Artificial Intelligence

Advanced Sensors, Artificial Intelligence and Machine Learning Encourage AGV Development


DUBLIN, April 1, 2021 /PRNewswire/ — The “Growth Opportunities of Sensor Technologies for Automated Guided Vehicles” report has been added to ResearchAndMarkets.com’s offering.

AGVS streamlines tasks in order fulfillment in logistics centers as well as improve safety in production lines. AGVs can perform more than material handling such as scanning barcodes and transferring the scanned data to the central cloud system. AGVs automate all the material handling tasks and remove the need for having workers move materials with the operation facility. Therefore, investment in AGVs will increase the rate of ROI in 2 to 3 years. Large logistics companies accelerate ROI since these companies handle thousands of deliveries every day.

AGV market is highly fragmented with many market leaders and startups. Partnerships between these two parties are quite common. For example, the developer of AGVs partnered with deep tech startups to enhance the functionalities of AGVS. Advanced AGVs use both sensors and AI to autonomously navigate in the facility without human guidance.

Key Topics Covered:

Chapter 1: Strategic Imperatives
1.1 The Strategic Imperative
1.2 Impact of the Top Three Strategic Imperatives of AGVs
1.3 About the Growth Pipeline Engine
1.4 Growth Opportunities Fuel the Growth Pipeline Engine
1.5 Research Process & Methodology
1.6 Key Findings

Chapter 2: Technology Landscape: Role of Sensors, AI, and Mobile Manipulators in AGVs
2.1 Automated Guided Vehicles Are Apt For Raw Material Handling For Work-in-progress Manufacturing Scenarios
2.2 AGV: Technology Landscape
2.3 Sensor Technology Trends Associated With AGVs – Key Sensor Technologies And Providers For AGVs
2.4 COVID-19 Pandemic Accelerated The Implementation Of Mobile Manipulator In E-commerce Order Fulfilment And Industrial Operation
2.5 Patenting Trends for AGV: The US Leads the World in Patent Filing

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Chapter 3: Role of Computer Vision and Machine Learning in Automated Guided Vehicles
3.1 AI is Powering AGVs to Help Optimize Production
3.2 Machine Learning and Deep Learning Enhance AGV Capabilities
3.3 Applications of Computer/Machine Vision IN AGVs: Seamless Movement of Objects without Human Intervention
3.4 Applications of Computer/Machine Vision in AGVs for Barcode Reading, Inspection, Surveillance, and Security

Chapter 4: Companies To Action: Key AGV Developers
4.1 Key Participants
4.2 AI Companies Working on the Development of Robotic Vehicles Focusing in artificial intelligence aspects

Chapter 5: Use Cases
5.1 Use Case 1: Micron Technology, US
5.2 Use Case 2: BMW, Germany
5.3 Use Case 3: Universal Robots, Denmark, and Unilever, Poland

Chapter 6: Growth opportunities
6.1 Growth Opportunity 1: Warehouse Automation for Inventory Management
6.2 Growth opportunity 2: Leveraging Safety and Optimizing Costs in Production Facilities

Chapter 7: Key Contacts

Companies Mentioned

  • BMW
  • Micron Technology, US
  • Unilever
  • Universal Robots

For more information about this report visit https://www.researchandmarkets.com/r/j1x14y

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