Stationary Robotics

Trend Overview

Impact:

High

Adoption:

< 5 Years

Focus Areas:

Automation & Efficiency

Trend Clusters:

Robotics

Sectors:

The trend of Stationary Robotics encompasses all robots that perform value-adding tasks from a fixed location. Attached to the floor, ceiling, or other surfaces, these devices often take the form of or resemble robotic arms.

The first stationary robots were introduced in the late 1950s, primarily in manufacturing and automotive production. More recently, for logistics applications the continued development of sensor technologies and artificial intelligence (AI) has resulted in increased throughput of market offerings across a broad range of use cases.

Stationary robots can be considered in two types of applications – collaborative and industrial. Collaborative applications necessitate human interaction, thereby adding an additional layer of complexity to the overall risk assessment for each individual use case. Industrial applications requiring a high payload, long range, and high speed, and they usually operate in a segregated area surrounded by security fencing.

As machine learning and perception technology continues to develop in laboratory and real-world settings, we at DHL see more applications of interoperability across mobile and stationary robotic systems in logistics.

Especially in light of labor shortages and demand volatility, many companies are realizing the significant economic potential of implementing stationary robots for repetitive logistics processes. Several logistics organizations are now implementing or actively engaged in testing stationary robotic systems in applications such as (de)palletization as well as sortation, picking, and singulation.

Based on anticipated further development of stationary robotics hardware and software and larger-scale production, we here at DHL expect companies will be able to extensively scale these solutions within the next 2 to 4 years. This will have a high impact on the logistics industry.

The Global Robotics Arm Market is Expected to Account for USD 84.66 Billion by 2031

Source: Databridge Market Research (2024): Global Robotics Arm Market - Industry Trends and Forecast to 2031

Relevance to the Future of Logistics

Automated Shipment Sorting

Sorting shipments is a very repetitive, monotonous task that nevertheless requires high-quality output. Operators who are required to perform this task for hours on end in the warehouse tend to lose concentration after a certain amount of time; their work becomes prone to error, leading to additional rework costs. Sorting is therefore an ideal application for automation, particularly the implementation of stationary sorting robots. These devices often use cameras and AI capabilities to differentiate shipments and use pre-defined characteristics to classify and sort them.

While some automated sortation systems use a series of independently moving robots, often called a table-sorting (t-sort) system, other solutions like FlexBuffer by ABB Robotics leverage robotics arms, dynamic racking systems, and inbound and outbound conveyors to provide a comprehensive sorting system for uniform or mixed goods.

Automated sortation solutions can help a facility meet its throughput requirements for order fulfillment. At the same time, they also guarantee proper handling of shipments to prevent sorting errors and protect against damage to outbound orders.

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Robotic Picking & Placing

The manual separation and alignment of parcels, letters, cartons, and flyers to prepare them for further processing downstream is very monotonous and labor intensive. The automation of this process via stationary robots has gained a lot of traction in recent years. Robotic induction, the act of picking an item and placing it with a specific orientation on a conveyer belt as well as identifying its characteristics, is a very scalable solution given its widespread applicability.

Technology providers such as Plus One Robotics, Körber, and Osaro continue to develop AI-based computer vision systems to meet the rising throughput requirements of logistics operations across the world.

Combining advanced computer vision solutions with robotic arms for specialized package manipulation creates potential for consistent throughput and reduced reliance on a fluctuating labor market. With this automation, the manual burden of highly repetitive tasks in operations is reduced, allowing a shift of focus to upskilling workers who are responsible for singulation. However, robotic solution providers will need to continue to prove laboratory throughput rates in operations where the package mix is high.

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Palletizing & Depalletizing

Stationary robotics hold great potential for the automation of palletizing and depalletizing in inbound and outbound warehouse or hub operations.

Distinction should be made between uniform and mixed (de)palletizing. While uniform (de)palletizing is the movement of same-shaped, unvarying goods from and onto a pallet, mixed (de)palletizing describes the handling of pallets with items of various sizes and weights. In general mixed (de)palletizing is more complex than uniform (de)palletizing as it requires much more powerful AI to stack disparate, unwieldy packages as securely and efficiently as possible.

In Jirny, Czech Republic, DHL Supply Chain has implemented a collaborative robotic arm by Universal Robots to palletize goods from two production lines, with a separate Fanuc industrial robotic arm in Jažlovice for similar functions.

Currently, stationary robots that can palletize and depalletize individual shipments are already widely deployed. Mixed depalletizing solutions are also reaching maturity, and at DHL we expect to see widespread use of stationary robots for mixed palletizing throughout the logistics industry in the near future.

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Challenges

Stationary robots are usually designed to handle specific shapes of package and are not designed to handle a wide variety of sizes and weights.

Under real-world conditions, many stationary robots fail to achieve the throughput rates indicated by laboratory experiments.

Brownfield facilities may lack the required technical infrastructure, necessitating costly changes to implement stationary robots on a broad scale.

Even with progressive automation of processes, a human being will always be needed to oversee and support applications; this means that complete automation without human supervision is unlikely in the near future.

When stationary robots are added, facilities require more safety infrastructure and – with each new automation investment – this infrastructure must be reevaluated.

Stationary robots are usually designed to handle specific shapes of package and are not designed to handle a wide variety of sizes and weights.

Under real-world conditions, many stationary robots fail to achieve the throughput rates indicated by laboratory experiments.

Brownfield facilities may lack the required technical infrastructure, necessitating costly changes to implement stationary robots on a broad scale.

When stationary robots are added, facilities require more safety infrastructure and – with each new automation investment – this infrastructure must be reevaluated.

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Outlook

The growing number of successful proofs of concept and pilot projects using stationary logistics robotics across a wide range of industries and environments is increasing the future implementation of stationary robotic systems. The development of stationary robotics has not yet reached its peak and it is anticipated to be advanced by the heightened capabilities of camera systems and machine learning algorithms. As a result, widespread deployment will eventually lead to more complex applications such as robotic orchestration of an end-to-end process by introducing automated handoffs between both stationary & mobile robotic solutions.

This trend should be ACTIVELY monitored,with implementations available for many use cases today.