Depalletization, the process of removing products from a pallet, is a fundamental task in logistics and manufacturing. For years, this work has been labor-intensive, repetitive, and physically demanding, making it prone to human error and injury. However, with the advancement of industrial robotics and computer vision, the question of whether to automate depalletization has become increasingly relevant for companies seeking to optimize their operations.
Why Automate Depalletization?
The decision to automate depalletization often comes down to a combination of economic and operational factors. Robotics offers solutions that address the inherent challenges of manual depalletization. Robots can operate consistently around the clock without breaks, significantly speeding up the receiving and unloading process. This results in a notable increase in throughput, sometimes reaching up to four times the speed of AI-based systems that handle individual products. By eliminating the reliance on human labor for repetitive and physically demanding tasks, companies can reassign their workforce to higher-value activities. Automation also minimizes handling errors, product damage, and workplace injuries. Manual depalletization involves lifting heavy loads and repetitive movements that can lead to fatigue and injury, whereas robots mitigate these risks, creating a safer work environment. Modern robotic systems, especially those equipped with computer vision, can adapt to unexpected variations in product arrangement on the pallet, even if they have shifted during transport or involve mixed items.
There are two main approaches to robotic depalletization: vision-based depalletization using AI and layer-based depalletization. The former uses advanced 3D vision systems to identify and locate individual products, offering a smaller footprint and lower initial investment, though with potentially lower throughput. Layer-based depalletization, on the other hand, employs larger robots with end-of-arm tools (EoAT) that handle an entire layer of products at once, achieving higher throughput but requiring more space. The choice between the two depends on the specific needs of the application, the required performance, and the available space.
Most Demanded Robotic Models for Depalletization
Leading brands in industrial robotics offer a wide range of solutions tailored to the demands of depalletization. KUKA is known for its flexibility and performance, offering palletizing robots with payload capacities ranging from 40 kg to 1,300 kg and reaches of up to 3,601 mm. Their robots are recognized for high speed, precision, repeatability, and compact design, which facilitates integration. ABB, one of the “Big 4” in robotics, is known for its large robotic arms used in heavy-duty applications. Their solutions are characterized by ease of programming, user-friendly interfaces, and strong integration with Industry 4.0 principles. The IRB 6700 model is an example of their robust offering. FANUC, the world’s largest manufacturer of industrial robots, stands out for the speed, precision, and durability of its robots. Its palletizing line includes models such as the R-2000iA and R-2000iB series (e.g., R-2000iB/100H, R-2000iB/210F), and the M-410, M-710, M-420, M-900 series for medium and heavy loads, as well as the M-2000iA for very heavy loads (up to 1200 kg). Yaskawa Motoman, a Japanese brand and another robotics giant, offers industrial robot lines specifically designed for palletizing. Its EPL (“Expert Palletizing”) and MPL (“Master Palletizing”) series are intended for medium to heavy load applications, with payload capacities ranging from 80 kg to 800 kg and configurations from 4 to 6 axes. Models like the EPL80, EPL300, and MPL800 are examples of their offerings.
Most Used Guided Vision Systems
Computer vision is the key component that gives robots the “intelligence” needed to handle the inherent variability of depalletization. 3D guided vision systems are predominant in this application. These systems are essential for recognizing and locating products on pallets, even if they are randomly stacked, have dark or reflective surfaces, or complex structures. They allow robots to identify objects, recognize variations, and manage multiple grips. AI-based vision and deep learning algorithms enable systems to process large volumes of data and “learn” to recognize different types of boxes and objects, even those that are tightly packed. Companies like Photoneo have developed systems that scan an entire pallet, create a 3D texture dataset, and use AI to recognize each box and send precise commands to the robot. RGB-D and Time-of-Flight (ToF) cameras, used by providers like MRDVS, capture detailed depth and color data, allowing for precise detection and placement of packaging units. Integrated 3D vision platforms, such as those offered by Mech-Mind (Mech-Eye DEEP-GL) and Pickit 3D, provide complete platforms with cameras, sensors, and processors. These platforms not only recognize box patterns and find ideal pick-up points but also plan depalletization strategies and ensure collision detection and trajectory planning. DEXFORCE AUTOMATION, with its Xema 3D Camera and Mixed AI platform, also offers solutions to recognize items with different colors and models, adapting to situations with reflective or tightly packed objects.
Automating depalletization is not just a trend but a growing necessity in both traditional and Industry 4.0 robotics. Investing in leading-brand robots and advanced guided vision systems offers tangible benefits in terms of efficiency, cost reduction, and safety, enabling companies to better adapt to the demands of today’s market.
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