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Parallelogram linkages are used to increase the stiffness of manipulators and allow precise control of end-effectors. They help maintain the orientation of connected links when the manipulator changes its position. They are implemented in many palletizing robots connected with binary, ternary and quaternary links through both active and passive joints. This limits the motion of some joints and hence results in relative and negative joint angles when assigning coordinate axes. This study aims to provide a simplified accurate model for manipulators built with parllelogram linkages to ease the kinematics calculations.

This study introduces a simplified model, replacing each parallelogram linkage with a single (binary) link with an active and a passive joint at the ends. This replacement facilitates countering motion while preserving subsequent link orientations. Validation of kinematics is performed on palletizing manipulators from five different OEMs. The validation of Dobot Magician and ABB IRB1410 was carried out in real time and in their control software. Other robots from ABB, Yaskawa, Kuka and Fanuc were validated using control environments and simulators.

The proposed model enables the straightforward derivation of forward kinematics and transforms hybrid robots into equivalent serial-link robots. The model demonstrates high accuracy streamlining the derivation of kinematics.

The proposed model facilitates the use of classical methods like the Denavit–Hartenberg procedure with ease. It not only simplifies kinematics derivation but it also helps in robot control and motion planning within the workspace. The approach can also be implemented to simplify the parallelogram linkages of robots with higher degrees of freedom such as the IRB1410.

The purpose of this paper is to provide a maximum speed algorithm for serial palletizing robots, which guarantees relatively low system modeling requirements and can be easily implemented in actual applications.

Operation speed is an important index of palletizing robots performance. In order to improve it, features of palletizing motions are analyzed, and a refined iterative learning control algorithm for maximum speed optimization is proposed. The refined algorithm learns to increase local speed when the following error does not exceed a predefined tolerance, unlike conventional applications which make actual output identical to its reference. Furthermore, experiments were developed to illustrate the new algorithm's ability to take full advantage of motor capacity, drive ability and repetitive link couplings to improve palletizing efficiency.

Experiments show that motion time decreases more than 20 percent after optimization.

The new iterative control algorithm can be easily applied to any repetitive handling operations where manipulating efficiency matters.

In this industrial case study paper the problem of handling production variations online, i.e. during actual production, is addressed. These variations may occur when it is not possible to exactly guarantee working conditions during a production cycle or between two consecutive cycles. These variations are common in some types of industries, like the glass and ceramic industry, where the products may change slightly during the production cycle. Since it is common to have two or more different model campaigns during a working day, it should be possible to easily parameterize the production system when a new campaign is started. This paper uses a highly‐efficient robotic palletizing system, developed for the partner company Sekurit Saint Gobain (Portugal), to introduce and explain how these problems may be addressed. The paper includes details about practical implementation, along with discussion of options and obtained operational results, showing it to be a good example of human‐machine co‐operation.

This paper aims to propose an automated framework for agile development and simulation of robotic palletizing cells. An automatic offline programming tool, for a variety of robot brands, is also introduced.

This framework, named AdaptPack Studio, offers a custom-built library to assemble virtual models of palletizing cells, quick connect these models by drag and drop, and perform offline programming of robots and factory equipment in short steps.

Simulation and real tests performed showed an improvement in the design, development and operation of robotic palletizing systems. The AdaptPack Studio software was tested and evaluated in a pure simulation case and in a real-world scenario. Results have shown to be concise and accurate, with minor model displacement inaccuracies because of differences between the virtual and real models.

An intuitive drag and drop layout modeling accelerates the design and setup of robotic palletizing cells and automatic offline generation of robot programs. Furthermore, A* based algorithms generate collision-free trajectories, discretized both in the robot joints space and in the Cartesian space. As a consequence, industrial solutions are available for production in record time, increasing the competitiveness of companies using this tool.

The AdaptPack Studio framework includes, on a single package, the possibility to program, simulate and generate the robot code for four different brands of robots. Furthermore, the application is tailored for palletizing applications and specifically includes the components (Building Blocks) of a particular company, which allows a very fast development of new solutions. Furthermore, with the inclusion of the Trajectory Planner, it is possible to automatically develop robot trajectories without collisions.

During the last few years Hawker Siddeley Dynamics Engineering have supplied many Versatran programmable manipulators with the image of the Versatran being primarily that of an industrial robot. It is intended in this paper to demonstrate how practical experience gained by Hawker Siddeley Dynamic Engineering indicates that there are very good reasons for supplying alternatives to a standard robot, for the 500P system of which most people will be familiar, Fig. 1a has in some cases too much capability and in other cases is inadequate. The market therefore has been rather limited for such a robot.

While robots have proved to be ideal for the manipulation of single articles accurately in three dimensions, they are not as cost‐effective in industry for tasks which require simultaneous manipulations of several parts. In addition, changes in part geometry often necessitate the redesign of the gripping device, modification to the sensors and re‐examination of the grasping strategies to be used. These tasks are time consuming and add to the inefficiency of the approach.

The purpose of this paper is to maximize the speed of industrial robots by obtaining the minimum‐time trajectories that satisfy various constraints commonly given in the application of industrial robots.

The method utilizes the dynamic model of the robot manipulators to find the maximum kinematic constraints that are used with conventional trajectory patterns, such as trapezoidal velocity profiles and cubic polynomial functions.

The experimental results demonstrate that the proposed method can decrease the motion times substantially compared with the conventional kinematic method.

Although the method used a dynamic model, the computational burden is minimized by calculating dynamics only at certain points, enabling implementation of the method online. The proposed method is tested on more than 40 different types of robots made by Hyundai Heavy Industries Co. Ltd (HHI). The method is successfully implemented in Hi5, a new generation of HHI robot controller.

The paper shows that the method is computationally very simple compared with other minimum‐time trajectory‐planning methods, thus making it suitable for online implementation.

The food industry is a highly competitive manufacturing area, but with relatively little robotic involvement as compared to the automotive industry. This is due to the fact that food products are highly variable both in shape, sizes and structure which poses a major problem for the development of manipulators for its handling. This paper reviews the current state of development in robot manipulators for the food industry. Three main areas were covered. They are: the handling of non‐rigid food products – the processing of meat, poultry, fish and milking, the harvesting of food products – picking of fruits, asparagus and mushrooms, and the packaging of food products – secondary and tertiary.

At present, the majority of industrial robots are not well suited to the specific needs of the food industry. Additionally, the high cost of robotic systems means that it is currently difficult for food manufacturers to financially justify the use of this technology. This paper aims to examine the unique requirements of the food industry with regards to robot manipulator design and outlines the design features of a low‐cost robotic arm developed specifically for use in food production.

Considerations for the design of the robot arm in addition to industrial requirements for hygienic design, low cost, fast pick and place speed, safety for operation alongside human workers and ease of reprogramming are discussed in detail.

A successful manipulator design must consider functional requirements relevant to food production from the very outset of the design process. The principal three requirements are those of ease of cleaning, speed and low cost.

The availability of low‐cost industrial robots specifically designed for food production might encourage a wider adoption of robotics and automation in the food industry and would benefit food manufacturers by reducing production costs and increasing competitiveness in what is becoming an increasingly difficult market.

This paper is of value to engineers and researchers developing robotic manipulators for use in the food industry.