Mobile Robotics – Autonomous Systems for Intralogistics and Production

The way materials move through a production facility directly determines how efficiently the entire operation runs. As production environments grow more complex and the pressure to optimise every process step intensifies, mobile robotics has established itself as a key technology for intralogistics and manufacturing automation. Rather than relying on fixed infrastructure to move goods from one point to another, these systems navigate independently through the production floor and adapt in real time to changing conditions. The result is a more flexible and scalable approach to material flow that meets the demands of modern industrial production far better than conventional conveyor-based solutions. This article explores how mobile robotics works, where it delivers the greatest value and what companies should consider when integrating it into existing production environments.

Production environments are changing faster than traditional logistics concepts can keep up with. Shorter product life cycles, increasing product variety, and stricter regulatory requirements create operational complexity that rigid infrastructure can only handle to a limited extent. Mobile robotics closes this gap by offering a level of flexibility that path-bound systems simply cannot provide. However, success does not depend on technical integration alone, but also on acceptance within the working environment: employees should experience autonomous systems as support, not as a threat. Clear communication and the deliberate integration of robots into daily operations help reduce resistance at an early stage.

Intralogistics has always been a supporting function, but it is increasingly becoming a strategic one. Today, the challenge is not only to move goods efficiently, but to do so in a way that remains adaptable when production conditions change. At the same time, digitalisation is increasingly replacing paper-based handovers as well as manual transport and kitting tasks, which are prone to errors and rarely provide high added value for employees. Several factors are driving this complexity:

• Batch sizes are shrinking while product variants are increasing
• Just-in-time supply requirements leave less tolerance for transport delays
• Regulatory frameworks in pharmaceuticals, biotech and food production require traceable material movements
• Growing production volumes on limited floor space put continuous pressure on logistics capacity
• Manual handovers, paper-based processes and human transport errors make end-to-end transparent material logistics more difficult

Mobile robotics systems are mobile transport platforms that move materials, components, and products through production and warehouse environments. Depending on their design, they either operate automatically along defined routes or autonomously with dynamic route planning. AGVs follow clearly predefined paths, which makes them easier to plan and often particularly reliable in structured indoor environments. AMRs can perceive their surroundings more flexibly and adapt their routes independently, making them especially interesting for more dynamic environments and potentially also for outdoor applications. Both system types communicate with control systems and other machines to coordinate material movements on a shared production floor.

In industrial use, two main types of mobile robot platforms are encountered today: Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs). AGVs navigate based on virtual or physical guide lines, following defined paths through the facility. This makes them particularly reliable and precise in stable, clearly structured environments. AMRs, by contrast, map their surroundings independently and plan routes entirely dynamically, giving them greater flexibility in frequently changing layouts. The JAG MoMa Family operates on the AGV principle with virtual line following, but combines this with obstacle detection and avoidance from the AMR domain. The result is a system that pairs the reliability of guided navigation with the robustness of modern safety sensor technology. Third-party systems integrated into the same fleet architecture may be configured as AGVs or AMRs depending on the manufacturer. The key characteristics that distinguish AGV platforms such as the JAG MoMa Family in industrial practice:

1. Precise navigation based on virtual guide lines with defined mission routes
2. Obstacle detection and avoidance without human intervention
3. Fleet management via centralised software platforms
4. Scalability by adding further units as throughput increases
5. Compatibility with existing production and warehouse infrastructure

The automated and partly autonomous behaviour of mobile robotics platforms is based on the interaction of defined navigation, obstacle detection and safety-certified sensor technology. The JAG MoMa platforms combine virtual line following according to the AGV principle with obstacle avoidance from the AMR field. This allows them to move along planned routes while also reacting to obstacles in the working environment. The relevant technologies include:

• Virtual line following for defined and predictable routes
• Obstacle avoidance to react to changing conditions during operation
• 360° LiDAR safety scanner for safe operation in shared working areas
• 3D cameras for obstacle detection at height
• Vision-based positioning correction for precise handovers and interactions with equipment

Safety-certified sensor layers ensure that the system slows down or stops when people or obstacles enter its path. The JAG MoMa platforms are certified according to ISO 3691-4 and ISO 13849, making them suitable for shared operation between people and mobile robotic systems.

A practical advantage of mobile robotics is that integration into existing facilities does not require the structural modifications associated with fixed conveyor systems. No walls need to be broken through, no rail systems installed. The vehicles are configured to the existing environment and communicate with the facility's control systems via standard interfaces, making the technology as suitable for modernisation projects in live operations as it is for new builds.

The impact on intralogistics is most visible in the consistency and predictability that mobile robotics brings to material flow. Processes that previously depended on manual transport cycles become more reliable and overall logistics performance becomes measurable and far easier to improve continuously.

The most direct application in intralogistics is autonomous transport between defined points in the facility. Whether raw materials from the warehouse to production, components to assembly stations or finished goods to packaging: AGV platforms take over tasks that would otherwise require manual labour or rigid conveyor infrastructure. They run around the clock without shift interruptions and with a level of consistency that manual processes rarely sustain over longer production periods.

Bottlenecks in intralogistics often arise not from insufficient transport capacity but from poor coordination and unpredictable timing. Fleet management software addresses this directly by assigning tasks, monitoring vehicle positions and rerouting units when congestion develops. Several factors work together to produce a measurably smoother material flow:

• Consistent transport cycle times that production planning can build on
• Automatic task prioritisation based on current production demand
• Real-time rerouting in response to obstacles or delays
• Reduced idle time at workstations waiting for incoming materials

Every trip an AGV completes generates data. Position tracking, cycle times, task completion rates and exception events are continuously recorded and made visible through fleet management dashboards. This transforms intralogistics from a largely invisible support function into a measurable process that feeds into overall production reporting. In operations where documentation has a regulatory dimension, this data trail offers practical value that extends well beyond its operational role.

Beyond pure warehouse logistics, this technology is increasingly deployed directly within production environments to supply and support active manufacturing processes rather than simply connecting storage areas. In many facilities, mobile robots now operate directly alongside production equipment and deliver materials precisely when and where they are needed. This close integration with the production flow helps minimise waiting times at workstations and keeps manufacturing processes running with greater consistency.

Production lines requiring a continuous supply of components, packaging materials or semi-finished products benefit considerably from this approach. When a production line signals a material requirement, the fleet management system assigns the next available unit and keeps production running without overloading workstations or building up unnecessary floor inventory.

How mobile systems are loaded and unloaded depends heavily on the specific process, the transported goods, and the desired level of automation. In simple applications, employees can manually place materials on the system or remove them. In more highly automated environments, push tables, lifting modules, conveyor technology, or stationary robots are used to load materials onto the mobile unit or remove them from it at defined transfer points. Another option is the direct integration of a robotic arm on the mobile platform. Depending on the task, this can be a 4- or 6-axis robot that independently handles samples, components, tools, or containers. As a result, mobile robotics becomes not only a transport system, but an active part of automated production and laboratory processes.

High-mix low-volume manufacturing is one of the most demanding environments for intralogistics because materials, routes and timing requirements change frequently. Rigid systems struggle to absorb this variability in practice. The ability to reprogram routes and task logic via software without making structural modifications means that production changeovers can be handled quickly and without interrupting the overarching logistics flow. For companies running multiple product variants in parallel, this adaptability translates directly into measurable throughput gains.

In regulated production environments such as pharmaceuticals, life sciences, food production or medical devices, mobile robotics contributes equally to efficiency and compliance. Traceability requirements demand documented material movements, and AGV and AMR systems can digitally record these transport processes and make them traceable. At the same time, their use is not limited to life science applications. In industries such as watchmaking, aerospace or general industrial manufacturing, mobile systems can reliably bring materials, tools, components or consumables to machines, lines and work areas. For companies operating under GMP guidelines or similar quality and documentation requirements, integration requires careful planning. In the long term, however, the benefits include greater process consistency, fewer manual transport errors and better transparency in material flow.

Mobile robotics does not operate in isolation. Its value increases considerably when connected to the broader automation landscape of a production facility, exchanging data in real time with control systems, robot cells and enterprise software. Through these connections, transport tasks can be triggered automatically by production events rather than by manual requests, making material movements an integrated part of the overall automation architecture.

For mobile robotics to function as part of a coherent production system, it must exchange information with the PLCs and control systems managing the rest of the facility. Standard communication protocols enable the vehicles to receive task instructions, report their status and trigger downstream actions upon completing a delivery. When this integration is well implemented, an AGV fleet behaves as a connected layer within the production control architecture rather than running as a separate logistics system alongside it.

When mobile platforms deliver materials to stationary robot cells or collect goods from them, precise synchronisation is essential for clean handoffs. The robot cell must know when a delivery is imminent and the mobile unit must know when the cell is ready to receive. This coordination runs via shared communication layers and the interfaces between the mobile robotics system and the stationary robot equipment must be carefully defined during system design rather than improvised at commissioning.

Full integration into production operations means connecting the fleet management system with MES and ERP platforms so that transport is driven by actual production orders rather than manual requests. When deploying mobile robotics at larger scale, the system connections typically involved include:

1. Fleet management software as the central coordination layer for all vehicle operations
2. MES integration to receive and confirm transport orders linked to production batches
3. ERP connection for inventory updates and material tracking within the facility
4. SCADA or supervisory systems for plant-wide visibility across all automated processes

The decision to invest in mobile robotics is driven by operational, economic and strategic factors in roughly equal measure. When the technology is implemented thoughtfully, its benefits grow over time as the fleet matures and operational data accumulates. Companies typically see simultaneous improvements across several areas, from operational efficiency and workplace safety through to long-term cost structures.

One of the clearest advantages over fixed systems is the ability to scale and adapt without major infrastructure investment. Building capacity means deploying additional units rather than rebuilding conveyor systems, and adapting to a changed production layout means reconfiguring software rather than laying new rails. This structural flexibility makes mobile robotics a strong choice for companies whose production requirements are likely to evolve in the years ahead.

Manual transport of heavy goods, trolleys or pallets is among the most common causes of workplace accidents in industrial production. Replacing these tasks with autonomous transport eliminates ergonomic risks and collision hazards from the working environment while freeing skilled personnel for activities that require genuine expertise and judgement. The safety case for mobile robotics often carries as much weight in investment decisions as the efficiency argument, particularly in facilities that have already experienced handling incidents.

The initial investment in mobile robotics is gradually offset by savings in personnel costs, lower error rates and reduced damage to goods and equipment. The key cost areas where improvements typically become visible:

• Labour hours previously spent on manual transport tasks
• Product and equipment damage caused by handling errors
• Downtime and rework resulting from supply irregularities at production stations

The data continuously generated by the fleet management system also supports ongoing route and task optimisation, meaning the system generally becomes more cost-efficient as operating hours accumulate.

Artificial intelligence is beginning to play a significant role in how mobile robotics systems navigate and make decisions. Machine learning models trained on historical movement data help the system improve incrementally in task allocation and energy management, shifting the approach from deterministic route planning towards genuinely adaptive behaviour across the entire fleet.

The longer-term trajectory points towards production environments in which material flow is fully autonomous and self-organising. In these setups, mobile platforms, robot cells and supervisory control systems communicate continuously to coordinate production without manual intervention at the logistics level. Realising the full potential of mobile robotics in this context requires close integration with process control systems, MES layers and smart factory infrastructure. Companies such as JAG Jakob AG, with their expertise in integrated automation and robotics, are developing the system architectures that make this convergence possible, embedding autonomous transport from the ground up into a coherent digital production model.