Technical Objectives


Project objective is to develop, demonstrate and validate a generic automation system for factory logistics in modern factories based on advanced Automated Guided Vehicles (AGV).

General objective

Today, automation is only marginally applied to factory logistics. In fact, transportation of raw materials and final products from/to storage and shipment points usually requires manually operated forklifts. The use of robotic Automated Guided Vehicles (AGV) in factory logistics is not yet widespread in manufacturing plants. For this reason, factory logistics is not well integrated into the modern manufacturing processes so far.

The use of forklifts results in low efficiency and high energy consumption. Furthermore, the operation of forklifts is not safe for workers. It is listed among the most frequent causes of severe accidents in factories. This is mainly due to the fact that forklift drivers are prone to errors and not always sufficiently trained. In addition to that, manufacturing environments are often rather cluttered with numerous blind spots.

The PAN-Robots consortium stresses that the use of AGVs in factory operations will lead to:

  • Higher flexibility,
  • Cost efficient logistics,
  • Low energy consumption and
  • Enhanced work safety

Technical objectives

The following 9 objectives summarize the main steps which are identified by the PAN-Robots consortium to develop a new generation of flexible, cost effective, safe and green AGVs. Those advanced AGVs will be able to transport material and products in modern factories based on autonomous on-board path planning and navigation to enable flexibility. The perception system to guide the AGVs through the factory will be based on a novel cooperative approach. Advanced on-board sensors will be combined with infrastructure sensors to enhance the cost effectiveness and increase safety. The fleet management will be intuitive and easy to use by workers without specialized training. In addition the installation time and costs will be dramatically reduced by semi-automated plant exploration supported by a localization approach utilising already existing landmarks and an advanced pallet handling system which detects and picks the pallets autonomously. Finally, the developed generic system will be exemplarily validated in the production process of a bottling company.