R&D Network M-AIR | Multimodal AI for adaptive intralogistics and robotics

Brief description

The goal of the M-AIR Multimodal AI for Adaptive Intralogistics and Robotics R&D network is to develop new products and processes related to digitalization, networking, and automation using artificial intelligence in the context of industrial intralogistics, especially for manufacturing SMEs. The development of applications and the novel combination of robotics, computer vision, and large language models are intended to address the challenges of efficiency, flexibility, safety, and predictability in the industry. This includes not only completely new developments (greenfield) but also the refit (brownfield) of existing logistics systems. In this way, the M-AIR network contributes to securing the long-term competitiveness of adaptive intralogistics and sustainably increasing the degree of automation.

The M-AIR network is a joint project of the Clusters Mobility & Logistics and AIR Artificial Intelligence Regensburg.

Duration: October 1, 2025 - September 30, 2026

Project partner

  • Artiquare GmbH
  • Gradial Data GmbH
  • Indyon GmbH
  • Ingenieurbüro Springs GmbH
  • Innok Robotics GmbH
  • Institut für systemisches Management und Organisation ISMO GmbH
  • Joseph Witt GmbH
  • Ostbayerische Technische Hochschule Amberg-Weiden
  • Ostbayerische Technische Hochschule Regensburg
  • Pendura UG (haftungs-beschränkt)
  • PYLABS GmbH
  • Schindler & Schill GmbH
  • sdp GmbH
  • SimPlan AG
  • Synnotech AG
  • Technische Hochschule Ingolstadt

Project ideas in the M-AIR network Multimodal AI for adaptive intralogistics and robotics

The M-AIR network brings together companies and research institutions that are jointly exploring new approaches for AI-based applications in production, logistics, and industry.
The following topics show possible project ideas that have emerged from previous exchange formats and workshops and could be further developed or explored in greater depth in the future.

  • Voice-controlled mission definition for mobile robots
    The aim is to formulate complex logistics tasks more simply and translate them automatically into precise driving commands.
  • Intelligent route planning for autonomous vehicles
    By evaluating sensor data, plans, and user instructions, vehicles could navigate more flexibly and safely in the future.
  • Adaptive bin picking systems with multimodal AI
    The combination of computer vision, robotics, and language models could significantly expand automation in warehouse and production environments.
  • AI-supported gripping point determination in robotics
    With the help of improved gripping decisions, robots could also act reliably with unknown objects in the future.
  • Generative planning of industrial plant layouts
    Use of generative AI models for the automatic creation and evaluation of factory or logistics layouts.
  • Digital remodeling of existing plants
    Conversion of 2D plans or scans into 3D models for efficient use in modernization or simulation.
  • Automated safety assessment of technical facilities
    Automated assessment of risks and safety requirements.
  • AI-supported management of product compliance
    Use of artificial intelligence to comply with regulatory requirements in global supply chains.

Contact

Uwe Pfeil
Cluster manager
Tel. +49 941 604889 55
uwe.pfeiltechbase.de

 

Project sponsor

VDI/VDE Innovation + Technik GmbH