Mobile platforms such as Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) are now the norm in production and intralogistics at many companies. Both technologies have established themselves for the automation of material transportation in industrial production, logistics and warehousing - and will become even more important in the future: according to the 'Trend Report Intralogistics and Plant Transportation 2021' by industrial logistics provider Inform, 76% of respondents from logistics and supply chain management believe that in-house transportation is highly or very highly relevant to the overall success of the company, as it is able to significantly reduce the logistics costs of internal merchandise management.

This importance is in line with the growth forecasts of various market research reports for the global AGV and AMR market, which predict a market potential worth billions by 2025. According to the consulting firm logistic iq, the market for AMRs alone is expected to grow to around 18 billion US dollars.

Increasing challenges

Productivity in use and safety for people and assets in the operating environment have always been the core requirements for AGVs and AMRs. With the advancing automation and digitalization of processes, both terms are being expanded to include new functional content. For example, depending on the application, productivity is also determined by how situationally and dynamically mobile platforms can react to changing requirements, environments and logistics processes.

The trend is moving away from rigid infrastructures and route layouts towards autonomous and decentralized route planning by the vehicles themselves. To achieve this, AGVs and AMRs must be able to localize themselves independently - for optimized navigation or navigation support and also to bring transparency to the material flow as well as the use and whereabouts of vehicles and transport goods.

In order to be able to use this data to control AGVs and AMRs and for digital fleet management, vehicles and vehicle fleets need to be able to integrate seamlessly into existing IT systems and be networked with machines and systems, as well as with each other. At the same time, technologies based on artificial intelligence will enable vehicles to take on increasingly complex tasks.

The safety requirements for AGVs and AMRs are also increasing. In order for mobile platforms to react and navigate flexibly, suitable safety systems are required that enable vehicles to recognize and avoid obstacles on the road and in space. In order to make vehicles more compact and enable cost-efficient series production, leaner safety architectures are also required that meet high safety requirements, including collaborative deployment scenarios. Characteristics of this include more compact drives with integrated safety concepts, fewer control components, less cabling and more decentralized intelligence and communication capability in the on-board safety control system.

Data treasures on board

Avoid collision hazards: With a 3D protective field, mobile platforms can also monitor obstacles in space in a safety-oriented manner.

© Sick

In addition to their actual transportation functions, AGVs and AMRs are able to collect vast amounts of operating and environmental data during operation thanks to their sensors, control technology and software. Location, orientation, 2D environment points and 3D environment contours, loading, speed, condition of components and overall systems, load status, diagnosis of vehicle uptime and malfunctions - these are just a few examples of data that are relevant for the digitalization of intralogistics processes. There is also a trend towards connecting AGVs and AMRs to each other in secure networks - and also coupling different secure networks with each other in a security-oriented manner. Technologies such as radio data transmission or industrial WLAN networks are suitable for the required wireless data exchange.

More productivity through localization

Intelligent localization solutions create the conditions for mobile platforms to define routes autonomously, adapt routes if necessary and react to changes in the situation. The technologies used here include laser scanners, optical-magnetic guidance systems or grid localization and contour localization systems. The latter in particular are increasingly being used as they are able to precisely measure the distance and direction of any type of detected contours using the environment measurement data from 2D or 3D LiDAR sensors or from safety laser scanners on board the vehicles. Walls, gates, pillars or shelves in front of or around the vehicle provide a large number of static orientation points that enable precise localization. Changes in the environment, such as temporarily placing a pallet in front of a hall wall, have no influence on the reliability of the localization.

The latest solutions for environment detection are even able to use 3D measurement data to map the environment (Simultaneous Localization and Mapping, SLAM). This enables mobile platforms to independently explore a new environment and create a map of it, which can then be used for self-localization and navigation in productive operation.