Дослідження Case · AerospaceАнонімісія за запитом. Назволені посилання на NDA.
Manufacturing · Robotics

From managing robots to optimising an intelligent fleet.

A manufacturer running AMRs and AGVs had limited visibility into route congestion, charging downtime, idle fleet utilisation and cross-zone movement inefficiencies.

Manufacturing · RoboticsAMR / AGV fleet intelligenceHybrid RTLS / RFID + analytics · measured outcomes.live asset positionHigherAMR/AGV utilisationМалиidle charging time
Завдання

Що вони були проти.

Route congestion

Robotic traffic created congestion hotspots across zones.

Idle & charging downtime

Fleet utilisation and charging were unmanaged.

No fleet-level view

Robots were managed as individual machines, not a fleet.

Наш підхід

Виробник-невтраль, результат-пол.

UWB for real-time robotic positioning, BLE for asset-interaction tracking, AGV/AMR telemetry for route analytics, and AI orchestration for congestion, idle-time and predictive movement optimisation.

Як ми вирішили

Що ми доставимо.

  • UWB RTLS real-time robotic positioning
  • BLE asset-interaction tracking
  • AGV/AMR telemetry route analytics
  • AI fleet orchestration — congestion, idle, predictive optimisation
Результати

Результат пилки дошки.

Higher
AMR/AGV utilisation
Мали
idle charging time
Сейф
robotic traffic
Higher
throughput
“We stopped managing robots as individual machines and started optimising them as an intelligent fleet.”

— Director of operations, industrial manufacturing (client anonymised)

Технології

Стек за ним

UWBBLEAGV/AMR telemetryAI orchestration
Чи можливо це ваша програма?

Скажіть, де ви застрягнете.

Щільність хвилин на вашому пристрої, номери, і що насправді буде перемістити KPIs . Виробник-невтраль, без платформи.

Поговорити з радником