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.

Commodity Management & Trading

Transforming trading ecosystems through data-driven insights and secure, agile platforms that empower global commerce.

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Customer & Marketing Strategy

Reimagining customer engagement with personalized, connected experiences that build trust and long-term value.

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Digital Engineering

Turning ideas into scalable digital solutions—accelerating transformation through design, technology, and engineering excellence.

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Station Studio

Redefining mobility hubs with digital innovation to create smarter, sustainable experiences for people and businesses on the move.

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

Supply Chain & Industry 5.0

Building resilient, intelligent supply networks that anticipate change and deliver efficiency with purpose.

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

Sustainability

Driving responsible innovation to help organizations achieve net-zero goals and create a positive impact for future generations.

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.

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Next-Gen Infrastructure

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

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

Cooperative networked UAVs for 5G coverage extension

Fixed base stations decide where 5G reaches. Users at remote sites, at temporary events or on the move often fall outside their range. NTT DATA built a control platform for a fleet of drones acting as IAB (Integrated Access and Backhaul) nodes: a ground donor station supplies the signal, and the drones reposition on their own to keep users connected.

Cooperative networked UAVs for 5G coverage extension

Contact

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

Objectives

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

Opportunity

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.

Key Challenges

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.

Solution

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.

Impact

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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