Effect of horizontal pick and place locations on shoulder kinematics

Könemann, Reinier; Bösch, T.; Kingma, Idsart; Dieën, Jaap H. van; Looze, M.P. de

doi:10.1080/00140139.2014.968636

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…[33] investigated alternative learning curves to assess learning effects on order-picking operations. [34] studied the effects of horizontal/vertical bin locations, bin angle, and hand usage on arm movements. [13] proposed a framework to integrate human factors into order-picking planning models.…”

Section: Literature Reviewmentioning

confidence: 99%

Minimization of metabolic energy expenditure in collaborative order picking

TUTAM

2023

JSR-A

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Order picking is one of the most repetitive, labor-intensive, and physically demanding operations in warehouses. Picking hundreds of orders daily requires high metabolic energy expenditure and is characterized by poor ergonomics posing high risks for musculoskeletal disorders. In traditional order picking, the order picker walks around racks in a warehouse throughout the day. Alternatively, it is aimed at minimizing inefficient time and musculoskeletal strains with ride-on order picking by allowing the order picker to stand on an operator’s platform of an order-picking truck and ride the truck between stop locations. However, the order picker has to step down from the platform at each stop location and step up onto the platform before riding the truck to the next stop location. Therefore, riding the truck with frequent stops leads to more metabolic energy expenditure and musculoskeletal disorders than walking, although it is faster. Benefiting advantages of both traditional and ride-on order picking, a relatively new order picking truck (collaborative order picking truck) is deployed in warehouses to reduce inefficient walking time and ergonomic riding disorders. In collaborative order picking, the order picker can walk from a stop location directly to the next pick location while the truck moves to the next stop location autonomously or ride the truck to the next stop location in case of having a large distance between stop locations. This paper develops an optimization model to minimize total metabolic energy expenditure in collaborative order picking by finding the shortest route and the best collaboration decision (walk or ride). Based on the Monte Carlo simulation, the metabolic energy expenditure with collaborative order picking is analyzed. Our results indicate metabolic energy savings with collaborative order picking up to 200% and 83% compared to traditional and ride-on order picking, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Minimization of metabolic energy expenditure in collaborative order picking

TUTAM

2023

JSR-A

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“…Directly associated with our case study are two commonplace workplace situations. In the first, parts are manually selected by a worker [ 24 ]. In the second, vibratory part feeders sort the piece [ 20 , 21 ].…”

Section: Case Study and Appropriateness To Industry 40mentioning

confidence: 99%

Practical Use of Robot Manipulators as Intelligent Manufacturing Systems

Blanco

Abu‐Dakka

Abderrahim

2018

Sensors

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This paper presents features and advanced settings for a robot manipulator controller in a fully interconnected intelligent manufacturing system. Every system is made up of different agents. As also occurs in the Internet of Things and smart cities, the big issue here is to ensure not only that implementation is key, but also that there is better common understanding among the main players. The commitment of all agents is still required to translate that understanding into practice in Industry 4.0. Mutual interactions such as machine-to-machine and man-to-machine are solved in real time with cyber physical capabilities. This paper explores intelligent manufacturing through the context of industrial robot manipulators within a Smart Factory. An online communication algorithm with proven intelligent manufacturing abilities is proposed to solve real-time interactions. The algorithm is developed to manage and control all robot parameters in real-time. The proposed tool in conjunction with the intelligent manufacturing core incorporates data from the robot manipulators into the industrial big data to manage the factory. The novelty is a communication tool that implements the Industry 4.0 standards to allow communications among the required entities in the complete system. It is achieved by the developed tool and implemented in a real robot and simulation

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“…No literature database has been found with both joints' 3D coordinates and complete inertial data related to upper body movements common in manufacturing. Some previous literature works have already investigated pick and place gestures by studying the effects of cycle time on physical demands [23] and the effects of object horizontal locations on shoulder kinematics [24]. However, these studies are more focused on ergonomic aspects rather than on the analysis of biomechanical features for the development of a prediction algorithm.…”

Section: Introductionmentioning

confidence: 99%

Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications

et al. 2020

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(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications.

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Effect of horizontal pick and place locations on shoulder kinematics

Cited by 7 publications

References 32 publications

Minimization of metabolic energy expenditure in collaborative order picking

Minimization of metabolic energy expenditure in collaborative order picking

Practical Use of Robot Manipulators as Intelligent Manufacturing Systems

Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications

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