비교리스트

 카트 문의
시장보고서
상품코드
1266821

배송 로봇 시장 - 솔루션별(하드웨어, 소프트웨어), 바퀴 수별(2륜 로봇, 3륜 로봇, 4륜 로봇, 6륜 로봇), 최종사용자별(식품 및 음료, 소매, 헬스케어, 우편) 및 예측(2023-2032년)

Delivery Robot Market - By Solution (Hardware, Software), By Number of wheels (2-wheel robots, 3-wheel robots, 4-wheel robots, 6-wheel robots), By End-Use (Food & Beverage, Retail, Healthcare, Postal Service) & Forecast, 2023 - 2032
발행일: | 리서치사: Global Market Insights Inc. | 페이지 정보: 영문 248 Pages | 배송안내 : 2-3일 (영업일 기준)

※ 본 상품은 영문 자료로 한글과 영문 목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문 목차를 참고해주시기 바랍니다.

배송 로봇 세계 시장 규모는 대학 구내에서의 배송 로봇의 이용 확대에 의해 2032년까지 크게 확대될 것으로 예상됩니다.

미국의 많은 대학에서는 대학 부지에서 배송 로봇의 효율성을 테스트하기 위해 배송 로봇의 사용이 증가하고 있습니다. 실제로 오하이오 주립대학은 보도 배송 로봇 기업인 카트켄과 제휴하여 50대의 완전 자동 배송 로봇을 테스트한 기관 중 하나입니다.

또 다른 예를 보면 2022년 12월 라스트마일 배송 로봇 기업인 키위봇은 대학 구내에서 차세대 로봇 배송을 위해 저명한 식품 주문 마켓플레이스인 그루브허와 계약을 체결했습니다. 이 분야에서의 제품 혁신의 에스컬레이션은 업계의 상황에 더욱 긍정적인 영향을 미칩니다. 예를 들어, 2022년 8월, 저명한 자율형 배송 로봇 제조사인 오토노미는 2세대 배송 로봇인 오토봇 2.0의 출시를 발표했습니다.

배송 로봇 시장 전체는 컴포넌트, 바퀴 수, 최종 용도, 지역에 따라 구분됩니다.

컴포넌트별로는 소프트웨어 분야 시장 규모가 2032년까지 큰 성장을 보일 것으로 예상됩니다. 주요 로봇 제조업체가 뛰어난 기능을 가진 배송 로봇 개발을 위한 노력을 강화하고 있는 것이 소프트웨어 컴포넌트의 수요를 높이고 있습니다. 게다가 스타쉽 테크놀로지스 등 많은 업계 관계자들은 자율주행 배송 로봇의 테스트 프로그램에 투자하고 있으며, 이는 부문의 성장을 촉진할 것으로 예상됩니다.

3륜 배송 로봇의 업계 규모는 2032년까지 5억 달러 이상이 될 것으로 예상됩니다. 이러한 로봇은 내구성이 높고 장기간 사용에 적합하기 때문에 채용이 진행되고 있습니다. 또한 모든 방향으로 빠르게 이동할 수 있으므로 배송 프로세스 최적화에 기여합니다. 또한 2륜 로봇에 비해 높은 효율성을 가지고 있습니다. 업계의 주요 기업이 최첨단 3륜 배송 로봇을 출시하는 데 주력하게 되면서, 부문의 확대에 한층 더 플러스하게 일할 것입니다.

최종 용도별로는 소매체인이 라스트마일 배송 시스템 강화에 힘을 쏟고 있기 때문에 2032년까지 소매분야의 배송 로봇 시장 점유율이 대폭 확대될 것으로 예상됩니다. Target 및 Walmart와 같은 많은 소매 업체들은 워크플로우 효율성을 최적화하기 위해 배송 로봇에 투자하고 있습니다. 배송 로봇은 예측 가능한 배송을 보장하고 소매 공간을 고도로 보완합니다. 또한 배송에 수반되는 수작업 실수를 없애면 보다 신속한 라스트원 마일을 배송할 수 있어 소매점 생산성을 높일 수 있습니다.

지역별로는 아시아태평양의 배송 로봇 산업은 2032년까지 30억 달러 이상에 달할 것으로 예상됩니다. 알리바바 그룹과 같은 업계 관계자가 제품 혁신에 투자하고 있으며 이 지역의 산업 성장에 기여하고 있습니다. 또한 중국과 일본 등 신흥국의 급속한 도시화로 E-Commerce 분야의 수요 증가에 대응하기 위한 배송 로봇에 대한 수요가 높아지고 있습니다.

목차

제1장 조사 방법과 범위

제2장 주요 요약

제3장 배송 로봇 산업 인사이트

  • COVID-19의 발생별 영향에 대해
    • 북미
    • 유럽
    • 아시아태평양
    • LAMEA
  • 러시아·우크라이나 전쟁의 영향
  • 배송 로봇의 업계 에코시스템 분석
    • 원재료의 구입처
    • 제조업체
    • 소프트웨어 및 기술 공급자
    • 서비스 제공자
    • 유통 채널 분석
    • 최종사용자
    • 벤더 매트릭스
  • 테크놀로지&이노베이션의 전망
    • 5G와 AI별 로보틱스
    • IoT와 인더스트리 4.0이 가져오는 임팩트
    • 클라우드 로보틱스
    • 사물인터넷(IoT)
    • 머신 비전 인식
    • 로봇 암
  • 특허 분석
  • 주요 뉴스 & 대처
  • 규제 상황에 대해
    • 국제규격
      • ISO 18646-2:2019 - 로보틱스-서비스 로봇의 성능 기준 및 관련 시험 방법
      • ISO 13482
    • 북미
      • 노동안전보건법(OSHA)
      • STD 01-12-002
      • UL 1740[57] 로봇 및 로봇 기기
    • 유럽
      • RIA 15.06-2013
      • 첨단 로봇과 자율 시스템을 위한 EU 제품 안전 프레임워크
      • 인공지능법
    • 아시아태평양
      • 집적 회로의 레이아웃 설계 보호에 관한 규정
      • 지능 로봇 개발·유통 촉진법 한국
    • 남미
      • NR-12
      • 정령 제5452호
    • MEA
      • 아랍에미리트(UAE)(GCC) 유해 물질 사용 제한(RoHS) 규제에 대해
  • 업계에 미치는 영향요인
    • 촉진요인
      • 소매업에서 배송 로봇의 활용이 진행된다
      • 병원·헬스케어 산업의 성장
      • 대학 구내에서 증가하는 배송 로봇의 활용법
      • E-Commerce 및 온라인 식품 산업의 상승
      • 로봇 제조 기업에 대한 정부의 대처와 투자의 증가
    • 업계의 잠재적 위험 및 과제
      • 고액의 초기 투자에 의한 신규 진입의 제한
  • 성장성 분석
  • Porter's 분석
  • PESTEL 분석

제4장 경쟁 상황, 2022년

  • 소개
  • 각사의 시장 점유율, 2022년
  • 주요 시장 진입 기업, 2022년
    • JD.com
    • Alibaba Group(Cainiao Smart Logistics Network Limited)
    • Kiwibot
    • Nuro
    • Relay Robotics
    • ST Engineering
    • Starship Technologies
  • 경쟁의 포지셔닝 매트릭스
  • 전략적 전망 매트릭스

제5장 배송 로봇 시장 : 솔루션별

  • 주요 동향 : 솔루션별
  • 하드웨어
  • 소프트웨어

제6장 배송 로봇 시장 : 최종 용도별

  • 주요 동향 : 최종 용도별
  • 음식
  • 소매
  • 헬스케어
  • 우편
  • 기타

제7장 배송 로봇 시장 : 바퀴 수별

  • 주요 동향 : 바퀴 수별
  • 2륜 로봇
  • 3륜 로봇
  • 4륜 로봇
  • 6륜 로봇

제8장 배송 로봇 시장 : 지역별 내역

  • 주요 동향 : 지역별
  • 북미
    • 미국
    • 캐나다
  • 유럽
    • 영국
    • 독일
    • 프랑스
    • 이탈리아
    • 스페인
  • 아시아태평양
    • 중국
    • 인도
    • 일본
    • 한국
    • 대만
  • LAMEA
    • 브라질
    • 멕시코
    • GCC
    • 남아프리카공화국

제9장 기업 개요

  • Alibaba Group
  • Alpha Asimov
  • Boston Dynamics
  • Eliport
  • JD.com(Jingdong)
  • Kiwibot
  • Nuro
  • Ottonomy
  • Panasonic Corporation
  • Piaggio & C. SpA
  • Relay Robotics
  • Richtech Robotics
  • Starship Technologies
  • ST Engineering
  • Suzhou Pangolin Robot Corp(CSJBot)
ksm 23.05.09

Global Delivery Robot Market size is anticipated to expand significantly through 2032, owing to the growing use of delivery robots across college campuses. Many universities across the U.S. are increasingly using delivery robots to test their efficacy on college premises. In fact, the Ohio State University is one such institution that partnered with Cartken, a sidewalk delivery robot firm to test 50 fully automated delivery robots.

Citing another instance, in December 2022, Kiwibot, a last-mile delivery robot firm, inked a deal with Grubhub, a prominent food ordering marketplace, for the next-gen robot delivery in college premises. Escalating product innovations in the field are further positively influencing the industry landscape. For instance, in August 2022, Ottonomy, a prominent autonomous delivery robot manufacturer, announced the launch of the Ottobot 2.0, a second-generation delivery robot.

The overall delivery robot market is segmented based on component, number of wheels, end-use, and region.

Based on components, the delivery robot market size from the software segment is anticipated to attain sizeable gains through 2032. Increasing efforts by key robot manufacturers towards developing delivery robots with superior functionalities have increased the demand for software components. Moreover, many industry players, such as Starship Technologies, are also investing in testing programs for self-driving delivery robots, which is expected to boost segment growth.

3-wheel delivery robot industry size is slated to be worth over USD 500 million by 2032. High durability of these robots has increased their adoption for long-term use. These robots can move quickly in any direction, which helps optimize the delivery process. Additionally, they are highly efficient than their 2-wheel counterparts. The growing focus of key industry participants on launching state-of-the-art 3-wheel delivery robots will further positively contribute to segment expansion.

In terms of end-use, delivery robot market share from the retail segment is poised to grow considerably through 2032, owing to the growing focus of retail chains on enhancing last-mile delivery systems. Many retail giants such as Target and Walmart are investing in delivery robots to optimize their workflow efficiency. Delivery robots ensure a predictable delivery, which highly complements the retail space. They also allow faster last-mile delivery and enhance the productivity of retail stores by eliminating the manual errors associated with delivery.

On the regional front, Asia Pacific delivery robot industry value is expected to reach over USD 3 billion by 2032. Strong presence of industry players, such as the Alibaba Group, investing in product innovations, will contribute to the growth of the regional industry. Moreover, rapid urbanization in emerging economies such as China and Japan has increased the demand for delivery robots to cater to the growing demand in the e-commerce space.

Table of Contents

Chapter 1 Methodology and Scope

  • 1.1 Market scope and definitions
  • 1.2 Base estimates & calculations
  • 1.3 Forecast calculations
  • 1.4 Data Sources
    • 1.4.1 Primary
    • 1.4.2 Data mining sources
      • 1.4.2.1 Paid sources
      • 1.4.2.2 Public sources

Chapter 2 Executive Summary

  • 2.1 Delivery robot industry 360 degree synopsis, 2018 - 2032
  • 2.2 Business trends
  • 2.3 Regional trends
  • 2.4 Component trends
  • 2.5 Wheels trends
  • 2.6 End-use trends

Chapter 3 Delivery Robot Industry Insights

  • 3.1 Impact of COVID-19 outbreak
    • 3.1.1 North America
    • 3.1.2 Europe
    • 3.1.3 Asia Pacific
    • 3.1.4 LAMEA
  • 3.2 Impacts of the Russia-Ukraine war
  • 3.3 Delivery robot industry ecosystem analysis
    • 3.3.1 Raw material suppliers
    • 3.3.2 Manufacturers
    • 3.3.3 Software & technology providers
    • 3.3.4 Service providers
    • 3.3.5 Distribution channel analysis
    • 3.3.6 End users
    • 3.3.7 Vendor matrix
  • 3.4 Technology & Innovation landscape
    • 3.4.1 5G and AI in robotics
    • 3.4.2 Impact of IoT and Industry 4.0
    • 3.4.3 Cloud robotics
    • 3.4.4 Internet of Things (IoT)
    • 3.4.5 Machine vision recognition
    • 3.4.6 Robotic arms
  • 3.5 Patent analysis
  • 3.6 Key news & initiatives
  • 3.7 Regulatory landscape
    • 3.7.1 International standards
      • 3.7.1.1 ISO 18646-2:2019: Robotics — Performance criteria and related test methods for service robots
      • 3.7.1.2 ISO 13482
    • 3.7.2 North America
      • 3.7.2.1 Occupational Safety & Health Act (OSHA)
      • 3.7.2.2 STD 01-12-002
      • 3.7.2.3 UL 1740 [57] Robots and Robotic Equipment
    • 3.7.3 Europe
      • 3.7.3.1 RIA 15.06-2013
      • 3.7.3.2 EU product safety framework for advanced robots & autonomous systems
      • 3.7.3.3 Artificial Intelligence Act
    • 3.7.4 Asia Pacific
      • 3.7.4.1 Regulations on the protection of layout designs of integrated circuits
      • 3.7.4.2 Intelligent Robots Development and Distribution Promotion Act: South Korea
    • 3.7.5 South America
      • 3.7.5.1 NR-12
      • 3.7.5.2 Decree Law No. 5452
    • 3.7.6 MEA
      • 3.7.6.1 United Arab Emirates (GCC) Restriction of Hazardous Substances (RoHS) regulation
  • 3.8 Industry impact forces
    • 3.8.1 Growth drivers
      • 3.8.1.1 Increasing use of delivery robots in the retail industry
      • 3.8.1.2 Growth in the hospitality and healthcare industries
      • 3.8.1.3 Increasing use of delivery robots in college campuses
      • 3.8.1.4 Rising e-commerce and online food industries
      • 3.8.1.5 Increasing government initiatives & investments in robot manufacturing companies
    • 3.8.2 Industry pitfalls & challenges
      • 3.8.2.1 High initial investments limiting the entry of new players in the market
  • 3.9 Growth potential analysis
  • 3.10 Porter's analysis
  • 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2022

  • 4.1 Introduction
  • 4.2 Company market share, 2022
  • 4.3 Major Market Players, 2022
    • 4.3.1 JD.com
    • 4.3.2 Alibaba Group (Cainiao Smart Logistics Network Limited)
    • 4.3.3 Kiwibot
    • 4.3.4 Nuro
    • 4.3.5 Relay Robotics
    • 4.3.6 ST Engineering
    • 4.3.7 Starship Technologies
  • 4.4 Competitive positioning matrix
  • 4.5 Strategic outlook matrix

Chapter 5 Delivery Robot Market, By Solution

  • 5.1 Key trends, by solution
  • 5.2 Hardware
    • 5.2.1 Market estimates and forecast, 2018 - 2032
  • 5.3 Software
    • 5.3.1 Market estimates and forecast, 2018 - 2032

Chapter 6 Delivery Robot Market, By End-Use

  • 6.1 Key trends, by end-use
  • 6.2 Food & beverage
    • 6.2.1 Market estimates and forecast, 2018 - 2032
  • 6.3 Retail
    • 6.3.1 Market estimates and forecast, 2018 - 2032
  • 6.4 Healthcare
    • 6.4.1 Market estimates and forecast, 2018 - 2032
  • 6.5 Postal service
    • 6.5.1 Market estimates and forecast, 2018 - 2032
  • 6.6 Others
    • 6.6.1 Market estimates and forecast, 2018 - 2032

Chapter 7 Delivery Robot Market, By Number of Wheels

  • 7.1 Key trends, by number of wheels
  • 7.2 2-wheel robots
    • 7.2.1 Market estimates and forecast, 2018 - 2032
  • 7.3 3-wheel robots
    • 7.3.1 Market estimates and forecast, 2018 - 2032
  • 7.4 4-wheel robots
    • 7.4.1 Market estimates and forecast, 2018 - 2032
  • 7.5 6-wheel robots
    • 7.5.1 Market estimates and forecast, 2018 - 2032

Chapter 8 Delivery Robot Market, By Region

  • 8.1 Key trends, by region
  • 8.2 North America
    • 8.2.1 Market estimates and forecast, 2018 - 2032
    • 8.2.2 Market estimates and forecast, by solution, 2018 - 2032
    • 8.2.3 Market estimates and forecast, by number of wheels, 2018 - 2032
    • 8.2.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.2.5 U.S.
      • 8.2.5.1 Market estimates and forecast, 2018 - 2032
      • 8.2.5.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.2.5.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.2.5.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.2.6 Canada
      • 8.2.6.1 Market estimates and forecast, 2018 - 2032
      • 8.2.6.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.2.6.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.2.6.4 Market estimates and forecast, by end-use, 2018 - 2032
  • 8.3 Europe
    • 8.3.1 Market estimates and forecast, 2018 - 2032
    • 8.3.2 Market estimates and forecast, by solution, 2018 - 2032
    • 8.3.3 Market estimates and forecast, by number of wheels, 2018 - 2032
    • 8.3.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.3.5 UK
      • 8.3.5.1 Market estimates and forecast, 2018 - 2032
      • 8.3.5.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.3.5.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.3.5.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.3.6 Germany
      • 8.3.6.1 Market estimates and forecast, 2018 - 2032
      • 8.3.6.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.3.6.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.3.6.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.3.7 France
      • 8.3.7.1 Market estimates and forecast, 2018 - 2032
      • 8.3.7.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.3.7.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.3.7.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.3.8 Italy
      • 8.3.8.1 Market estimates and forecast, 2018 - 2032
      • 8.3.8.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.3.8.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.3.8.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.3.9 Spain
      • 8.3.9.1 Market estimates and forecast, 2018 - 2032
      • 8.3.9.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.3.9.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.3.9.4 Market estimates and forecast, by end-use, 2018 - 2032
  • 8.4 Asia Pacific
    • 8.4.1 Market estimates and forecast, 2018 - 2032
    • 8.4.2 Market estimates and forecast, by solution, 2018 - 2032
    • 8.4.3 Market estimates and forecast, by number of wheels, 2018 - 2032
    • 8.4.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.4.5 China
      • 8.4.5.1 Market estimates and forecast, 2018 - 2032
      • 8.4.5.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.4.5.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.4.5.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.4.6 India
      • 8.4.6.1 Market estimates and forecast, 2018 - 2032
      • 8.4.6.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.4.6.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.4.6.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.4.7 Japan
      • 8.4.7.1 Market estimates and forecast, 2018 - 2032
      • 8.4.7.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.4.7.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.4.7.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.4.8 South Korea
      • 8.4.8.1 Market estimates and forecast, 2018 - 2032
      • 8.4.8.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.4.8.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.4.8.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.4.9 Taiwan
      • 8.4.9.1 Market estimates and forecast, 2018 - 2032
      • 8.4.9.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.4.9.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.4.9.4 Market estimates and forecast, by end-use, 2018 - 2032
  • 8.5 LAMEA
    • 8.5.1 Market estimates and forecast, 2018 - 2032
    • 8.5.2 Market estimates and forecast, by solution, 2018 - 2032
    • 8.5.3 Market estimates and forecast, by number of wheels, 2018 - 2032
    • 8.5.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.5.5 Brazil
      • 8.5.5.1 Market estimates and forecast, 2018 - 2032
      • 8.5.5.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.5.5.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.5.5.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.5.6 Mexico
      • 8.5.6.1 Market estimates and forecast, 2018 - 2032
      • 8.5.6.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.5.6.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.5.6.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.5.7 GCC
      • 8.5.7.1 Market estimates and forecast, 2018 - 2032
      • 8.5.7.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.5.7.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.5.7.4 Market estimates and forecast, by end-use, 2018 - 2032
    • 8.5.8 South Africa
      • 8.5.8.1 Market estimates and forecast, 2018 - 2032
      • 8.5.8.2 Market estimates and forecast, by solution, 2018 - 2032
      • 8.5.8.3 Market estimates and forecast, by number of wheels, 2018 - 2032
      • 8.5.8.4 Market estimates and forecast, by end-use, 2018 - 2032

Chapter 9 Company Profiles

  • 9.1 Alibaba Group
    • 9.1.1 Business Overview
    • 9.1.2 Financial Data
    • 9.1.3 Product Landscape
    • 9.1.4 Strategic Outlook
    • 9.1.5 SWOT Analysis
  • 9.2 Alpha Asimov
    • 9.2.1 Business Overview
    • 9.2.2 Financial Data
    • 9.2.3 Product Landscape
    • 9.2.4 SWOT Analysis
  • 9.3 Boston Dynamics
    • 9.3.1 Business Overview
    • 9.3.2 Financial Data
    • 9.3.3 Product Landscape
    • 9.3.4 Strategic Outlook
    • 9.3.5 SWOT Analysis
  • 9.4 Eliport
    • 9.4.1 Business Overview
    • 9.4.2 Financial Data
    • 9.4.3 Product Landscape
    • 9.4.4 SWOT Analysis
  • 9.5 JD.com (Jingdong)
    • 9.5.1 Business Overview
    • 9.5.2 Financial Data
    • 9.5.3 Product Landscape
    • 9.5.4 Strategic Outlook
    • 9.5.5 SWOT Analysis
  • 9.6 Kiwibot
    • 9.6.1 Business Overview
    • 9.6.2 Financial Data
    • 9.6.3 Product Landscape
    • 9.6.4 Strategic Outlook
    • 9.6.5 SWOT Analysis
  • 9.7 Nuro
    • 9.7.1 Business Overview
    • 9.7.2 Financial Data
    • 9.7.3 Product Landscape
    • 9.7.4 Strategic Outlook
    • 9.7.5 SWOT Analysis
  • 9.8 Ottonomy
    • 9.8.1 Business Overview
    • 9.8.2 Financial Data
    • 9.8.3 Product Landscape
    • 9.8.4 Strategic Outlook
    • 9.8.5 SWOT Analysis
  • 9.9 Panasonic Corporation
    • 9.9.1 Business Overview
    • 9.9.2 Financial Data
    • 9.9.3 Product Landscape
    • 9.9.4 Market Strategy
    • 9.9.5 SWOT Analysis
  • 9.10 Piaggio & C. SpA
    • 9.10.1 Business Overview
    • 9.10.2 Financial Data
    • 9.10.3 Product Landscape
    • 9.10.4 Market Strategy
    • 9.10.5 SWOT Analysis
  • 9.11 Relay Robotics
    • 9.11.1 Business Overview
    • 9.11.2 Financial Data
    • 9.11.3 Product Landscape
    • 9.11.4 Strategic Outlook
    • 9.11.5 SWOT Analysis
  • 9.12 Richtech Robotics
    • 9.12.1 Business Overview
    • 9.12.2 Financial Data
    • 9.12.3 Product Landscape
    • 9.12.4 SWOT Analysis
  • 9.13 Starship Technologies
    • 9.13.1 Business Overview
    • 9.13.2 Financial Data
    • 9.13.3 Product Landscape
    • 9.13.4 Strategic Outlook
    • 9.13.5 SWOT Analysis
  • 9.14 ST Engineering
    • 9.14.1 Business Overview
    • 9.14.2 Financial Data
    • 9.14.3 Product Landscape
    • 9.14.4 Strategic Outlook
    • 9.14.5 SWOT Analysis
  • 9.15 Suzhou Pangolin Robot Corp (CSJBot)
    • 9.15.1 Business Overview
    • 9.15.2 Financial Data
    • 9.15.3 Product Landscape
    • 9.15.4 Strategic Outlook
    • 9.15.5 SWOT Analysis
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