind of scattered task queue. One fleet keeps every robot on task until its battery physically hits zero, stranding it mid-job and losing whatever it was doing. The other continuously predicts each robot's remaining task time against its remaining charge, pulls it off the task before it would strand, hands the unfinished work to another robot, and routes it to recharge. Scale the fleet size and task load up and watch the two strategies diverge.

⚙ Under the hood

Two identical robot fleets work a floor of scattered tasks while their batteries drain. One fleet runs every robot until it dies mid-task and gets stranded, losing the task; the other predicts each robot's remaining task time against its remaining battery, proactively routes it to recharge before it would strand, and reassigns its unfinished task to another robot. Compare live throughput and stranded-incident counts as fleet size and task load scale up.

Fleet ManagementBattery-Aware SchedulingPredictive MaintenanceTask ReassignmentMulti-Robot Systems

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install