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Mixed-Fleet Intralogistics Control for Automated Warehouses
NAiSE and Linde Material Handling integrate VDA 5050-compatible autonomous vehicles to support interoperable fleet orchestration in warehouse and production logistics.
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Photo source: Louis Vieira, Linde MH
NAiSE and Linde Material Handling have expanded their intralogistics cooperation through the integration of multiple Linde automated guided vehicle platforms into the NAiSE intralogistics platform. The collaboration addresses a growing industrial requirement for mixed-fleet automation, where autonomous and manually operated material handling vehicles must function within shared operational environments while maintaining traffic coordination, safety compliance, and workflow transparency.
Mixed-Fleet Automation Challenge
Warehouses and manufacturing facilities increasingly deploy autonomous mobile robots and automated guided vehicles for pallet transport, storage replenishment, and internal logistics. However, heterogeneous fleets often create integration complexity when vehicles from different manufacturers rely on separate control systems.
This cooperation addresses that interoperability challenge through VDA 5050, the communication standard developed by the German automotive industry for manufacturer-independent AGV fleet integration. By using this interface framework, fleet orchestration can be centralized without requiring a single-vendor automation architecture.
Linde Material Handling contributes autonomous vehicle hardware, while NAiSE provides the fleet orchestration layer responsible for task management, traffic optimization, and operational visibility.
Integrated Vehicle Platforms and Technical Roles
The integration includes four Linde autonomous vehicle systems designed for different material handling profiles.
The L-MATIC core supports payloads up to 1,200 kg with lifting heights up to 1.84 metres. The L-MATIC AC k increases payload capacity to 1,400 kg and lifting height to 3.8 metres. The L-MATIC HD k supports 1,600 kg loads with lift heights up to 3.5 metres, while the R-MATIC k extends payload capacity to 2,300 kg with storage handling up to 10 metres.
All integrated systems operate at speeds up to 7.2 km/h and use hybrid navigation based on LiDAR and reflector guidance along predefined routes.
Linde’s role includes autonomous vehicle engineering, onboard navigation, and functional safety systems. NAiSE provides the independent fleet control environment enabling coordination between Linde systems and other VDA 5050-compatible vehicles.
Safety and Fleet Coordination Architecture
Shared industrial traffic environments require coordinated safety mechanisms, particularly where autonomous forklifts interact with manual vehicles, warehouse personnel, and dynamic infrastructure.
The integrated Linde systems include 360-degree safety scanners, 2D laser protective curtains, emergency stop systems, collision avoidance functionality, and optical and acoustic warning signals.
Within the broader system architecture, NAiSE’s software layer manages task allocation, route coordination, and traffic prioritization across mixed fleets. This separation between vehicle-level autonomy and centralized orchestration allows facilities to maintain fleet flexibility without locking into a single hardware supplier.
Optional 1D and 2D scanning modules support identification workflows, while use-case-specific attachments and cantilever forklift configurations extend deployment flexibility.
Industrial Deployment Use Cases
The combined system targets warehouse automation, production logistics, pallet handling, goods replenishment, and high-bay storage workflows.
In practical deployment, facilities can use lower-capacity vehicles for repetitive horizontal transport while assigning higher-capacity systems such as the R-MATIC k to vertical storage and retrieval operations. Centralized orchestration improves resource allocation while maintaining compatibility with manually operated logistics assets.
Operational benefits derive from reduced dispatch fragmentation, improved fleet transparency, and simplified integration of future automation assets using standardized interfaces.
Edited by Aishwarya Mambet, Induportals Editor, with AI assistance.
www.naise.com
NAiSE and Linde Material Handling have expanded their intralogistics cooperation through the integration of multiple Linde automated guided vehicle platforms into the NAiSE intralogistics platform. The collaboration addresses a growing industrial requirement for mixed-fleet automation, where autonomous and manually operated material handling vehicles must function within shared operational environments while maintaining traffic coordination, safety compliance, and workflow transparency.
Mixed-Fleet Automation Challenge
Warehouses and manufacturing facilities increasingly deploy autonomous mobile robots and automated guided vehicles for pallet transport, storage replenishment, and internal logistics. However, heterogeneous fleets often create integration complexity when vehicles from different manufacturers rely on separate control systems.
This cooperation addresses that interoperability challenge through VDA 5050, the communication standard developed by the German automotive industry for manufacturer-independent AGV fleet integration. By using this interface framework, fleet orchestration can be centralized without requiring a single-vendor automation architecture.
Linde Material Handling contributes autonomous vehicle hardware, while NAiSE provides the fleet orchestration layer responsible for task management, traffic optimization, and operational visibility.
Integrated Vehicle Platforms and Technical Roles
The integration includes four Linde autonomous vehicle systems designed for different material handling profiles.
The L-MATIC core supports payloads up to 1,200 kg with lifting heights up to 1.84 metres. The L-MATIC AC k increases payload capacity to 1,400 kg and lifting height to 3.8 metres. The L-MATIC HD k supports 1,600 kg loads with lift heights up to 3.5 metres, while the R-MATIC k extends payload capacity to 2,300 kg with storage handling up to 10 metres.
All integrated systems operate at speeds up to 7.2 km/h and use hybrid navigation based on LiDAR and reflector guidance along predefined routes.
Linde’s role includes autonomous vehicle engineering, onboard navigation, and functional safety systems. NAiSE provides the independent fleet control environment enabling coordination between Linde systems and other VDA 5050-compatible vehicles.
Safety and Fleet Coordination Architecture
Shared industrial traffic environments require coordinated safety mechanisms, particularly where autonomous forklifts interact with manual vehicles, warehouse personnel, and dynamic infrastructure.
The integrated Linde systems include 360-degree safety scanners, 2D laser protective curtains, emergency stop systems, collision avoidance functionality, and optical and acoustic warning signals.
Within the broader system architecture, NAiSE’s software layer manages task allocation, route coordination, and traffic prioritization across mixed fleets. This separation between vehicle-level autonomy and centralized orchestration allows facilities to maintain fleet flexibility without locking into a single hardware supplier.
Optional 1D and 2D scanning modules support identification workflows, while use-case-specific attachments and cantilever forklift configurations extend deployment flexibility.
Industrial Deployment Use Cases
The combined system targets warehouse automation, production logistics, pallet handling, goods replenishment, and high-bay storage workflows.
In practical deployment, facilities can use lower-capacity vehicles for repetitive horizontal transport while assigning higher-capacity systems such as the R-MATIC k to vertical storage and retrieval operations. Centralized orchestration improves resource allocation while maintaining compatibility with manually operated logistics assets.
Operational benefits derive from reduced dispatch fragmentation, improved fleet transparency, and simplified integration of future automation assets using standardized interfaces.
Edited by Aishwarya Mambet, Induportals Editor, with AI assistance.
www.naise.com
