All Foundries

Isometric blue-and-white model of an energy and logistics network with a docked cargo ship, solar panels, factories, office buildings, pipes, trucks, workers, and stacked materials.

商品管理と取引

データに基づくインサイトと、セキュアで機動性の高いプラットフォームにより、グローバルな商取引を支えるトレーディングエコシステムを変革します。

Isometric illustration of a bright modern office with glass meeting rooms, people working at desks, large data dashboards, presentation screens, plants, and studio lights.

顧客とマーケティング戦略

パーソナライズされたつながりある体験を通じて顧客エンゲージメントを再構築し、信頼と長期的な価値を築きます。

Isometric 3D smart city model with white-blue buildings, cranes, power grids, map pins, and people using tablets around connected infrastructure platforms.

デジタルエンジニアリング

アイデアをスケーラブルなデジタルソリューションへと転換し、デザイン・テクノロジー・エンジニアリングの力で加速させます。

Isometric 3D model of a white-and-blue EV charging station with canopy, office buildings, small cars, delivery van, chargers, planters, and tiny people.

ステーション・スタジオ

デジタルイノベーションでモビリティハブを再定義し、移動中の人々と企業に、よりスマートでサステナブルな体験を創出します。

Isometric 3D logistics campus with white modern warehouses, blue-green accents, workers, delivery vans, a loading dock, and stacked shipping containers.

サプライチェーン＆インダストリー5.0

変化を予測し、目的を持って効率性を実現する、強靭で知的な供給ネットワークを構築します。

White-and-blue sustainable campus with solar panels, a wind turbine, EV charging stations, loading dock trucks, trees, and small groups of people.

サステナビリティ

責任あるイノベーションを推進し、組織がネットゼロ目標を達成し、将来の世代に良い影響をもたらすよう応援します。

Isometric smart factory scene with white buildings, robotic arms on assembly lines, drones, autonomous carts, workers, control screens, and a fenced power substation connected by blue paths.

Physical AI

Physical AI gives machines, robots and physical operations the ability to perceive their environment, decide what matters and act in real time. Factories, plants, stores and infrastructure become systems able to sense conditions, learn from them and improve on their own, closing the gap between the physical and the digital world.

Isometric 3D smart infrastructure scene with white roads and buildings, blue data lines linking control rooms, vans, a rail crossing, drone, workers, and trees.

Next-Gen Infrastructure

From networks as infrastructure to connected platforms that enable critical, autonomous and intelligent operations.

AGV Simulation and Path Optimization

An AGV fleet runs smoothly until a pallet blocks an aisle or a new mission arrives mid-route. NTT DATA built a digital twin in NVIDIA Isaac Sim where LiDAR and cameras detect obstacles, NVIDIA cuOpt recalculates paths without the blocked segments, and a simulated MES/WMS creates and assigns missions with no manual scheduling.

AGV Simulation and Path Optimization

Smart Robotics for Data Center Automation

Data centers depend on skilled staff to read meters, check lamps and notice odd appearances or strange odors, and those people are getting harder to find. NTT DATA put autonomous patrol robots with cameras and sensors on these routine rounds, which used to take skilled workers 1 to 2 hours every day.

Smart Robotics for Data Center Automation

お問い合わせ

AGV Simulation and Path Optimization

Route planning: Real time

CuOpt recalculates optimized paths each time sensors report a blocked segment.

Dispatching: Automated

Missions come from a simulated MES/WMS and go straight to the vehicles.

Obstacle detection: LiDAR and cameras

Sensors update route availability while AGVs move.

Fleet view: KPI dashboard

Live routes, status, wear, power consumption and completed missions per vehicle.

AGV Simulation and Path Optimization

GV fleets ready for a floor in constant change

目的

Coordinate AGVs in a dynamic layout so they handle obstacles, route disruptions and new missions safely, with full visibility of the system.

機会

A digital twin gives teams a place to test routing and dispatching logic before anything changes on the real floor. Connected to MES/WMS missions, the optimization works inside daily operations instead of a separate pilot.

Layouts change faster than plans

Industrial sites rely more each year on AGVs for flexible material handling, while their layouts keep shifting. Every obstacle or new order forces a reroute, and manual scheduling falls behind.

主な課題

1: Obstacles and disruptions. Blocked aisles and route changes interrupt planned paths.

2: Shifting mission demand. New orders arrive while vehicles are already on the move.

3: Manual scheduling. Planners assign and sequence missions by hand, and the fleet waits for them.

4: Limited fleet view. Teams had no live picture of routes, status and performance.

解決策

Sensing, optimization and dispatching in one digital twin

NTT DATA integrated real-time sensing, dynamic optimization and automated dispatching within NVIDIA Isaac Sim. The architecture also uses NVIDIA Omniverse, USD, AnyLogic and MQTT.

Sensor-based obstacle detection

LiDAR and camera sensors detect obstacles and mark which route segments stay open.

Path recalculation with cuOpt

NVIDIA cuOpt recalculates optimized paths and excludes the blocked segments.

utomated mission dispatching

A simulated MES/WMS generates missions and assigns each one to a vehicle.

Live routes and KPIs

Planned and optimized routes appear in real time, next to a basic KPI dashboard for each AGV.

影響

A fleet able to react on its own

The scalable digital-twin architecture optimizes and coordinates AGVs in real time. Dynamic routing and automated dispatching cut delays and manual planning. Vehicles react to obstacles as soon as sensors detect them, and optimized routes reduce movements, energy use and resource waste. Every decision runs on the site’s own mission data and business rules.

ドラッグ