Potential for high autonomy and increased efficiency

In the logistics industry in particular, highly autonomous mobile robots offer enormous opportunities to make processes more efficient and optimize them in the long term. According to the World Robotics 2024 Service Robots Report, logistics is currently the largest application area for mobile robots, and this market is growing rapidly. However, despite this potential, many robotic solutions are still in their early stages, especially in terms of autonomy.

The current situation: transportation and simple tasks
Today, mobile robots in logistics primarily perform transportation tasks between fixed points, with their activities often limited to simple tasks such as handing over parcels or moving materials. These robots often work on predefined, fixed routes and do not require complex decision-making or adaptation to their environment. Typically, these systems are equipped with basic control technology, such as sensors for lane guidance and bumpers, that enable them to follow simple routines and to stop safely when they come into contact with an obstacle, without causing any damage.

Examples of such solutions are Automated Guided Vehicles (AGVs), which are used in large warehouses or fixed production lines. These systems use magnetic tapes or QR codes as landmarks on the floor for orientation and perform tasks such as transporting goods from one point to another. However, such systems are not very flexible because they can only function in clearly defined and controlled environments, such as production lines or automated warehouses – in other words, they only work effectively where processes have little variability.



Top 5 areas of application for mobile robots

According to the World Robotics 2024 Service Robots Report, most mobile robots currently used in logistics perform repetitive tasks and navigate either via physical markings or virtual routes along predefined processes. Although their potential is high, their autonomy remains limited. However, modern developments in this area are increasingly relying on advanced sensor technologies such as 3D LiDAR (Light Detection and Ranging) or cameras that enable robots to follow a defined route without the need for structural measures. At the same time, these technologies allow the detection and avoidance of obstacles, which is why such systems are often referred to as autonomous mobile robots (AMR).

The potential for high autonomy: robots with advanced capabilities
In contrast to today's mostly simple transport robots, highly autonomous mobile robots offer far more potential for the logistics industry. Such systems are not only able to scan their environment, but also to make independent decisions to perform complex tasks. In addition to the task of transport, they can, for example, sort products during the journey, label them or prepare them for subsequent process steps. For example, a robot could sort goods by region of destination and place them in the appropriate packaging during transport.

However, for these more advanced applications, mobile robots need advanced navigation systems and artificial intelligence (AI) that enable them to understand and react to dynamic environments.

AI in logistics: the current situation
The use of artificial intelligence in the logistics industry has increased significantly in recent years. While in 2019, just under 15 percent of German logistics companies were working with AI, three years later, 22 percent of German companies were already using artificial intelligence – from demand forecasting and sales planning to transport optimization. This illustrates how dynamically AI technologies are establishing themselves in the logistics industry. 

Technological key components for high autonomy:

An example of deep learning in this context would be “visual recognition and object recognition”: robots require advanced camera systems that are complemented by object recognition algorithms. These technologies enable robots to identify, classify and interact with objects in their environment. AI-based models such as deep convolutional neural networks (DCNNs) have proven particularly effective at accurately recognizing objects to optimize navigation.

With such functionalities, transport routes and times could be used more efficiently, which could lead to significant cost and time savings.

For example, a large logistics company was able to achieve a 225% improvement in picking efficiency by using autonomous mobile robots (see case study in German).

To further increase the efficiency of these technologies, they should be easily integrated into the respective development environment, work together efficiently and remain maintainable.