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.

Robotics Cloud Platform

Manufacturers often run robots from several providers, and every brand demands its own specialist skills. NTT DATA built the Robotics Cloud Platform, an agnostic control layer for robotic arms independent of proprietary systems. ROS and a digital twin simulate each movement, and AI adds new skills before a robot reaches the production line.

Robotics Cloud Platform

Multi-UAV system for autonomous inspection

Critical infrastructure and private areas need constant watching, and fixed surveillance leaves gaps across large sites. NTT DATA applied artificial intelligence to build a multi-UAV system for autonomous patrolling. Reinforcement learning optimizes every flight path, and computer vision designed for bad weather keeps inspection results reliable.

Multi-UAV system for autonomous inspection

お問い合わせ

Multi-UAV system for autonomous inspection

Flight planning: Reinforcement learning

Drones optimize their paths against several objectives at once.

Patrol coverage: Complete

Routes aim to cover the entire assigned area.

Fleet links: Robust

Optimization keeps drones in contact during coordinated missions.

Inspection conditions: Rain, fog and changing light

Computer vision analysis holds up in poor weather.

Cooperative Networked UAVs

Faster, more accurate surveillance for critical infrastructure

目的

Improve the responsiveness and accuracy of surveillance, protect infrastructure integrity and reduce security risks in private areas.

機会

Autonomous drones report on the condition and security of essential assets in real time, and the same system adapts to patrols in other domains.

Large sites, fixed cameras

A fixed camera watches one point. Critical infrastructure spreads across wide areas where weather, light and threats keep changing, and patrols on foot or by car cover only part of the ground.

主な課題

1: Coverage gaps. Fixed surveillance leaves parts of large sites unwatched.

2: Competing objectives. Each mission must cover the whole area and keep the drones connected to each other.

3: Weather and lighting. Rain, fog and shifting light degrade visual inspection.

4: Response time. Security teams need detection and threat assessment as events unfold.

解決策

A cooperative fleet guided by reinforcement learning

NTT DATA designed a fleet of drones which optimize their flight paths with reinforcement learning (RL), combined with weather-resilient computer vision. The system runs on Python, ROS and Docker.

Multi-UAV control framework

A learning engine and a control interface manage the drones as one system, each running a programmable protocol stack.

RL-based flight optimization

The RL model adjusts each route to make the mission more effective, starting with complete area coverage during patrols.

Communication-aware routing

The same optimization protects the links between drones so they operate in coordination, with the fleet ready for next-generation networks such as IOWN.

Weather-resilient computer vision

Inspection algorithms analyze visual data accurately in rain, fog or changing light.

影響

Patrols with live detection and threat assessment

Security teams see threats as the drones detect them, with an assessment attached. RL-driven flight optimization and weather-resistant computer vision give operators a robust, adaptable option for autonomous inspection and surveillance. Every image and inspection record stays under the infrastructure operator’s control.

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