Versatile AI Risk Assessment
Robotics and vehicles

Autonomous mobile robots in intralogistics

Also known as: Autonomous mobile robots (warehouse robotics)

Robots independently move goods around a warehouse or accompany staff picking orders; a controller allocates orders and plans routes.

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A concise PDF covering how the system works, its data flows and points to investigate, ready to share.

The explanations and measures describe the typical system design. Extensive personal changes add an appendix.

Understand the use case

What defines this use case?

Each robot navigates using cameras, lasers and other sensors and avoids obstacles. Amazon and DHL Supply Chain use these robots in their warehouses.

This page describes a typical system design. Actual data flows and permissions depend on your implementation.

System design
Analysis and processing
Ability to act
Acts on physical processes
Fields of use
logistics, online retail
Also known as
AMR, warehouse robotics, order-picking robot

How it works, step by step

System design and data flows

  1. Input data

    Orders arrive from warehouse management; robots provide camera, laser and position data.

    Robots combine camera, laser and movement data for navigation. Robots accompanying staff may record who handles which order and when; their cameras also capture nearby people.

  2. Processing

    Fleet control allocates orders and plans routes; each robot detects nearby obstacles and people.

    The robot processes sensor data, identifies its surroundings and plans movements to fulfil a transport task. A controller executes the selected movements and continually processes new feedback. Perception, planning and physical execution operate as a connected control loop.

  3. Results

    Robots visit storage locations, bring goods or shelves to workstations, or show staff which item to pick next.

    The result describes the defined model task. A match, category or forecast acquires its professional significance only in conjunction with the underlying data and intended use.

  4. Use and impact

    Control determines dispatch speed and workers' pace, and every error has immediate effects on the warehouse floor.

    This is where computation leads to physical execution. The controller carries out a selected action; sensors then provide new information about the state of the environment.

Explore the system design: Select a component to see its task, incoming and outgoing data, and role in the use case. The guided walkthrough explains each phase. Then use Yes, Uncertain and No to represent your specific system variant.

5 review first12 clarify9 baseline threats

InputProcessingResultImpact Externalaccess 22 Input 12 Third-partycontent 00 AI model 11 Providerservice 03 Documentcollection 00 Memory 00 Training andfeedback 03 OtherAI agents 00 Tools andinterfaces 00 Result 00 Business appor equipment 10 person 00 Affectedperson 00 AI serviceswithout approval 01 Externalaccess 22 Input 12 Third-partycontent 00 AI model 11 Providerservice 03 Documentcollection 00 Memory 00 Training andfeedback 03 OtherAI agents 00 Tools andinterfaces 00 Result 00 Business appor equipment 10 person 00 Affectedperson 00 AI serviceswithout approval 01
  • Present
  • Depends on your system
  • Absent
  • Review first
  • To clarify
Architecture as a list

How it differs from similar use cases

Data and information

Input data and information sources

Robots accompanying staff may record who handles which order and when; their cameras also capture nearby people. Robots combine camera, laser and movement data for navigation.

Processing and operating environment

DHL Supply Chain uses Locus Robotics robots; Amazon uses its own models.

Storage and further use

Amazon developed DeepFleet itself; other deployers adopt a manufacturer's robots and software. Fleet movement data can feed into further controller development.

Results and data sharing

Whether fleet control posts entries in the warehouse management system depends on integration. Robots drive autonomously through the warehouse and move shelves, containers or goods.

Using a provider does not automatically determine where processing takes place. Storage location, subcontractors, deletion periods and use for training must be checked for the specific deployment.

Assess risks and determine measures

From understanding the system to analysing risks

  1. Define the system boundary

    Record data, participants, interfaces and permissions. Resolve uncertain characteristics in the model.

  2. Investigate threats

    Relate review candidates to actual data flows: what could happen, and who would be affected?

  3. Assess risk

    Assess impacts and likelihood in the deployment context. The number of review candidates is not a risk score.

  4. Provide evidence of measures

    Select appropriate controls, assign responsibilities and define effectiveness checks. Document residual risks and approvals.

Key threats in the application context

  • 3 particularly important
  • 2 typical
  • 12 depending on the system
  • 9 for every AI system

A total of 26 threats from the threat catalogue may be relevant to this use case.

Also review: whether the outputs are substantively correct

Check probability reliability, possible test-data leakage into training and the consequences of an error in the business process.

Also review: Safety of people and equipment

Check independent safety functions, intended operating limits and the safety evidence your sector requires.

Priority review points

  1. Excessive Agency

    Robots and people share spaces. If control permits more than the safety concept allows, such as higher speeds or entry into restricted zones, collisions may result.

  2. Application Denial of Service

    An attack disabling central fleet control or the wireless network stops all robots at once. The warehouse can no longer dispatch goods.

  3. Misalignment

    A controller judged mainly on throughput increases workers' pace and plans routes with barely adequate safety distances. More parcels per hour then mean greater strain and accident risk.

2 further review candidates in the typical design

Other relevant threats

  1. Model Denial of Service

    Complex or high-volume inputs can exhaust model computing resources.

    Listed because of these characteristics

    • Controls physical processes
    • Accessible over a network
    • Processes text, images and speech together

  2. Multimodal Attacks

    Each additional input type, such as images or speech, opens another route for manipulation or contradictory results.

    Listed because of this characteristic

    • Processes text, images and speech together

Up to 12 further threats may apply, depending on your system design. Nine threats need to be reviewed for every AI system.

System features and open questions

Each Yes adds further threats to your checklist, up to 12 in total. If you cannot yet answer a question, include its threats in your review.

How many threats a Yes adds
  • Added by this Yes

How your system is built

  1. Processes confidential data3 threats
  2. Learns from your own data3 threats
  3. Processes personal data2 threats
  4. Uses operational feedback2 threats
  5. Determines its own work steps1 threat
  6. Uses another provider's model1 threat
  7. Learns in operation without renewed approval1 threat
  8. Learns from examples with correct answers1 threat

How you operate the system

  1. Accepts queries directly through an interface2 threats
  2. Incurs costs with each query1 threat
  3. Passes content to services outside your environment1 threat
  4. Shares computing resources with other customers or applications1 threat
  5. Used alongside unapproved AI services1 threat

How your system is built

Does the system determine its next steps itself based on earlier results?

Fleet control plans routes and sequences and, depending on the system, allocates jobs or replans after disruptions.

If yes, add

Does the system process information about identifiable people, including in inputs, outputs or logs?

Robots accompanying staff may record who handles which order and when; their cameras also capture nearby people.

If yes, add

Does the model or its base model come from another provider, even if you host it?

DHL Supply Chain uses Locus Robotics robots; Amazon uses its own models.

If yes, add

Does the system keep learning in operation and change its behaviour without renewed approval?

If yes, add

How you operate the system

These questions arise for almost every AI system.

Controls and implementation

Measures for the priority review areas are expanded. Each linked measure explains how to implement it.

Review priorityExcessive Agency3 measures
Review priorityApplication Denial of Service3 measures
Review priorityMisalignment3 measures
Model Denial of Service3 measures
Multimodal Attacks2 measures

Controls for the remaining threats are available on their pages in the threat catalogue.

AI Act provisions

When AI Act provisions apply
  1. Art. 4: AI literacy

    For every AI system Applies since 2 February 2025.

  2. Annex III no. 4 point b: Employment decisions

    Depending on use Review trigger from 2 December 2027.

  3. Art. 6 para. 1, Annex I Section A: Safety component of an assessed product

    Depending on use Review trigger from 2 August 2028.

Applies to every AI system

Art. 4: AI literacy

Applies if staff or appointed representatives operate or use AI systems.

Applies since 2 February 2025.

Obligations and details
Who bears the obligations
Provider, Deployer, Product manufacturer, Provider of a general-purpose AI model

Detailed obligations

May apply in particular uses

Annex III no. 4 point b: Employment decisions

Applies if the system assigns tasks by behaviour to workers picking orders with robots or monitors and evaluates their performance.

Applies from 2 December 2027.

Obligations and details
The general condition is
Applies if the system influences working conditions, promotion or dismissal, allocates tasks by behaviour or personal characteristics, or monitors workers' performance and behaviour.
Who bears the obligations
Provider, Deployer, Product manufacturer, Importer, Distributor, Authorised representative

Detailed obligations

Art. 6 para. 1, Annex I Section A: Safety component of an assessed product

Applies if the system acts as a robot's safety component, for example by detecting people in its path, and Annex I legislation requires third-party assessment for that machine.

Applies from 2 August 2028.

Obligations and details
The general condition is
Applies if the system is itself a product or a safety component of a product requiring third-party assessment under Annex I legislation, such as machinery, lifts, medical devices or toys.
Exception
Systems improving only operation, performance or comfort without a safety role are not safety components under Article 6(1a)–(1c).
Who bears the obligations
Provider, Deployer, Product manufacturer

Detailed obligations

Classify your system with the quick checker All obligations in the obligations compass

Cross-cutting threats

Review these threats for every AI system, whatever its purpose.

Why these threats always belong on the checklist

Atypical system features

View 16 further questions

If any of these features does apply to your system, add the listed threats.

Sources

Related use cases

Also in the field of Robotics and vehicles

Often used together with

Updated: 30 September 2026. Review checklist rules version 0.3.2, threat catalogue version 2026.10.24.2. How these pages are structured