Machine Vision-Assisted Design of End Effector Pose in Robotic Mixed Depalletizing of Heterogeneous Cargo
Automated depalletizing systems aim to offer continuous and efficient operation in warehouse logistics, reducing cycle times and contributing to worker safety. However, most commercially available depalletizing solutions are designed primarily for highly homogeneous cargo arranged in orthogonal conf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/4/1137 |
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| author | Sebastián Valero Juan Camilo Martinez Ana María Montes Cesar Marín Rubén Bolaños David Álvarez |
| author_facet | Sebastián Valero Juan Camilo Martinez Ana María Montes Cesar Marín Rubén Bolaños David Álvarez |
| author_sort | Sebastián Valero |
| collection | DOAJ |
| description | Automated depalletizing systems aim to offer continuous and efficient operation in warehouse logistics, reducing cycle times and contributing to worker safety. However, most commercially available depalletizing solutions are designed primarily for highly homogeneous cargo arranged in orthogonal configurations. This paper presents a real-time approach for depalletizing heterogeneous pallets with boxes of varying sizes and arbitrary orientations, including configurations where the topmost surfaces of boxes are not necessarily parallel to each other. To accomplish this, we propose an algorithm that leverages deep learning-based machine vision to determine the size, position, and orientation of boxes relative to the horizontal plane of a robot arm from sparse depth data. Using this information, we implement a path planning method that generates collision-free trajectories to enable precise box grasping and placement onto a production line. Validation through both simulated and real-world experiments demonstrates the feasibility and accuracy of this approach in complex industrial settings, highlighting potential improvements in the efficiency and adaptability of automated depalletizing systems. |
| format | Article |
| id | doaj-art-c7ea29a0bca949bbab44e341d8a8e9a6 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c7ea29a0bca949bbab44e341d8a8e9a62025-08-20T02:44:33ZengMDPI AGSensors1424-82202025-02-01254113710.3390/s25041137Machine Vision-Assisted Design of End Effector Pose in Robotic Mixed Depalletizing of Heterogeneous CargoSebastián Valero0Juan Camilo Martinez1Ana María Montes2Cesar Marín3Rubén Bolaños4David Álvarez5Department of Industrial Engineering, Universidad de los Andes, Bogotá 111711, ColombiaDepartment of Industrial Engineering, Universidad de los Andes, Bogotá 111711, ColombiaDepartment of Industrial Engineering, Universidad de los Andes, Bogotá 111711, ColombiaIntegra S.A., Pereira 660003, ColombiaIntegra S.A., Pereira 660003, ColombiaDepartment of Industrial Engineering, Universidad de los Andes, Bogotá 111711, ColombiaAutomated depalletizing systems aim to offer continuous and efficient operation in warehouse logistics, reducing cycle times and contributing to worker safety. However, most commercially available depalletizing solutions are designed primarily for highly homogeneous cargo arranged in orthogonal configurations. This paper presents a real-time approach for depalletizing heterogeneous pallets with boxes of varying sizes and arbitrary orientations, including configurations where the topmost surfaces of boxes are not necessarily parallel to each other. To accomplish this, we propose an algorithm that leverages deep learning-based machine vision to determine the size, position, and orientation of boxes relative to the horizontal plane of a robot arm from sparse depth data. Using this information, we implement a path planning method that generates collision-free trajectories to enable precise box grasping and placement onto a production line. Validation through both simulated and real-world experiments demonstrates the feasibility and accuracy of this approach in complex industrial settings, highlighting potential improvements in the efficiency and adaptability of automated depalletizing systems.https://www.mdpi.com/1424-8220/25/4/1137segmentationhand–eye calibrationrobotic depalletizingheterogeneous cargo handlingdeep learning in roboticssparse stereo vision |
| spellingShingle | Sebastián Valero Juan Camilo Martinez Ana María Montes Cesar Marín Rubén Bolaños David Álvarez Machine Vision-Assisted Design of End Effector Pose in Robotic Mixed Depalletizing of Heterogeneous Cargo Sensors segmentation hand–eye calibration robotic depalletizing heterogeneous cargo handling deep learning in robotics sparse stereo vision |
| title | Machine Vision-Assisted Design of End Effector Pose in Robotic Mixed Depalletizing of Heterogeneous Cargo |
| title_full | Machine Vision-Assisted Design of End Effector Pose in Robotic Mixed Depalletizing of Heterogeneous Cargo |
| title_fullStr | Machine Vision-Assisted Design of End Effector Pose in Robotic Mixed Depalletizing of Heterogeneous Cargo |
| title_full_unstemmed | Machine Vision-Assisted Design of End Effector Pose in Robotic Mixed Depalletizing of Heterogeneous Cargo |
| title_short | Machine Vision-Assisted Design of End Effector Pose in Robotic Mixed Depalletizing of Heterogeneous Cargo |
| title_sort | machine vision assisted design of end effector pose in robotic mixed depalletizing of heterogeneous cargo |
| topic | segmentation hand–eye calibration robotic depalletizing heterogeneous cargo handling deep learning in robotics sparse stereo vision |
| url | https://www.mdpi.com/1424-8220/25/4/1137 |
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