The trend of Stationary Robotics consists of all robots that perform value-added 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. However, with advances in artificial intelligence (AI) and sensor technologies in recent years, application in the logistics industry has expanded significantly today. Stationary robots can be divided into two types, collaborative robots, which are designed for flexible applications that require interaction with humans, and industrial robots, which are mainly used in applications that require a high payload, long range, and high speed, and usually operate in a segregated area surrounded by security fences. However, even when using collaborative robots, safety measures should be reviewed before use to ensure the health and safety of workers. The increasing prevalence of stationary robots indicates that in future humans will handle fewer monotonous tasks and the focus will shift to collaboration and task sharing between humans and machines.
Especially in light of warehouse labor shortages and demand volatility, many logistics companies are realizing the significant economic potential of implementing stationary robots for repetitive processes. Currently, many companies across all industries are testing stationary robotic solutions and discovering the most promising applications for their logistics operations. Based on this experience and the anticipated further development of stationary robotics hardware and software, we here at DHL expect in 1 to 3 years companies will be able to extensively scale these solutions. This will have a high impact on the logistics industry.
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
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 Solutions 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.
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.
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.
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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- Data Bridge Market Research (2022): Global Robotic Arm Market – Industry Trends and Forecast to 2029
- DHL (2021): DHL Express maximizes productivity amid record volumes at its Miami service center
- DHL (2021): DHL increases productivity with successful robotic sortation pilot in Atlanta