GCRI · Advanced Industry and Robotics
Industrial robotics
Industrial robots weld, paint, assemble, pick and inspect, and collaborative robots now work beside people. Robot density has grown fastest in a few economies and sectors such as automotive and electronics. This page sets out how robots are deployed, how their safety is engineered and regulated, how adoption is measured, and what evidence serves the manufacturers, workers and regulators involved.
Industrial robotics on the record.
Industrial robotics: how the risk develops.
Robot sensing, safety and autonomy. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Robots suit repeatable tasks
Robots excel at precise, repetitive tasks in structured settings. Variable parts and unstructured environments still challenge them, though vision and AI are widening their range.
Safety comes from design and separation
Traditional robots work behind guards; collaborative robots limit force, speed and separation so people can work nearby, following safety standards.
Costs and integration set adoption
The robot itself is often a minority of total cost. Integration, programming and maintenance determine whether small firms can adopt.
Adoption is concentrated
Robot installations concentrate in a few countries and industries, shaping where productivity gains and labour effects fall.
Infrastructure exposure in Germany.
Night lights, population density and flood water over Germany, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Infrastructure exposure, for this page
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Infrastructure exposure, framed on Germany.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological Survey · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Live: U.S. Geological Survey Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (12 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
Robots bring safety and labour questions to every domain they enter.
Robotics findings are designed for the Labs Evidence series. Safety requirements follow ISO 10218 and ISO/TS 15066 for collaborative robots; installation statistics come from the International Federation of Robotics.
- Workplace robot incident data.
- Collaborative robot evaluations.
Plant operations and safety.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Plant operations and safety
Industrial robotics · Plant 3 · Shift 2
- OEE, % of planned762Watch
- Safety-critical alarms active2Alert
- Robot cell stops this shift8
- Energy per unit0.9kWhNormal
Barriers
layers of protection
| High-pressure trip | SIS | Q4 | Healthy |
| Relief valve RV-12 | Mechanical | Q2 | Healthy |
| Gas detection zone 4 | Detection | Q2 | Bypassed |
| Robot cell light curtain | Safeguard | Q2 | Healthy |
Reactor pressure
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Robot density | operational robots per ten thousand manufacturing employees | annual | International Federation of Robotics statistics | REPORTED |
| Annual robot installations | units by sector | annual | Industry statistics | REPORTED |
| Collaborative robot share of installations | percent | annual | Industry statistics | REPORTED |
| Robot-related workplace injuries | count | annual | Occupational injury statistics | Published statistics |
| Firms using robots | percent of firms by size | per survey | Business surveys | REPORTED |
| Robot imports | value | annual | Trade statistics | Published statistics |
Model industrial robotics as a system. Run the cascade.
Assembled from the Nexus Standards area profile Industry and manufacturing: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Natech event
- Dam failure
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Chemical spill or leak (set off downstream)
- Emergency-response overload (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Toxic gas release (set off downstream)
- Industrial fire (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)NRS-H50Hazard family: Industrial and process accidents (NHS-1 infra.industrial)NRS-H109Hazard family: Energy-transition technology failures (NHS-1 tech.energy-transition)NRS-X10Chemical, industrial, nuclear and radiological safetyNRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-K14Domain pack profile: Manufacturing industrial (DOMAIN-014)
Observation
- eMARS Major Accident Reporting System (listed for reference)
- World Development Indicators (listed for reference)
- ILOSTAT (listed for reference)
- Manufacturing value added as a proportion of GDP and per capita
- CO2 emission per unit of value added
- Manufacturing, value added (% of GDP)
- Direct economic loss to all other damaged or destroyed productive assets attributed to disasters
Example geography
- The Strait of Malacca: 227 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| International Federation of Robotics World Robotics reports | Installations and density by country and sector | Commercial report; public summaries (to check) |
| Eurostat enterprise robot use statistics | Share of firms using industrial and service robots | Open, Eurostat reuse policy |
| National occupational injury statistics | Injuries involving machinery and robots | Public records, licence per government |
| UN Comtrade robot trade (HS 847950) | Trade in industrial robots | Free access with limits, UN terms |
| ISO 10218 and ISO/TS 15066 | Robot safety requirements | Licensed standards, purchase required |
How industrial robotics is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in industrial robotics: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Who decides, and what the evidence serves.
Advanced Industry and Robotics prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Manufacturers | Where robots add value and how to deploy them safely | Integration cost analyses; safety reviews |
| Workplace safety regulators | How to oversee robot and cobot applications | Injury and safety practice evidence |
| Industrial and labour ministries | How to support adoption and workforce transition | Adoption and labour evidence |
Outputs this topic produces
- Robot adoption profiles by country, sector and firm size
- Safety practice reviews for collaborative applications
- Integration cost analyses for small firms
- Evidence notes on labour effects
What experts ask.
- 01
How do robots perceive and decide?
The sensors, models and control loops that let a robot act, and how they fail.
- 02
How are people protected?
Guarding, speed and force limits, safety-rated sensing and the design of shared workspaces.
- 03
What changes with autonomy?
How learning-based control and mobile robots affect predictability and verification.
- 04
Who is accountable?
How responsibility is divided between manufacturer, integrator and user.
What drives the risk.
Traditional industrial robots are fast and powerful and have been kept behind fences. Collaborative robots are designed to share space with people by limiting speed, force and power, or by stopping when a person approaches. Their safety depends on correct risk assessment of the whole application, not the robot alone.
Perception is a frequent source of failure. Lighting, reflections, dust and unusual objects can confuse sensors, and learning-based perception may behave unpredictably outside its training conditions.
Mobile robots in warehouses and plants move among people and vehicles, introducing traffic risks. Verification becomes harder as behaviour becomes more adaptive, because exhaustive testing is not possible.
Integration matters as much as design. Most robot incidents occur during maintenance, programming or abnormal operation, when safeguards are bypassed or people enter the work envelope.
How evidence is gathered, and where it stops.
| Method or data | What it shows | Its limit |
|---|---|---|
| Application risk assessment | Hazards of a specific robot task | Quality depends on assessor competence |
| Safety-rated testing | Sensor and stop performance | Tests cannot cover every condition |
| Simulation | Behaviour in varied scenarios | Simulation-to-reality gap |
| Incident analysis | Causes of robot-related injuries | Small and incomplete datasets |
How it is studied, and what the methods cannot say.
- 01Robotics and automationRobot cell risk assessment · Vision-guided handling · Collaborative workstation designApplied here
- 02Systems engineeringIntegrated safety and security requirements · Factory and site acceptance testingApplied here
- 03Sensors, IoT and observation networksCondition monitoring · In-line quality inspection · Securing connected sensorsApplied here
- 04Optimisation and operations researchProduction scheduling · Energy-aware operations · Buffer and spare sizingApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What is robot density?
What is a collaborative robot?
Do robots reduce jobs?
Are collaborative robots inherently safe?
Does the platform test robots?
Why are maintenance tasks risky?
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