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Confirmations are applied in kit preparation for mixed-model assembly to promote quality, but research that explains the impact on time efficiency has been lacking. The purpose of this paper is to determine the extent to which the type of confirmation method relates to time-efficient kit preparation when order batching is applied.

An industrially relevant laboratory experiment is applied, simulating kit preparation with order batching for mixed-model assembly. The time efficiency is studied as associated with four confirmation methods – barcode ring scanner, button presses, voice commands and RFID-reading wristbands – when applied as pick-from and place-to confirmation. Furthermore, the paper also considers the quality outcome.

Efficiency is promoted by methods that minimise interrupting the picker’s motions when performing pick-from confirmations and with methods that allow each hand to place components and perform place-to confirmations simultaneously – here represented by button presses and RFID-reading wristbands. Moreover, combining various methods for the tasks of pick-from or place-to confirmation can benefit efficiency.

Pickers at an early stage of the learning curve (one shift of training) were considered.

The findings promote the customised applications of picking information systems in industry.

Combining various methods for the tasks of pick-from and place-to confirmation can provide more fitting applications that better align with the picker’s preferences.

Combinations of various methods when applied as either pick-from or place-to confirmation, or both, are studied.

Current single head pick and place robots have reached their practical limit for throughput rates due to impractical speeds and acceleration, which often damage or lose the product being transferred. The purpose of this paper is to present a new system which uses 2 XY motion slides and an indexing flexible conveyor to achieve a more desired motion while achieving a high throughput rate.

An innovative robotic pick and place motion design (the FlowBot) was previously created to address the changing needs of the packaging and automation industry. A full patent has been filed covering this technology. This paper documents a refinement to the FlowBot concept that produces a more compact implementation, entitled the Compact FlowBot.

Tit was found that the motion of smaller steps with limited accelerations does produce higher throughputs without the excessive accelerations that Delta robots produce. The robotics system does require limited Z height so the potential for multiple stacked systems is presented.

This novel robot has been found to be a next generation design, which has been confirmed by an international patent search. Many established consumer packaging goods companies and food processing companies have lauded its merits. The system needs to move into prototype and full development mode.

This paper aims to represent a novel framework for optimization of robotic handling from disarray to structure where the products are randomly distributed on a surface, the initial location of the products are known (with the aid of image processing, laser position sensors, etc.) and there is a set of final positions for the products.

Pick‐and‐place is one of the main solutions especially for the food products where the products are prone to damage, have adhesive surfaces and the grippers can be complicated. The aim of this paper is to maximize the utilization of the pick‐and‐place robotic system. In order to do so the handling process is modelled mathematically and the pick‐and‐place problem is formulated based on assignment problem where Hungarian algorithm is utilized to minimize the total distance travelled by the robot. Furthermore, a simulation program is developed to demonstrate the possible improvements of the algorithm in comparison with the existing algorithms.

Utilizing the proposed algorithm can significantly increase the utilization of robots in the pick‐and‐place operation.

The new optimization algorithm can be applied to any industry with pick‐and‐place where time efficiency and maximum utilization matters.

The purpose of this paper is to give a comprehensive solution method for the manipulation of parts with complex geometries arriving in bulk into a robotic assembly cell. As bin-picking applications are still not reliable in intricate workcells, first, the problem is transformed to a semi-structured pick-and-place application, then by collecting and organizing the required process planning steps, a methodology is formed to achieve reliable factory applications even in crowded assembly cell environments.

The process planning steps are separated into offline precomputation and online planning. The offline phase focuses on preparing the operation and reducing the online computational burdens. During the online phase, the parts laying in a semi-structured arrangement are first recognized and localized based on their stable equilibrium using two-dimensional vision. Then, the picking sequence and corresponding collision-free robot trajectories are planned and optimized.

The proposed method was evaluated in a geometrically complex experimental workcell, where it ensured precise, collision-free operation. Moreover, the applied planning processes could significantly reduce the execution time compared to heuristic approaches.

The methodology can be further generalized by considering multiple part types and grasping modes. Additionally, the automation of grasp planning and the enhancement of part localization, sequence planning and path smoothing with more advanced solutions are further research directions.

The paper proposes a novel methodology that combines geometrical computations, image processing and combinatorial optimization, adapted to the requirements of flexible pick-and-place applications. The methodology covers each required planning step to reach reliable and more efficient operation.

Pick-and-place tasks are common across many industrial sectors, and many rigid-linked robots have been proposed for this application. This paper aims to alternatively present the development of a continuum robot for low-load medium-speed pick-and-place tasks.

An inversion of a previously proposed dual continuum mechanism, as a key design element, was used to realize the horizontal movements of the CurviPicker’s end effector. A flexible shaft was inserted to realize rotation and translation about a vertical axis. The design concept, kinematics, system descriptions and proof-of-concept experimental characterizations are elaborated.

Experimental characterizations show that the CurviPicker can achieve satisfactory accuracy after motion calibration. The CurviPicker is easy to control due to its simple kinematics, while its structural compliance makes it safe to work with, as well as less sensitive to possible target picking position errors to avoid damaging itself or the to-be-picked objects.

The vertical translation of the CurviPicker is currently realized by moving the flexible shaft. Insertion of the flexible shaft introduces possible disturbances. It is desired to explore other form of variations to use structural deformation to realize the vertical translation.

The proposed CurviPicker realizes the Schöenflies motions via a simple structure. Such a robot can be used to increase robot presence and automation in small businesses for low-load medium-speed pick-and-place tasks.

To the best of the authors’ knowledge, the CurviPicker is the first continuum robot designed and constructed for pick-and-place tasks. The originality stems from the concept, kinematics, development and proof-of-concept experimental characterizations of the CurviPicker.

The spring‐assisted gantry robots, described in this paper, were designed primarily for the rapid transfer of lightweight objects from one point to another, e.g. to pick objects from a conveyor belt and to place them in a box. The average amount of work required for pick‐and‐place operations carried out by a conventional gantry robot was decided. Springs were added to conserve the kinetic energy of the main bar, which slides in the X‐direction and the work of the same pick‐and‐place operation was decided. A theoretical study showed that when the spring constant was optimized the required motor work of the spring‐assisted robots were 42–95 percent less than the required work of the conventional robot. The conceptual robot exists in mathematical models in Matlab and SIMULINK.

This paper presents an investigation on the development of different pattern placement strategies in robotic palletisation of box packages in the packaging industry with practical implementations for one, two, four and five block patterns with the aim of improving the operational efficiency in robotic palletisation.

The work involves considering the gripper design and maximum number of picks and various process parameters that affect the robotic implementation of pallet patterns and develops a methodology to form different patterns for a given pallet size.

The proposed methodology represents an efficient approach for pallet pattern implementation and results in reduced number of placements required for a given number of boxes per layer and reduced time for palletisation.

The paper introduces a novel technique for pallet loading problem (PLP) considering the physical aspects and restrictions encountered when using the robot and the gripper size to generate the pattern on the pallet. Traditional solutions of PLP do not consider these aspects in pattern placements.

For many years, ABB has been setting the standard for flexible automation in a wide range of industries. Combining pace with precision, the IRB 340 FlexPicker, for example, is opening up new markets as it drastically reduces the cost of pick and place operations. A range of refinements, including a brand new software package dubbed PickMaster, a wash‐down version of the IRB 340 robot and an upgraded controller for unparalleled speed and motion control, now give FlexPicker even more robotic muscle.

Currently, the vision and depth information obtained from the eye-to-hand RGB-D camera can apply to the reconstruction of the three-dimensional (3D) environment for a robotic operation workspace. The reconstructed 3D space contributes to a symmetrical and equal observation view for robots and humans, which can be considered a digital twin (DT) environment. The purpose of this study is to enhance the robot skill in the physical workspace, although the artificial intelligence (AI) technique has high performance of the robotic operation in the known environments.

A multimodal interaction framework is proposed in DT operation environments.

A fast image-based target segmentation technique is combined in the 3D reconstruction of the robotic operation environment from the eye-to-hand camera, thus expediting the 3D DT environment generation without accuracy loss. A multimodal interaction interface is integrated into the DT environment.

The users are supported to operate the virtual objects in the DT environment using speech, mouse and keyboard simultaneously. The humans’ operations in 3D DT virtual space are recorded, and cues are provided for the robot’s operations in practice.

The Industry 4.0 initiative – with its ultimate objective of revolutionizing the supply-chain – putted more emphasis on smart and autonomous systems, creating new opportunities to add flexibility and agility to automatic manufacturing systems. These systems are designed to free people from monotonous and repetitive tasks, enabling them to concentrate in knowledge-based jobs. One of these repetitive functions is the order-picking task which consists of collecting parts from storage (warehouse) and distributing them among the ordering stations. An order-picking system can also pick finished parts from working stations to take them to the warehouse. The purpose of this paper is to present a simplified model of a robotic order-picking system, i.e. a mobile manipulator composed by an automated guided vehicle (AGV), a collaborative robot (cobot) and a robotic hand.

Details about its implementation are also presented. The AGV is needed to safely navigate inside the factory infrastructure, namely, between the warehouse and the working stations located in the shop-floor or elsewhere. For that purpose, an ActiveONE AGV, from Active Space Automation, was selected. The collaborative robot manipulator is used to move parts from/into the mobile platform (feeding the working stations and removing parts for the warehouse). A cobot from Kassow Robots was selected (model KR 810), kindly supplied by partner companies Roboplan (Portugal) and Kassow Robotics (Denmark). An Arduino MKR1000 board was also used to interconnect the user interface, the AGV and the collaborative robot. The graphical user interface was developed in C# using the Microsoft Visual Studio 2019 IDE, taking advantage of this experience in this type of language and programming environment.

The resulting prototype was fully demonstrated in the partner company warehouse (Active Space Automation) and constitutes a possible order-picking solution, which is ready to be integrated into advanced solutions for the factories of the future.

A solution to fully automate the order-picking task at an industrial shop-floor was presented and fully demonstrated. The objective was to design a system that could be easy to use, to adapt to different applications and that could be a basic infrastructure for advanced order-picking systems. The system proved to work very well, executing all the features required for an order-picking system working in an Industry 4.0 scenario where humans and machines must act as co-workers. Although all the system design objectives were accomplished, there are still opportunities to improve and add features to the presented solution. In terms of improvements, a different robotic hand will be used in the final setup, depending on the type of objects that are being required to move. The amount of equipment that is located on-board of the AGV can be significantly reduced, freeing space and lowering the weight that the AGV carries. For example, the controlling computer can be substituted by a single-board-computer without any advantage. Also, the cobot should be equipped with a wrist camera to identify objects and landmark. This would allow the cobot to fully identify the position and orientation of the objects to pick and drop. The wrist camera should also use bin-picking software to fully identify the shape of the objects to pick and also their relative position (if they are randomly located in a box, for example). These features are easy to add to the developed mobile manipulator, as there are a few vision systems in the market (some that integrate with the selected cobot) that can be easily integrated in the solution. Finally, this paper reports a development effort that neglected, for practical reasons, all issues related with certification, safety, training, etc. A future follow-up paper, reporting a practical use-case implementation, will properly address those practical and operational issues.