Integration of a Multi-Camera Vision System and Admittance Control for Robotic Industrial Depalletizing

Chiaravalli, Davide; Palli, Gianluca; Monica, Riccardo; Aleotti, Jacopo; Rizzini, Dario Lodi

doi:10.1109/etfa46521.2020.9212020

Cited by 26 publications

(16 citation statements)

References 13 publications

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“…• A multi-sensor vision apparatus, mounted on the robot end-effector, detects the products within the pallet. The design and control of the vision system are not discussed in this paper, but in [24], [25]. • Auxiliary mechatronics components, such as actuator boards, emergency stops, etc.…”

Section: System Requirementsmentioning

confidence: 99%

A Mobile Robotized System for Depalletizing Applications: Design and Experimentation

et al. 2021

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In this paper, a mobile manipulation system for automatized logistic applications is presented. The robotic system is specifically designed for depalletizing/palletizing tasks, namely is product extraction from homogeneous pallets and assembly of new heterogeneous pallets. The robotic system is mainly composed by an autonomous vehicle, a collaborative robotic arm and a lifting device, which is able to collect products from different pallet layers. The handling strategy is not based on lifting items, as in classical pick-and-place operations, but on dragging them aboard the mobile vehicle. As the payload weight is not supported by the arm, the overall robotic system is very light compared to the manipulated items, which is a paramount benefit for a mobile collaborative application. This paper presents the mechanical design, the hardware selection and the experimentation in a laboratory scenario, thus demonstrating the effectiveness of the proposed manipulation strategy.

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Section: System Requirementsmentioning

confidence: 99%

A Mobile Robotized System for Depalletizing Applications: Design and Experimentation

et al. 2021

Self Cite

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“…Carton detection is extremely important for automatic logistics transport such as robots performing stacking and unstacking operations, and especially for achieving the goal of completing the automation of logistics scenarios. Traditional methods of carton localization include manual identification, Radio Frequency Identification (RFID)-based carton position measurement [ 1 ], 3D laser scanning measurement [ 2 ], and traditional vision schemes [ 3 ]. However, these schemes always suffer from low adaptability and reliability.…”

Section: Introductionmentioning

confidence: 99%

SCD: A Stacked Carton Dataset for Detection and Segmentation

Yang¹,

Gou³

et al. 2022

Sensors

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Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons and the unloading of cartons in the containers. However, there is no public large-scale carton dataset for the research community to train and evaluate the carton detection models up to now, which hinders the development of carton detection. In this article, we present a large-scale carton dataset named Stacked Carton Dataset (SCD) with the goal of advancing the state-of-the-art in carton detection. Images were collected from the Internet and several warehouses, and objects were labeled for precise localization using instance mask annotation. There were a total of 250,000 instance masks from 16,136 images. Naturally, a suite of benchmarks was established with several popular detectors and instance segmentation models. In addition, we designed a carton detector based on RetinaNet by embedding our proposed Offset Prediction between the Classification and Localization module (OPCL) and the Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality, which boosts AP by 3.1∼4.7% on SCD at the model level, while BGS guides the detector to pay more attention to the boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL for other datasets, we conducted extensive experiments on MS COCO and PASCAL VOC. The improvements in AP on MS COCO and PASCAL VOC were 1.8∼2.2% and 3.4∼4.3%, respectively.

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“…Carton detection is extremely important for automatic logistics transport such as robots perform stacking and unstacking operations, and especially for achieving the goal of completing automation of logistics scenarios. Traditional methods of carton localization include manual identification, RFID-based (Radio Frequency Identification) carton position measurement [1], 3D laser scanning measurement [2], and traditional vision schemes [3]. However, these schemes always suffer from low adaptability and reliability.…”

Section: Introductionmentioning

confidence: 99%

SCD: Stacked Carton Scene Detection

Yang¹,

Wu²,

Gou³

et al. 2022

Preprint

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Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons, the unloading of cartons in the containers. However, there is no public large-scale carton dataset for the research community to train and evaluate the carton detection models up to now, which hinders the development of carton detection. In this paper, we present a large-scale carton dataset named Stacked Carton Dataset (SCD) with the goal of advancing the state-of-the-art in carton detection. Images are collected from the Internet and several warehouses, and objects are labeled using per-instance segmentation for precise localization. There are total of 250,000 instance masks from 16,136 images. Naturelly, a suite of benchmarks are established with several popular detectors. In addition, we design a carton detector based on RetinaNet by embedding our proposed Offset Prediction between Classification and Localization module (OPCL) and Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality which boosts AP by 3.1%∼4.7% on SCD at the model level while BGS guides the detector to pay more attention to boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL to other datasets, we conduct extensive experiments on MS COCO and PASCAL VOC. The improvements of AP on MS COCO and PASCAL VOC are 1.8%∼2.2% and 3.4%∼4.3% respectively. Source dataset is available here.

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Integration of a Multi-Camera Vision System and Admittance Control for Robotic Industrial Depalletizing

Cited by 26 publications

References 13 publications

A Mobile Robotized System for Depalletizing Applications: Design and Experimentation

A Mobile Robotized System for Depalletizing Applications: Design and Experimentation

SCD: A Stacked Carton Dataset for Detection and Segmentation

SCD: Stacked Carton Scene Detection

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