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2003560

全球倉儲人工智慧機器人市場：按功能/應用、機器人類型、人工智慧能力、部署模式、最終用戶/產業、自主層級和地區分類－市場規模、產業動態、機會分析和預測（2026-2035 年）

Global AI Robotics in Warehousing Market: By Function / Application, Robot Type, AI Capability, Deployment Mode, End User / Industry, Autonomy Level, Region - Market Size, Industry Dynamics, Opportunity Analysis and Forecast for 2026-2035

出版日期: 2026年03月19日 | 出版商:

Astute Analytica | 英文 310 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

全球倉庫人工智慧機器人市場正經歷快速且變革性的擴張，反映出自動化在現代價值鏈中日益成長的重要性。該市場在2025年的價值為125.7億美元，預計到2035年將達到1026.7億美元。這一顯著成長意味著在2026年至2035年的預測期內，複合年成長率將達到23.37%。如此快速的成長凸顯了人工智慧驅動機器人的加速普及，因為全球倉庫都在努力滿足日益成長的效率、速度和準確性需求。

推動該市場快速成長的因素有很多。電子商務的擴張是主要驅動力之一，因為線上零售不斷改變消費者的行為，強調快速配送和豐富的產品種類。為了滿足這些需求，倉庫需要更快、更精準地運作，這使得能夠自動化揀貨、包裝、分類和庫存管理等複雜任務的先進機器人系統變得至關重要。此外，倉儲業普遍存在的人手不足也加速了自動化進程。

顯著的市場趨勢

截至2026年初，倉儲人工智慧機器人市場的供應商格局正在經歷一場劇烈的變革，從Start-Ups的新創企業生態系統演變為由產業巨頭和高度專業化的人工智慧顛覆者主導的激烈競爭格局。這種轉變反映了市場的成熟，規模、技術水平和戰略夥伴關係如今已成為成功的關鍵因素。在主要企業中，Geek+ 已成為全球自主移動機器人 (AMR) 部署量最大的企業，並在貨到人 (G2P) 解決方案領域佔據了約50%的全球市場佔有率。

在高密度立方體儲存和食品雜貨自動化領域，AutoStore 和 Symbotic 已成為主要企業。 AutoStore 的模組化、節省空間的儲存系統革新了倉庫設計，使企業能夠在有限的空間內最大限度地提高儲存容量。同時，Symbotic 透過與美國主要零售商建立深度合作關係，確立了行業領先地位，並為全面的端到端自動化解決方案樹立了行業標準。

Locus Robotics 已成為協作式自主移動機器人（簡稱協作機器人）領域的領導企業，其產品專為履約營運而設計。 Locus Robotics 以其高效的機器人即服務 (RaaS) 模式和直覺的多機器人編配軟體而備受讚譽，提供擴充性且易於使用的解決方案。他們的協作機器人可與人類操作員協同工作，在無需大規模基礎設施改造的情況下提高生產效率。

主要成長促進因素

倉庫人事費用不斷上漲，加上技術純熟勞工短缺，正在加速向機器人解決方案的轉型，並成為倉儲行業市場成長的主要驅動力。隨著薪資上漲和對熟練人員的競爭持續推高人事費用，企業面臨著在保持高生產力的同時降低營運成本的壓力。這些財務負擔迫使倉庫業者探索採用自動化技術，以實現穩定的績效，同時避免人工操作帶來的許多挑戰，例如離職率、需要培訓以及員工缺勤等問題。

新機會的趨勢

全球零售和物流公司的巨額投資正在為機器人倉儲市場創造機遇，預計將推動該市場快速擴張和創新。這些行業面臨著提高效率、降低人事費用以及滿足消費者日益成長的快速配送需求的壓力，因此正將機器人技術視為關鍵解決方案。來自全球主要企業的資金湧入正在推動先進機器人系統的研發和應用，使倉庫能夠自動化執行更複雜的任務，並更有效地擴展營運規模。

最佳化障礙

電池劣化和充電瓶頸是阻礙人工智慧機器人市場成長的重大挑戰，尤其是在高度依賴自主移動機器人（AMR）的倉庫環境中。隨著機器人數量的增加，充電基礎設施的壓力日益凸顯。例如，運行200台AMR需要大規模、設計完善的充電設施，以支援其持續運作。如果沒有充足的基礎設施和智慧管理，充電很快就會成為主要的營運瓶頸。

產業價值鏈分析
- 元件供應商
- 機器人製造商
- 軟體和人工智慧解決方案供應商
- 系統整合商
- 最終用戶
產業展望
- 倉庫自動化的發展
- 物流業人工智慧應用趨勢
PESTLE分析
波特五力分析
- 供應商的議價能力
- 買方的議價能力
- 替代品的威脅
- 新進入者的威脅
- 競爭強度
市場成長及前景
- 市場收入估算與預測（2020-2035 年）
- 價格趨勢分析

第4章：全球倉儲人工智慧機器人市場分析

競爭格局儀錶板
- 市場集中度
- 企業市場占有率分析（以金額為準，%），2025 年
- 競爭對手分析與基準測試

第5章：全球倉儲人工智慧機器人市場分析

市場動態和趨勢
- 成長促進因素
- 抑制因子
- 機會
- 主要趨勢
市場規模及預測（2020-2035）
- 透過人工智慧功能
- 按機器人類型
- 自主等級
- 依部署類型
- 按功能/用途
- 最終用戶/行業特定
- 按地區

第6章：北美市場分析

第7章：歐洲市場分析

第8章：亞太市場分析

第9章：中東和非洲市場分析

第10章：南美市場分析

第11章：公司簡介

Yaskawa Electric Corporation
Amazon Robotics
Boston Dynamics
Cognex Corporation
Dematic（KION Group）
Elettric 80 SpA
ABB Ltd.
FANUC Corporation
Fetch Robotics
Geek+
GreyOrange
KUKA AG
Locus Robotics
Magazino GmbH
Mobile Industrial Robots（MiR）
Honeywell Intelligrated
Omron Corporation
Swisslog（KUKA Group）
Teradyne Inc.（Adept Technology）
其他主要企業

第12章附錄

簡介目錄

Product Code: AA03261735

The global AI robotics in warehousing market is undergoing rapid and transformative expansion, reflecting the growing importance of automation in modern supply chains. In 2025, the market was valued at USD 12.57 billion, and it is projected to reach an impressive USD 102.67 billion by 2035. This remarkable growth corresponds to a compound annual growth rate (CAGR) of 23.37% during the forecast period from 2026 to 2035. Such a steep rise highlights the accelerating adoption of AI-driven robotics as warehouses worldwide strive to meet the increasing demands of efficiency, speed, and accuracy.

Several key factors are driving this market surge. The expansion of e-commerce is a primary catalyst, as online retail continues to reshape consumer behavior by emphasizing fast delivery and vast product assortments. To keep pace with these expectations, warehouses must operate at higher speeds and with greater precision, necessitating advanced robotic systems that can automate complex tasks such as picking, packing, sorting, and inventory management. Additionally, widespread labor shortages in the warehousing sector are intensifying the push towards automation.

Noteworthy Market Developments

As of early 2026, the vendor landscape in the AI robotics in warehousing market has undergone a dramatic transformation, evolving from a fragmented startup ecosystem into a fiercely competitive arena dominated by both consolidated industry giants and hyper-specialized AI disruptors. This shift reflects the maturation of the market, where scale, technological sophistication, and strategic partnerships now define success. Among the leaders, Geek+ stands out as the global volume champion in Autonomous Mobile Robot (AMR) deployment, commanding nearly 50% of the global market share in goods-to-person (G2P) solutions.

In the realm of high-density cubic storage and grocery automation, AutoStore and Symbotic have established themselves as the key players. AutoStore's modular and space-efficient storage system has revolutionized warehouse design, allowing companies to maximize storage capacity in limited spaces. Symbotic, meanwhile, has carved out a leadership position through its deep integration with major U.S. retailers, setting the industry standard for comprehensive end-to-end automation solutions.

Locus Robotics has emerged as the undeniable leader in collaborative AMRs, commonly known as cobots, designed specifically for fulfillment operations. Celebrated for its highly effective Robotics-as-a-Service (RaaS) model and intuitive multi-robot orchestration software, Locus offers a solution that is both scalable and user-friendly. Their cobots work alongside human operators, enhancing productivity without requiring extensive infrastructure changes.

Core Growth Drivers

Increasing warehouse labor costs, coupled with a scarcity of skilled workers, are major factors accelerating the shift toward robotic solutions and driving market growth in the warehousing sector. As labor expenses continue to rise, fueled by wage inflation and heightened competition for qualified personnel, companies face mounting pressure to control operational costs while maintaining high levels of productivity. This financial strain compels warehouse operators to explore automation technologies that can deliver consistent performance without the challenges associated with human labor, such as turnover, training needs, and absenteeism.

Emerging Opportunity Trends

High investment from global retail and logistics companies is expected to create favorable opportunities for the robotics warehousing market, driving rapid expansion and innovation. As these industries face increasing pressure to enhance efficiency, reduce labor costs, and meet growing consumer demand for faster delivery times, they are turning to robotics as a critical solution. The influx of capital from major players worldwide is fueling research, development, and deployment of advanced robotic systems, enabling warehouses to automate more complex tasks and scale operations more effectively.

Barriers to Optimization

Battery degradation and charging bottlenecks present significant challenges that could hamper growth in the AI robotics market, particularly in warehouse environments relying heavily on Autonomous Mobile Robots (AMRs). As fleets expand, the strain on charging infrastructure becomes increasingly apparent. For example, managing a fleet of 200 AMRs requires a well-designed and extensive charging setup capable of supporting continuous operations. Without adequate infrastructure and intelligent management, charging can quickly become a major operational bottleneck.

Detailed Market Segmentation

By robot type, the Automated Guided Vehicles (AGVs) segment commanded a substantial 41% market share in 2024, highlighting their pivotal role in industrial automation and logistics. AGVs have earned a reputation as one of the most dependable and mature robotic technologies available, making them a preferred choice for companies seeking to modernize their operations while minimizing risks. Their proven track record in heavy industry and legacy logistics environments underscores their reliability and effectiveness in handling repetitive material transport tasks in complex and often harsh conditions.

By function and application, the picking and packing segment emerged as the leader in the AI robotics in warehousing market, holding an estimated 39% market share in 2025. This dominance highlights the critical importance of these processes within warehouse operations, where efficiency and accuracy directly impact overall productivity and customer satisfaction. Order picking, in particular, has long been recognized as one of the most labor-intensive and costly activities in traditional logistics, historically accounting for 50% to 55% of total warehouse operating expenses. This significant cost burden has driven companies to seek automation solutions that can streamline picking and packing tasks, reduce errors, and lower labor costs.

By AI capability, the machine learning (ML) and predictive analytics segment established its dominance over the market in 2024, capturing a commanding 42.22% share. This strong foothold underscores the critical role that ML and predictive analytics play in elevating robotic systems from basic automated devices to intelligent, adaptive machines capable of complex decision-making. Without these AI capabilities, a robot's functionality is severely limited, akin to an expensive remote-controlled car that can only follow pre-programmed commands without learning or adapting to its environment.

By end users, the e-commerce and omni-channel retail sector dominates the market with a commanding 44% share, reflecting its critical role in shaping logistics and fulfillment strategies. This prominence is largely driven by the increasing demand for micro-fulfillment centers and the pressure to meet stringent same-day delivery service level agreements (SLAs). As consumer expectations for rapid and reliable delivery continue to rise, retailers are compelled to adopt advanced automation solutions that can handle the complexity and scale of modern order fulfillment.

Segment Breakdown

By AI Capability

Machine Learning & Predictive Analytics
Computer Vision & Imaging
Sensor Fusion & IoT Integration
Natural Language Processing (NLP)
Autonomous Navigation & Path Planning
Others

By Robot Type

Automated Guided Vehicles (AGVs)
Towing AGVs
Unit Load AGVs
Autonomous Mobile Robots (AMRs)
Picking AMRs
Pallet Handling AMRs
Robotic Arms & Pick-and-Place Robots
Collaborative Robots (Cobots)
Sorting & Packaging Robots
Others

By Function / Application

Picking & Packing
Sorting & Distribution
Inventory Management & Tracking
Material Transport & Handling
Loading & Unloading
Quality Inspection
Others

By End User / Industry

E-Commerce & Retail
Third-Party Logistics Providers (3PLs)
Food & Beverage
Pharmaceuticals & Healthcare
Consumer Goods
Industrial & Manufacturing
Others

By Deployment Mode

On-Premises
Cloud-Integrated Edge Systems

By Autonomy Level

Semi-Autonomous Robots
Fully Autonomous Robots

By Region

North America
The U.S.
Canada
Mexico
Europe
Western Europe
The UK
Germany
France
Italy
Spain
Rest of Western Europe
Eastern Europe
Poland
Russia
Rest of Eastern Europe
Asia Pacific
China
India
Japan
Australia & New Zealand
South Korea
ASEAN
Rest of Asia Pacific
Middle East & Africa (MEA)
Saudi Arabia
South Africa
UAE
Rest of MEA
South America
Argentina
Brazil
Rest of South America

Geography Breakdown

North America commands a significant 41% share of the global market for AI robotics in warehousing, reflecting the region's proactive approach to addressing critical labor and operational challenges. In both the United States and Canada, the adoption of warehouse robotics is seen as a strategic offensive measure aimed at countering the effects of severe wage inflation and rising labor costs. Warehouse wages in the region have surged beyond $22 per hour, creating substantial pressure on companies to find cost-effective solutions that maintain productivity without escalating expenses. At the same time, warehouses face brutal labor turnover rates exceeding 40%, which disrupts operations and increases recruitment and training costs.
Within this context, North American supply chain executives are prioritizing solutions that offer more than just inexpensive hardware. Their focus has shifted toward Robotics-as-a-Service (RaaS) models and the seamless integration of advanced software systems. Unlike traditional capital expenditure-heavy investments in robotics equipment, RaaS allows companies to treat automation as an operational expense (OpEx), bypassing the often lengthy and challenging capital expenditure (CapEx) approval processes. This flexibility enables warehouses to rapidly deploy robotic systems and scale operations according to demand without the upfront financial burden.
As a result, the North American market for AI robotics in warehousing is characterized by sophisticated, flexible approaches that emphasize operational agility and cost management. The region's supply chain leaders are leveraging RaaS and advanced software capabilities to mitigate labor challenges and to enhance overall warehouse efficiency and competitiveness.

Leading Market Participants

Yaskawa Electric Corporation
Amazon Robotics
Boston Dynamics
Cognex Corporation
Dematic (KION Group)
Elettric 80 S.p.A.
ABB Ltd.
FANUC Corporation
Fetch Robotics
Geek+
GreyOrange
KUKA AG
Locus Robotics
Magazino GmbH
Mobile Industrial Robots (MiR)
Honeywell Intelligrated
Omron Corporation
Swisslog (KUKA Group)
Teradyne Inc. (Adept Technology)

Table of Content

Chapter 1. Executive Summary: Global AI Robotics In Warehousing Market

Chapter 2. Research Methodology & Research Framework

2.1. Research Objective
2.2. Product Overview
2.3. Market Segmentation
2.4. Qualitative Research
- 2.4.1. Primary & Secondary Sources
2.5. Quantitative Research
- 2.5.1. Primary & Secondary Sources
2.6. Breakdown of Primary Research Respondents, By Region
2.7. Assumption for Study
2.8. Market Size Estimation
2.9. Data Triangulation

Chapter 3. Global AI Robotics In Warehousing Market Overview

3.1. Industry Value Chain Analysis
- 3.1.1. Component Suppliers
- 3.1.2. Robotics Manufacturers
- 3.1.3. Software & AI Solution Providers
- 3.1.4. System Integrators
- 3.1.5. End Users
3.2. Industry Outlook
- 3.2.1. Evolution of Warehouse Automation
- 3.2.2. Adoption Trends of AI in Logistics
3.3. PESTLE Analysis
3.4. Porter's Five Forces Analysis
- 3.4.1. Bargaining Power of Suppliers
- 3.4.2. Bargaining Power of Buyers
- 3.4.3. Threat of Substitutes
- 3.4.4. Threat of New Entrants
- 3.4.5. Degree of Competition
3.5. Market Growth and Outlook
- 3.5.1. Market Revenue Estimates and Forecast (US$ Mn), 2020-2035
- 3.5.2. Price Trend Analysis

Chapter 4. Global AI Robotics In Warehousing Market Analysis

4.1. Competition Dashboard
- 4.1.1. Market Concentration Rate
- 4.1.2. Company Market Share Analysis (Value %), 2025
- 4.1.3. Competitor Mapping & Benchmarking

Chapter 5. Global AI Robotics In Warehousing Market Analysis

5.1. Market Dynamics and Trends
- 5.1.1. Growth Drivers
- 5.1.2. Restraints
- 5.1.3. Opportunity
- 5.1.4. Key Trends
5.2. Market Size and Forecast, 2020-2035 (US$ Mn)
- 5.2.1. By AI Capability
  - 5.2.1.1. Key Insights
    - 5.2.1.1.1. Machine Learning & Predictive Analytics
    - 5.2.1.1.2. Computer Vision & Imaging
    - 5.2.1.1.3. Sensor Fusion & IoT Integration
    - 5.2.1.1.4. Natural Language Processing (NLP)
    - 5.2.1.1.5. Autonomous Navigation & Path Planning
    - 5.2.1.1.6. Others
- 5.2.2. By Robot Type
  - 5.2.2.1. Key Insights
    - 5.2.2.1.1. Automated Guided Vehicles (AGVs)
      - 5.2.2.1.1.1. Towing AGVs
      - 5.2.2.1.1.2. Unit Load AGVs
    - 5.2.2.1.2. Autonomous Mobile Robots (AMRs)
      - 5.2.2.1.2.1. Picking AMRs
      - 5.2.2.1.2.2. Pallet Handling AMRs
    - 5.2.2.1.3. Robotic Arms & Pick-and-Place Robots
    - 5.2.2.1.4. Collaborative Robots (Cobots)
    - 5.2.2.1.5. Sorting & Packaging Robots
    - 5.2.2.1.6. Others
- 5.2.3. By Autonomy Level
  - 5.2.3.1. Key Insights
    - 5.2.3.1.1. Semi-Autonomous Robots
    - 5.2.3.1.2. Fully Autonomous Robots
- 5.2.4. By Deployment Mode
  - 5.2.4.1. Key Insights
    - 5.2.4.1.1. On-Premises
    - 5.2.4.1.2. Cloud-Integrated Edge Systems
- 5.2.5. By Function / Application
  - 5.2.5.1. Key Insights
    - 5.2.5.1.1. Picking & Packing
    - 5.2.5.1.2. Sorting & Distribution
    - 5.2.5.1.3. Inventory Management & Tracking
    - 5.2.5.1.4. Material Transport & Handling
    - 5.2.5.1.5. Loading & Unloading
    - 5.2.5.1.6. Quality Inspection
    - 5.2.5.1.7. Others
- 5.2.6. By End User / Industry
  - 5.2.6.1. Key Insights
    - 5.2.6.1.1. E-Commerce & Retail
    - 5.2.6.1.2. Third-Party Logistics Providers (3PLs)
    - 5.2.6.1.3. Food & Beverage
    - 5.2.6.1.4. Pharmaceuticals & Healthcare
    - 5.2.6.1.5. Consumer Goods
    - 5.2.6.1.6. Industrial & Manufacturing
    - 5.2.6.1.7. Others
- 5.2.7. By Region
  - 5.2.7.1. Key Insights
    - 5.2.7.1.1. North America
      - 5.2.7.1.1.1. The U.S.
      - 5.2.7.1.1.2. Canada
      - 5.2.7.1.1.3. Mexico
    - 5.2.7.1.2. Europe
      - 5.2.7.1.2.1. Western Europe
        
        5.2.7.1.2.1.1. The UK
        5.2.7.1.2.1.2. Germany
        5.2.7.1.2.1.3. France
        5.2.7.1.2.1.4. Italy
        5.2.7.1.2.1.5. Spain
        5.2.7.1.2.1.6. Rest of Western Europe
      - 5.2.7.1.2.2. Eastern Europe
        
        5.2.7.1.2.2.1. Poland
        5.2.7.1.2.2.2. Russia
        5.2.7.1.2.2.3. Rest of Eastern Europe
    - 5.2.7.1.3. Asia Pacific
      - 5.2.7.1.3.1. China
      - 5.2.7.1.3.2. India
      - 5.2.7.1.3.3. Japan
      - 5.2.7.1.3.4. South Korea
      - 5.2.7.1.3.5. Australia & New Zealand
      - 5.2.7.1.3.6. ASEAN
        
        5.2.7.1.3.6.1. Indonesia
        5.2.7.1.3.6.2. Malaysia
        5.2.7.1.3.6.3. Thailand
        5.2.7.1.3.6.4. Singapore
        5.2.7.1.3.6.5. Rest of ASEAN
      - 5.2.7.1.3.7. Rest of Asia Pacific
    - 5.2.7.1.4. Middle East & Africa
      - 5.2.7.1.4.1. UAE
      - 5.2.7.1.4.2. Saudi Arabia
      - 5.2.7.1.4.3. South Africa
      - 5.2.7.1.4.4. Rest of MEA
    - 5.2.7.1.5. South America
      - 5.2.7.1.5.1. Argentina
      - 5.2.7.1.5.2. Brazil
      - 5.2.7.1.5.3. Rest of South America

Chapter 6. North America Market Analysis

6.1. Market Dynamics and Trends
- 6.1.1. Growth Drivers
- 6.1.2. Restraints
- 6.1.3. Opportunity
- 6.1.4. Key Trends
6.2. Market Size and Forecast, 2020-2035 (US$ Mn)
- 6.2.1. Key Insights
  - 6.2.1.1. By AI Capability
  - 6.2.1.2. By Robot Type
  - 6.2.1.3. By Autonomy Level
  - 6.2.1.4. By Deployment Mode
  - 6.2.1.5. By Function / Application
  - 6.2.1.6. By End User / Industry
  - 6.2.1.7. By Country

Chapter 7. Europe Market Analysis

7.1. Market Dynamics and Trends
- 7.1.1. Growth Drivers
- 7.1.2. Restraints
- 7.1.3. Opportunity
- 7.1.4. Key Trends
7.2. Market Size and Forecast, 2020-2035 (US$ Mn)
- 7.2.1. Key Insights
  - 7.2.1.1. By AI Capability
  - 7.2.1.2. By Robot Type
  - 7.2.1.3. By Autonomy Level
  - 7.2.1.4. By Deployment Mode
  - 7.2.1.5. By Function / Application
  - 7.2.1.6. By End User / Industry
  - 7.2.1.7. By Country

Chapter 8. Asia Pacific Market Analysis

8.1. Market Dynamics and Trends
- 8.1.1. Growth Drivers
- 8.1.2. Restraints
- 8.1.3. Opportunity
- 8.1.4. Key Trends
8.2. Market Size and Forecast, 2020-2035 (US$ Mn)
- 8.2.1. Key Insights
  - 8.2.1.1. By AI Capability
  - 8.2.1.2. By Robot Type
  - 8.2.1.3. By Autonomy Level
  - 8.2.1.4. By Deployment Mode
  - 8.2.1.5. By Function / Application
  - 8.2.1.6. By End User / Industry
  - 8.2.1.7. By Country

Chapter 9. Middle East & Africa Market Analysis

9.1. Market Dynamics and Trends
- 9.1.1. Growth Drivers
- 9.1.2. Restraints
- 9.1.3. Opportunity
- 9.1.4. Key Trends
9.2. Market Size and Forecast, 2020-2035 (US$ Mn)
- 9.2.1. Key Insights
  - 9.2.1.1. By AI Capability
  - 9.2.1.2. By Robot Type
  - 9.2.1.3. By Autonomy Level
  - 9.2.1.4. By Deployment Mode
  - 9.2.1.5. By Function / Application
  - 9.2.1.6. By End User / Industry
  - 9.2.1.7. By Country

Chapter 10. South America Market Analysis

10.1. Market Dynamics and Trends
- 10.1.1. Growth Drivers
- 10.1.2. Restraints
- 10.1.3. Opportunity
- 10.1.4. Key Trends
10.2. Market Size and Forecast, 2020-2035 (US$ Mn)
- 10.2.1. Key Insights
  - 10.2.1.1. By AI Capability
  - 10.2.1.2. By Robot Type
  - 10.2.1.3. By Autonomy Level
  - 10.2.1.4. By Deployment Mode
  - 10.2.1.5. By Function / Application
  - 10.2.1.6. By End User / Industry
  - 10.2.1.7. By Country