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In 2021, the market for robotic arms alone was estimated at $26.24 billion and is expected to grow further to $74.35 billion by 2029.

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 have to perform this task for hours on end in the warehouse tend to lose concentration after a certain amount of time, making their work error-prone and 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 items for shipment and use pre-defined characteristics to classify and sort them.

One example of such a device, used at a DHL eCommerce facility in Atlanta, Georgia, USA is the DoraSorter sorting robot from Dorabot. This device uses a tray with a clamp instead of a specialized end-of-arm tool to support the specifics of each operation, ensuring even delicate shipments and irregularly shaped items are not crushed or destroyed.

The use of sorting robots like the DoraSorter can help to further drive human-robot collaboration. It also provides valid business cases especially for greenfield facilities.

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.

As an example, the robotic induction solution of Plus One Robotics uses AI to identify objects for pick-and-place applications. When AI is unable to identify objects, a human teleoperator receives an alert message via Yonder supervisor software and can gain access to and control of the robotic arm from a remote service center. The AI system learns from this intervention to further improve its capabilities if similar situations occur in the future.

This AI-human collaboration indicates the potential to create new jobs through widespread implementation of robotic solutions in warehouses and manufacturing environments by upskilling existing labor.

Palletizing & Depalletizing

The automation of palletizing and depalletizing in inbound and outbound warehouse or hub operations holds great potential for stationary robotics. A 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. However, the company Dexterity provides software that can be used with any robot size or configuration to enable flexible handling and optimal stacking of mixed boxes.

Currently, stationary robots that can palletize and depalletize individual shipments are already widely deployed. Mixed depalletizing solutions are also reaching maturity, but we at DHL expect it to take another 2 to 4 years before widespread use of stationary robots for mixed palletizing is achieved throughout the logistics industry.


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.
There will always be use cases where a robot’s capabilities are limited and a human’s ability to perform certain specialist tasks will continue to be more efficient and effective.
When stationary robots are added, facilities require more safety infrastructure and – with each new automation investment – this infrastructure must be reevaluated.

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


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, however. Added to this, widespread deployment will eventually lead to more complex applications.

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