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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.

Intelligent pneumatic actuator (IPA) is a new generation of actuator developed for Research and Development (R&D) purposes in the academic and industrial fields. The purpose of this paper is to show the application of optical encoder and pressure sensor in IPA, to develop a real-time model similar to the existing devices, and to assess the position control performance using a proportional-integrative (PI) controller and a bang-bang controller in real-time.

A micro optical encoder chip is used to detect cylinder rod position by reading constructed laser stripes on a guide rod, whereas a pressure sensor is used to detect the chamber pressure reading. To control the cylinder movements by manipulating pulse-width modulation (PWM) cycles, two unit valves of two ports and two positions were used. A PI controller and a bang-bang controller are used with suitable gain value to drive the valve using PWM to achieve the target actuator position.

The results show the experimental results of the closed-loop position tracking performance of the system using a data acquisition (DAQ) card over MATLAB software.

This paper presents a real-time model used to replace the microcontroller-based system from previous IPA design. The paper proposes two control strategies, PI and bang-bang, to control position using encoder and pressure reading.

Streamlining automated processes is currently undertaken by developing optimization methods and algorithms for robotic manipulators. This paper aims to present a new approach to improve streamlining of automatic processes. This new approach allows for multiple robotic manipulators commonly found in the industrial environment to handle different scenarios, thus providing a high-flexibility solution to automated processes.

The developed system is based on a spatial discretization methodology capable of describing the surrounding environment of the robot, followed by a novel path-planning algorithm. Gazebo was the simulation engine chosen, and the robotic manipulator used was the Universal Robot 5 (UR5). The proposed system was tested using the premises of two robotic challenges: EuRoC and Amazon Picking Challenge.

The developed system was able to identify and describe the influence of each joint in the Cartesian space, and it was possible to control multiple robotic manipulators safely regardless of any obstacles in a given scene.

This new system was tested in both real and simulated environments, and data collected showed that this new system performed well in real-life scenarios, such as EuRoC and Amazon Picking Challenge.

The new proposed approach can be valuable in the robotics field with applications in various industrial scenarios, as it provides a flexible solution for multiple robotic manipulator path and motion planning.

Development of autonomous robot manipulator for human-robot assembly tasks is a key component to reach high effectiveness. In such tasks, the robot real-time object recognition is crucial. In addition, the need for simple and safe teaching techniques need to be considered, because: small size robot manipulators’ presence in everyday life environments is increasing requiring non-expert operators to teach the robot; and in small size applications, the operator has to teach several different motions in a short time.

For object recognition, the authors propose a deep belief neural network (DBNN)-based approach. The captured camera image is used as the input of the DBNN. The DBNN extracts the object features in the intermediate layers. In addition, the authors developed three teaching systems which utilize iPhone; haptic; and Kinect devices.

The object recognition by DBNN is robust for real-time applications. The robot picks up the object required by the user and places it in the target location. Three developed teaching systems are easy to use by non-experienced subjects, and they show different performance in terms of time to complete the task and accuracy.

The proposed method can ease the use of robot manipulators helping non-experienced users completing different assembly tasks.

This work applies DBNN for object recognition and three intuitive systems for teaching robot manipulators.

Soft robotics is currently a rapidly growing new field of robotics whereby the robots are fundamentally soft and elastically deformable. Fabrication of soft robots is currently challenging and highly time- and labor-intensive. Recent advancements in three-dimensional (3D) printing of soft materials and multi-materials have become the key to enable direct manufacturing of soft robots with sophisticated designs and functions. Hence, this paper aims to review the current 3D printing processes and materials for soft robotics applications, as well as the potentials of 3D printing technologies on 3D printed soft robotics.

The paper reviews the polymer 3D printing techniques and materials that have been used for the development of soft robotics. Current challenges to adopting 3D printing for soft robotics are also discussed. Next, the potentials of 3D printing technologies and the future outlooks of 3D printed soft robotics are presented.

This paper reviews five different 3D printing techniques and commonly used materials. The advantages and disadvantages of each technique for the soft robotic application are evaluated. The typical designs and geometries used by each technique are also summarized. There is an increasing trend of printing shape memory polymers, as well as multiple materials simultaneously using direct ink writing and material jetting techniques to produce robotics with varying stiffness values that range from intrinsically soft and highly compliant to rigid polymers. Although the recent work is done is still limited to experimentation and prototyping of 3D printed soft robotics, additive manufacturing could ultimately be used for the end-use and production of soft robotics.

The paper provides the current trend of how 3D printing techniques and materials are used particularly in the soft robotics application. The potentials of 3D printing technology on the soft robotic applications and the future outlooks of 3D printed soft robotics are also presented.

Practically all salmon fillets produced in Norway are trimmed clean of unwanted fat, bone remnants and other defects according to customer requirements. In today’s modern salmon-processing plants, the trimming operation is performed by a combination of automated trimming machines and manual post-trimming. Manual post-trimming is necessary due to the inability of current trimming machines to obtain satisfactory trimming. The purpose of this paper is to describe the work done so far toward a robotic post-trimming of salmon fillets.

A prototype concept system was developed to explore the possibility of robotic post-trimming. The concept is based on 3D machine vision, a high-speed robot manipulator and a flexible light-weight cutting knife.

The developed prototype demonstrated the feasibility of detecting a pre-defined object to be trimmed in 3D, and performing the specified trimming cut along a 3D cutting trajectory.

The developed prototype system was built and integrated – focusing so far only on a single trimming operation: the tail cut.

The originality in the paper is the description of a prototype integrated system, focused on robotic post-trimming of salmon fillets. The value is in providing a starting point for further development toward a complete robotic post-trimming of salmon fillets.

The purpose of this paper is the increase the flexibility of robots used for handling of 3D (food) objects handling by the development and evaluation of a novel 3D Bernoulli gripper.

A new gripper technology have been designed and evaluated. A deformable surface have been used to enable individual product handling. The lift force generated and the force exerted on the product during gripping is measured using a material tester instrument. Various products are tested with the gripper. A experimental/theoretical approach is used to explain the results.

A deformable surface can be used to generate a lift force using the Bernoulli principle on 3D objects. Using a small forming a significant increase in the lift force generated is recorded. Increasing the forming further was shown to have little or even negative effects. The forces exerted on the product during forming was measured to be sufficiently low to avoid product damage.

To be able to improve the grippers lift strength a better model and understanding of the flow is needed.

A novel Bernoulli gripper for 3D Bernoulli gripping have been designed and evaluated. The gripper enables flexible and delicate handling of various product shapes, 3D as well as 2D. Increased utilization of robots in the food industry can be gained.

The purpose of this paper is to describe a new slaughter line for industrial slaughtering of salmonid fish. Traditionally, slaughtering of farmed salmonids – salmon and rainbow trout – was done manually by bleed cutting with knives. Using the new slaughter line that includes 3D machine vision and a bleed‐cutting robot, slaughtering is almost completely automated – nominally requiring only one person to supervise the line and manually bleed cut the fish not handled by the robot.

The design approach of the salmonid slaughter line focuses on using 3D machine vision and a bleed‐cutting robot with four biaxial pneumatic actuators to handle the slaughtering of pre‐anesthetized salmon and rainbow trout.

Under normal operating conditions, the slaughter line is capable of automatically slaughtering 85‐95 percent of all fish at an average feed rate of 30‐80 salmon/min, and the remaining 5‐15 percent are slaughtered manually. Several issues have been discovered, that should be addressed to improve the slaughter line.

This paper presents a new complete salmonid slaughter line that has reduced the need for manual labor in salmonid slaughtering plants.

This paper aims to target to print functionally gradient materials (FGM) devices made of immiscible polymers in multi-material fused deposition modelling (FDM) systems. The design is intended to improve adhesion of dissimilar thermoplastics without the need for chemical compatibilization so that filaments from many different sources can be used effectively. Therefore, there is a need to invent an alternative solution for printing multiple immiscible polymers in an FDM system with the desired adhesion.

In this study, the authors have developed a bi-extruder for FDM systems which can print two thermoplastics through a single nozzle with a static intermixer to enhance bonding between input materials. The system can also change the composition of extrudates continuously.

The uniqueness of this extruder is in its easy access to the internal channel so that a static intermixer can be inserted, enabling deposition of mechanically interlocked extrudates composed of two immiscible polymers. Without this intermixer, the bi-extruder extrudes with simple side-by-side co-extrusion having no mechanical interlocking. The bi-extruder was characterized by printing objects using pairs of materials including polylactic acid, acrylonitrile butadiene styrene and high impact polystyrene. Microscope images of the cross-sections of the extrudates confirm the ability of this bi-extruder to control the composition as desired. It was also found that the mechanically interlocked extrudates composed of two immiscible polymers substantially reduces adhesion failures within and between filaments.

In this study, the first-ever FDM extruder with a mechanical blending feature next to the nozzle has been designed and used to successfully print FGM objects with improved mechanical properties.

Frictional sliding contact problems between laterally graded orthotropic half-planes and a flat rigid stamp are investigated. The presented study aims at guiding engineering applications in the prediction of the contact response of orthotropic laterally graded members.

The solution procedure is based on a finite element (FE) approach which is conducted with an efficient FE analysis software ANSYS. The spatial gradations of the orthotropic stiffness constants through the horizontal axis are enabled utilizing the homogeneous FE approach. The Augmented Lagrangian contact algorithm is used as an iterative non-linear solution method in the contact analysis.

The accuracy of the proposed FE solution method is approved by using the comparisons of the results with those computed using an analytical technique. The prominent results indicate that the surface contact stresses can be mitigated upon increasing the degree of orthotropy and positive lateral gradations.

One can infer from the literature survey that, the contact mechanics analysis of orthotropic laterally graded materials has not been investigated so far. In this study, an FE method-based computational solution procedure for the aforementioned problem is addressed. The presented study aims at guiding engineering applications in the prediction of the contact response of orthotropic laterally graded members. Additionally, this study provides some useful points related to computational contact mechanics analysis of orthotropic structures.