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Global Goods-to-Person AMRs Market Strategic Research Report

Global Goods-to-Person AMRs Market Strategic Research Report
$3,500 USD
Market Research Reports
Strategic Research Report
Global Goods-to-Person AMRs Market
$2.57B2025
22%CAGR
2032Forecast
Market Research Reports · Global
Market Research Reports Intelligence Series

By Type: LiDAR Based, LiDAR+Vision Based

By Application: e-Commerce, Logistics & 3PL, Retail, Manufacturing, Others

Regional Forecast: Asia Pacific, Latin America, MEA, Europe, North America

Key Players: Geekplus Technology, Mobile Industrial Robots, Omron, ABB, Grey Orange, 6 River Systems, Fetch Robotics (Zebra), Dematic, Yujin Robot, ForwardX Robotics, Iplusmobot Technology, KUKA (Swisslog), Mushiny, Vecna, Locus Robotics, Syrius Robotics, Django Robotics, Bosch Rexroth, Amazon, AMS, Inc., DF Automation & Robotics, Karter (Weighpack)

Region: Global
Formats: PDF, Excel, Word & PowerPoint
Base year: 2025 · forecast to 2032
Length: 157 pages
Market size 2025
$2.57B
Billion USD
Forecast CAGR
22%
2025-2032
Forecast 2032
$10.3B
Projected
Regions
5
Asia Pacific · Latin America · MEA · Europe · North America

Overview

Scope of the Report

The global Goods-to-Person AMRs market size is predicted to grow from US$ 2,567 million in 2025 to US$ 10,210 million in 2032; it is expected to grow at a CAGR of 22.0% from 2026 to 2032.

An autonomous mobile robot (AMR) is a robot that performs behaviors or tasks with a high degree of autonomy. The AMR relies on autonomous navigation where no wires, tape, GPS or other navigation markers are required. Its laser guidance system assures precise navigation, obstacle avoidance and human safety. The drive-around mapping with laptop adjustments enables fast and easy route updates and additions. The demands placed on the functionality of autonomous robotic systems are significantly higher compared to conventional industrial robots. The aim is that mobile systems operate autonomously in unknown and dynamic environments to fulfill their assigned tasks. For this purpose, it is essential to explore and model the environment in a suitable way. The information gathered by sensors has to be combined to allow for an accurate positioning. In addition, the perceived surroundings have to be consolidated in an exact map representation. Having acquired this knowledge, the robot is able to plan an optimal collision-free path to a given goal and to perform complex handling tasks. Goods-to-Person (GTP) is a warehouse automation solution in which products are brought to a person by a Autonomous Mobile Robots (AMRs).

The growth of the Goods-to-Person Autonomous Mobile Robot (G2P AMR) market is being fueled by several key drivers. First and foremost, the rapid expansion of e-commerce has significantly increased the demand for faster, more accurate, and more efficient fulfillment processes. G2P AMRs directly address this need by bringing goods to human pickers, enabling a “goods-to-person” model that drastically reduces picking time, minimizes walking distance, and enhances order accuracy.

Simultaneously, the global logistics industry is facing increasing labor shortages and rising workforce costs, making automation an attractive and often necessary solution. G2P AMRs help mitigate labor dependency while improving throughput, enabling companies to scale operations without proportionally increasing headcount. These robots can operate continuously, navigate dynamic warehouse environments autonomously, and adapt flexibly to changing layouts or order volumes.

Technological advancements in fields such as artificial intelligence, sensor fusion, real-time mapping (SLAM), and machine vision have further improved the reliability, safety, and performance of AMRs. This makes them increasingly capable of operating in complex, high-density storage environments with minimal human oversight. The seamless integration of G2P AMRs with Warehouse Management Systems (WMS) and other software platforms has also contributed to smarter and more coordinated operations.

Global key players of Goods-to-Person AMRs include Geekplus Technology, Mobile Industrial Robots and Omron, etc. The top three players hold a share about 26%. North America is the largest market, has a share about 35%. In terms of product type, LiDAR+Vision Based is the largest segment, occupied for a share of about 79%, and in terms of application, e-Commerce has a share about 36 percent.

This report presents a comprehensive overview of the global Goods-to-Person AMRs market, covering market size and forecast, segmentation by product type and application, competitive landscape, leading players and regional and country-level outlook.

Segment by Type

  • LiDAR Based
  • LiDAR+Vision Based

Segment by Application

  • e-Commerce
  • Logistics & 3PL
  • Retail
  • Manufacturing
  • Others

Who Can Use This Report?

This report is written for decision-makers who need a clear, data-backed view of the global Goods-to-Person AMRs market:

  • Manufacturers, suppliers and solution providers benchmarking their position and planning product, capacity and go-to-market strategy
  • Distributors, channel partners and end users in e-Commerce, Logistics & 3PL, Retail evaluating demand and sourcing options
  • Investors, financial analysts and consultants assessing growth opportunities, competitive dynamics and M&A potential
  • Government agencies, industry associations and research institutions tracking industry developments and policy impact

Market snapshot

Global Goods-to-Person AMRs Market Strategic Research Report snapshot, 2025–2032

Source: Market Research Reports
Market size CAGR 22%
Regional growth momentum
Market share by segment
Key metrics
Base value
$2.57B
2025
Forecast
$10.3B
2032
CAGR
22%
2025–2032
Regions
5
global
Key companies
Geekplus TechnologyMobile Industrial RobotsOmronABBGrey Orange6 River SystemsFetch Robotics (Zebra)Dematic
© MarketResearchReports.comDisclaimer: The actual data may vary in the final report which undergoes verification check post order confirmation.

Segments covered in this report

By Type
LiDAR BasedLiDAR+Vision Based
By Application
e-CommerceLogistics & 3PLRetailManufacturingOthers

Table of contents

Click a chapter to expand
01Executive Summary
02Industry Overview & Forecast
  • 2.1.1 Market Definition and Scope
  • 2.1.2 Market Size and Growth Forecast
  • 2.1.3 Volume Analysis
  • 2.1.4 Segment Outlook by Type
  • 2.1.5 Segment Outlook by Application
  • 2.1.6 Regional Outlook
  • 2.1.7 Structural Developments Shaping the Forecast
  • 2.1.8 Forecast Risks and Sensitivities
03Market Segmentation by Type
  • 3.1 Market Segmentation by Type
  • 3.1.1 Market by Type Overview
  • 3.1.2 LiDAR Based
  • 3.1.3 LiDAR+Vision Based
  • 3.1.4 Volume Analysis
04Market Segmentation by Application
  • 4.1 Market Segmentation by Application
  • 4.1.1 Market by Application Overview
  • 4.1.2 e-Commerce
  • 4.1.3 Logistics & 3PL
  • 4.1.4 Retail
  • 4.1.5 Manufacturing
  • 4.1.6 Others
  • 4.1.7 Volume Analysis
05Regional Market Forecast
  • Asia Pacific
  • North America
  • Europe
  • Middle East & Africa
  • Latin America
06Country-Level Market Forecast
  • 6.1 Asia Pacific
  • 6.1.1 China
  • 6.1.2 Japan
  • 6.1.3 Korea
  • 6.1.4 Southeast Asia
  • 6.1.5 India
  • 6.1.6 Australia
  • 6.1.7 Rest of Asia Pacific
  • 6.2 North America
  • 6.2.1 United States
  • 6.2.2 Canada
  • 6.2.3 Mexico
  • 6.2.4 Rest of North America
  • 6.3 Europe
  • 6.3.1 Germany
  • 6.3.2 France
  • 6.3.3 UK
  • 6.3.4 Italy
  • 6.3.5 Russia
  • 6.3.6 Rest of Europe
  • 6.4 Middle East & Africa
  • 6.4.1 Egypt
  • 6.4.2 South Africa
  • 6.4.3 Israel
  • 6.4.4 Turkey
  • 6.4.5 GCC Countries
  • 6.4.6 Rest of Middle East & Africa
  • 6.5 Latin America
  • 6.5.1 Brazil
  • 6.5.2 Rest of Latin America
07Growth Drivers & Inhibitors
  • 7.1 Growth Drivers & Inhibitors
  • 7.1.1 Section Overview
  • 7.1.2 Growth Drivers
  • 7.1.3 Growth Inhibitors
  • 7.1.4 Driver and Inhibitor Impact Assessment
  • 7.1.5 Analyst Perspective
08Key Company Profiles
  • 8.1 Geekplus Technology
  • 8.1.1 Company Overview
  • 8.1.2 Key Products & Segments
  • 8.1.3 Financial Performance (2023–2025)
  • 8.1.4 Business Strategy
  • 8.1.5 SWOT Analysis
  • 8.1.6 Strategic Implications (2026–2032)
  • 8.2 Mobile Industrial Robots
  • 8.2.1 Company Overview
  • 8.2.2 Key Products & Segments
  • 8.2.3 Financial Performance (2023–2025)
  • 8.2.4 Business Strategy
  • 8.2.5 SWOT Analysis
  • 8.2.6 Strategic Implications (2026–2032)
  • 8.3 Omron
  • 8.3.1 Company Overview
  • 8.3.2 Key Products & Segments
  • 8.3.3 Financial Performance (2023–2025)
  • 8.3.4 Business Strategy
  • 8.3.5 SWOT Analysis
  • 8.3.6 Strategic Implications (2026–2032)
  • 8.4 ABB
  • 8.4.1 Company Overview
  • 8.4.2 Key Products & Segments
  • 8.4.3 Financial Performance (2023–2025)
  • 8.4.4 Business Strategy
  • 8.4.5 SWOT Analysis
  • 8.4.6 Strategic Implications (2026–2032)
  • 8.5 Grey Orange
  • 8.5.1 Company Overview
  • 8.5.2 Key Products & Segments
  • 8.5.3 Financial Performance (2023–2025)
  • 8.5.4 Business Strategy
  • 8.5.5 SWOT Analysis
  • 8.5.6 Strategic Implications (2026–2032)
  • 8.6 6 River Systems
  • 8.6.1 Company Overview
  • 8.6.2 Key Products & Segments
  • 8.6.3 Financial Performance (2023–2025)
  • 8.6.4 Business Strategy
  • 8.6.5 SWOT Analysis
  • 8.6.6 Strategic Implications (2026–2032)
  • 8.7 Fetch Robotics (Zebra)
  • 8.7.1 Company Overview
  • 8.7.2 Key Products & Segments
  • 8.7.3 Financial Performance (2023–2025)
  • 8.7.4 Business Strategy
  • 8.7.5 SWOT Analysis
  • 8.7.6 Strategic Implications (2026–2032)
  • 8.8 Dematic
  • 8.8.1 Company Overview
  • 8.8.2 Key Products & Segments
  • 8.8.3 Financial Performance (2023–2025)
  • 8.8.4 Business Strategy
  • 8.8.5 SWOT Analysis
  • 8.8.6 Strategic Implications (2026–2032)
  • 8.9 Yujin Robot
  • 8.9.1 Company Overview
  • 8.9.2 Key Products & Segments
  • 8.9.3 Financial Performance (2023–2025)
  • 8.9.4 Business Strategy
  • 8.9.5 SWOT Analysis
  • 8.9.6 Strategic Implications (2026–2032)
  • 8.10 ForwardX Robotics
  • 8.10.1 Company Overview
  • 8.10.2 Key Products & Segments
  • 8.10.3 Financial Performance (2023–2025)
  • 8.10.4 Business Strategy
  • 8.10.5 SWOT Analysis
  • 8.10.6 Strategic Implications (2026–2032)
  • 8.11 Iplusmobot Technology
  • 8.11.1 Company Overview
  • 8.11.2 Key Products & Segments
  • 8.11.3 Financial Performance (2023–2025)
  • 8.11.4 Business Strategy
  • 8.11.5 SWOT Analysis
  • 8.11.6 Strategic Implications (2026–2032)
  • 8.12 KUKA (Swisslog)
  • 8.12.1 Company Overview
  • 8.12.2 Key Products & Segments
  • 8.12.3 Financial Performance (2023–2025)
  • 8.12.4 Business Strategy
  • 8.12.5 SWOT Analysis
  • 8.12.6 Strategic Implications (2026–2032)
  • 8.13 Mushiny
  • 8.13.1 Company Overview
  • 8.13.2 Key Products & Segments
  • 8.13.3 Financial Performance (2023–2025)
  • 8.13.4 Business Strategy
  • 8.13.5 SWOT Analysis
  • 8.13.6 Strategic Implications (2026–2032)
  • 8.14 Vecna
  • 8.14.1 Company Overview
  • 8.14.2 Key Products & Segments
  • 8.14.3 Financial Performance (2023–2025)
  • 8.14.4 Business Strategy
  • 8.14.5 SWOT Analysis
  • 8.14.6 Strategic Implications (2026–2032)
  • 8.15 Locus Robotics
  • 8.15.1 Company Overview
  • 8.15.2 Key Products & Segments
  • 8.15.3 Financial Performance (2023–2025)
  • 8.15.4 Business Strategy
  • 8.15.5 SWOT Analysis
  • 8.15.6 Strategic Implications (2026–2032)
  • 8.16 Syrius Robotics
  • 8.16.1 Company Overview
  • 8.16.2 Key Products & Segments
  • 8.16.3 Financial Performance (2023–2025)
  • 8.16.4 Business Strategy
  • 8.16.5 SWOT Analysis
  • 8.16.6 Strategic Implications (2026–2032)
  • 8.17 Django Robotics
  • 8.17.1 Company Overview
  • 8.17.2 Key Products & Segments
  • 8.17.3 Financial Performance (2023–2025)
  • 8.17.4 Business Strategy
  • 8.17.5 SWOT Analysis
  • 8.17.6 Strategic Implications (2026–2032)
  • 8.18 Bosch Rexroth
  • 8.18.1 Company Overview
  • 8.18.2 Key Products & Segments
  • 8.18.3 Financial Performance (2023–2025)
  • 8.18.4 Business Strategy
  • 8.18.5 SWOT Analysis
  • 8.18.6 Strategic Implications (2026–2032)
  • 8.19 Amazon
  • 8.19.1 Company Overview
  • 8.19.2 Key Products & Segments
  • 8.19.3 Financial Performance (2023–2025)
  • 8.19.4 Business Strategy
  • 8.19.5 SWOT Analysis
  • 8.19.6 Strategic Implications (2026–2032)
  • 8.20 AMS, Inc.
  • 8.20.1 Company Overview
  • 8.20.2 Key Products & Segments
  • 8.20.3 Financial Performance (2023–2025)
  • 8.20.4 Business Strategy
  • 8.20.5 SWOT Analysis
  • 8.20.6 Strategic Implications (2026–2032)
  • 8.21 DF Automation & Robotics
  • 8.21.1 Company Overview
  • 8.21.2 Key Products & Segments
  • 8.21.3 Financial Performance (2023–2025)
  • 8.21.4 Business Strategy
  • 8.21.5 SWOT Analysis
  • 8.21.6 Strategic Implications (2026–2032)
  • 8.22 Karter (Weighpack)
  • 8.22.1 Company Overview
  • 8.22.2 Key Products & Segments
  • 8.22.3 Financial Performance (2023–2025)
  • 8.22.4 Business Strategy
  • 8.22.5 SWOT Analysis
  • 8.22.6 Strategic Implications (2026–2032)
09Competitive Landscape
  • 9.1 Competitive Landscape Overview
  • 9.2 Competitive Intensity Assessment
  • 9.3 Key Player Strategies & Positioning
  • 9.4 Competitive Dynamics & Strategic Outlook
  • 9.4.1 Emerging Competitive Threats
  • 9.4.2 Consolidation vs. Fragmentation Outlook
  • 9.4.3 Competitive Response Matrix
  • 9.4.4 Strategic Recommendations, 2026–2032
10Porter's Five Forces Analysis
  • 10.1 Threat of New Entrants
  • 10.2 Bargaining Power of Buyers
  • 10.3 Bargaining Power of Suppliers
  • 10.4 Threat of Substitutes
  • 10.5 Competitive Rivalry
11PESTLE Analysis
  • 11.1 Political
  • 11.2 Economic
  • 11.3 Social and Demographic
  • 11.4 Technological
  • 11.5 Legal and Regulatory
  • 11.6 Environmental
  • 11.7 Strategic Implications of the PESTLE Assessment
12SWOT Analysis
13Future Trends & Outlook
  • 13.1 Future Trends & Outlook
  • 13.1.1 Trend Summary and Commercial Maturity Assessment
  • 13.1.2 Technology and Innovation Trends
  • 13.1.3 Long-Term Market Outlook
  • 13.1.4 Investment & M&A Activity Outlook
  • 13.1.5 Overall Outlook Assessment

Frequently asked questions

What is the size of the global Goods-to-Person AMRs market?
The global Goods-to-Person AMRs market is estimated at US$ 2.57 billion in 2025 (base year) and is projected to reach US$ 10.21 billion by 2032.
What is the forecast CAGR for the Goods-to-Person AMRs market?
The market is expected to grow at a CAGR of 22.0% from 2026 to 2032, expanding from US$ 2.57 billion in 2025 to US$ 10.21 billion in 2032, roughly 4.0 times its base-year value.
What is Goods-to-Person AMRs?
An autonomous mobile robot (AMR) is a robot that performs behaviors or tasks with a high degree of autonomy. The AMR relies on autonomous navigation where no wires, tape, GPS or other navigation markers are required. Its laser guidance system assures precise navigation, obstacle avoidance and human safety.
What are the main segments of the Goods-to-Person AMRs market by type?
By type, the market is segmented into LiDAR Based and LiDAR+Vision Based.
Which applications drive demand in the Goods-to-Person AMRs market?
Key applications covered include e-Commerce, Logistics & 3PL, Retail, Manufacturing and Others.
Who are the key players in the Goods-to-Person AMRs market?
Key players profiled include Geekplus Technology, Mobile Industrial Robots, Omron, ABB, Grey Orange, 6 River Systems, Fetch Robotics (Zebra) and Dematic, among 22 companies covered in total.
Which regions and countries are covered for Goods-to-Person AMRs?
The market is analysed across Asia Pacific, North America, Europe, Middle East & Africa and Latin America, with 20 country-level markets including China, Japan, United States, Canada, Germany, France, Egypt and South Africa.
What is driving growth in the Goods-to-Person AMRs market?
The growth of the Goods-to-Person Autonomous Mobile Robot (G2P AMR) market is being fueled by several key drivers.
Who should buy the Goods-to-Person AMRs market report?
The report is intended for manufacturers and solution providers, distributors and end users in e-Commerce, Logistics & 3PL and Retail, investors and consultants, and government or industry bodies who need market size, segmentation, competitive and regional data for the Goods-to-Person AMRs market.
What license options are available for this report?
The report is available as a Single User License (US$ 3,500, one named user), a Site License (US$ 5,250, up to 10 users) and a Global / Corporate License (US$ 7,000, unlimited users), all delivered in PDF format.

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04
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CAGR projections use time-series regression on 5-10 years of historical data, adjusted for identified demand drivers (technology adoption curves, regulatory catalysts, demographic shifts) and demand inhibitors (cost barriers, substitution risk). Scenario modeling covers base, optimistic, and conservative cases.

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