Perceptionbased Intelligent Materialhandling in Industrial Logistics Environments

Poss, Christian; Irrenhauser, Thomas; Prueglmeier, Marco; Goehring, Daniel; Zoghlami, Firas; Salehi, Vahid

doi:10.1145/3313991.3314011

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…Again, methods are used to locate and classify objects (e.g. people [21], load carriers [22]) or to predict information about an object (e.g. 6DoF gripping point [23], pallet pickup pose [24]).…”

Section: Perception For Industrial and Logistics Roboticsmentioning

confidence: 99%

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Towards an Artificial Perception Framework for Autonomous Robots in Logistics

Mayershofer

Fottner

2021

Advances in Automotive Production Technology – Theory and Application

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Autonomous robots in logistics are a promising approach towards a fully automated material flow. In order to use their full potential however, they must be able to extract semantic information from logistics environments. In contrast to other application areas of autonomous robots (e.g. autonomous driving, service robotics) the logistics domain lacks a common dataset and benchmark suite covering multiple sensor modalities and perception tasks. This paper conceptualizes a framework for artificial perception research in logistics that aims to close this gap in a sustainable, data-driven way. Our framework consists of three components: (1) A foundation, based on logistics-specific standards, concepts and requirements. ( 2) An open dataset, covering multiple sensor modalities and perception tasks and (3) a standardized benchmark suite. As shown in other research areas, a common and open platform for datadriven research facilitates novel developments and makes results comparable and traceable over time.

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Section: Perception For Industrial and Logistics Roboticsmentioning

confidence: 99%

“…Reference [23] provides data(on request) on 71 supermarket objects. Given the absence of necessary datasets, [22,26,27] describe the evaluation on custom recorded data. Thus a valid comparison is not possible.…”

Section: Perception For Industrial and Logistics Roboticsmentioning

confidence: 99%

Towards an Artificial Perception Framework for Autonomous Robots in Logistics

Mayershofer

Fottner

2021

Advances in Automotive Production Technology – Theory and Application

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“…Therefore, many scholars have carried out different researches on the improvement and optimization of recognition algorithm for irregular object [9][10][11][12]. Poss et al developed a modular algorithm to achieve a successful grasping rate of 72% when handling various types of express packages, including dirty, labeled, or damaged ones [13]. Li et al utilized progressive probabilistic Hough transform to locate the position of each woven bag in images, which was established a relationship model between image intensity and region-growing threshold [14].…”

Section: Introductionmentioning

confidence: 99%

A new novel recognition and positioning system of black light-absorbing volute for automation depalletizing development

Yang,

Lin,

et al. 2024

Meas. Sci. Technol.

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In the application of vision measurement, the black light-absorbing object is difficult to reflect the structured light from infrared emitter of the RGB-D camera. Therefore, an image recognition algorithm based on reference environment information is proposed to acquire the spatial positioning information of black volutes in the depalletizing system. The hardware system of the depalletizing system is mainly constructed of an upper computer, a six-axis industrial robot, an RGB-D camera and an end adsorption device. Firstly, the horizontal position information of each volute placed on the cardboard is obtained by the depth differences between the cardboard and the volute. Then, the depth information of the volute is obtained by the upper cardboard depth through collecting the position of the end vacuum suction cup triggered by feedback signal from vacuum generator. Secondly, a regional planar hand-eye calibration method is developed to improve the calibration accuracy in two-dimensional coordinates. The regional calibration method divides the robot working area into four regions: upper left, lower left, upper right, and lower right. The transformation matrix of each region is calculated separately. Finally, the depalletizing experiment is conducted on the three types of volutes. It is concluded that the average positioning error of the grasping center point of each volute obtained by our method is 3.795 mm, and its standard deviation is 1.769 mm. The average value of regional planar hand-eye calibration error is 4.044 mm, and its standard deviation is 1.501 mm. Under a stack of materials with dimensions of 1350 mm × 1350 mm × 1500 mm, the maximum error is controlled within 15mm. Additionally, when combined with the end feedback compensation mechanism, the success rate for grasping all three volutes reaches 100%.

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“…The focus in the research field has been on the perception for the gripping step [18][19][20] to the detriment of the placing action [21,22]. Analogically, the focus of the industrial research and development lies in the perception based gripping of items in structured or unstructured environments [23,24].…”

Section: Introductionmentioning

confidence: 99%

Design of a Deep Post Gripping Perception Framework for Industrial Robots

Zoghlami

Kurrek

Jocas

et al. 2020

Journal of Computing and Information Science in Engineering

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The use of flexible and autonomous robotic systems is the solution for the automation in dynamic and unstructured industrial environments. This context requires the robot to be aware of its surroundings throughout the whole manipulation task, also after accomplishing the gripping action. This work introduces the deep post gripping perception framework, which includes the post gripping perception abilities realized with the help of deep learning techniques, especially unsupervised learning methods. These abilities help the robot to execute a stable and precise placing of the gripped items depending on the required task. We describe the development of the framework based on an established literature review. The result of the work is a modular design of the framework with the help of three functional components used to build planning, monitoring and verifying modules.

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Perceptionbased Intelligent Materialhandling in Industrial Logistics Environments

Cited by 3 publications

References 16 publications

Towards an Artificial Perception Framework for Autonomous Robots in Logistics

Towards an Artificial Perception Framework for Autonomous Robots in Logistics

A new novel recognition and positioning system of black light-absorbing volute for automation depalletizing development

Design of a Deep Post Gripping Perception Framework for Industrial Robots

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