Project Cycle 2013-2017: Total utilization of raw materials in the supply chain for food with a bio-economical perspective

The CYCLE project aimed at upcycling food industry co-streams in the value chains for pelagic fish, chicken, vegetables and potatoes, where new utilisations may increase benefits and economic profit. Project partners from Norwegian industry participated actively, e.g. to test new technologies for conserving food co-streams for animal feed. E.g., discarded potatoes were compacted and wrapped in plastic to achieve a long shelf-life due to fermentation by lactic acid bacteria. CYCLE was an interdisciplinary project with a bio-economic perspective, studying both marine and agricultural value chains. The project conducted several studies to improve the resource utilization in Norwegian food chains by developing novel technology and innovative, sustainable approaches. CYCLE integrated disciplines such as food safety, sustainability, sensor & automation technology, logistics and animal nutrition. Food co-streams being not appropriate for human food consumption were utilised as feed, fertilizers or energy. Recycling of local resources is of special interest in organic farming, where organic matter and nutrients should circulate in the farming system, producing a surplus to be sold as food. Current linear, industrially designed food models should be replaced by cycling systems. In cooperation with NIBIO, NORSØK lead a work package on feed and fertilisers. Hydrolysation of chicken feathers, and hydrothermal carbonification of residues after anaerobic digestion to produce biochar for soil amendment are two examples of CYCLE activities managed by NIBIO and NORSØK

Case Nortura/Norilia.Improving the utilisation of co-streams in poultry processing

Industrialised chicken production is far from organic agriculture prinicples. Still of interest is a more sustainable utilisation of by-products, e.g. hydrolysation of feathers for proteins, or extraction of food grade oil from chicken bones. Such approaches were studied in the bioeconomy-project "CYCLE" (2013-2017)

Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration

The robotic handling of compliant and deformable food raw materials, characterized by high biological variation, complex geometrical 3D shapes, and mechanical structures and texture, is currently in huge demand in the ocean space, agricultural, and food industries. Many tasks in these industries are performed manually by human operators who, due to the laborious and tedious nature of their tasks, exhibit high variability in execution, with variable outcomes. The introduction of robotic automation for most complex processing tasks has been challenging due to current robot learning policies. A more consistent learning policy involving skilled operators is desired. In this paper, we address the problem of robot learning when presented with inconsistent demonstrations. To this end, we propose a robust learning policy based on Learning from Demonstration (LfD) for robotic grasping of food compliant objects. The approach uses a merging of RGB-D images and tactile data in order to estimate the necessary pose of the gripper, gripper finger configuration and forces exerted on the object in order to achieve effective robot handling. During LfD training, the gripper pose, finger configurations and tactile values for the fingers, as well as RGB-D images are saved. We present an LfD learning policy that automatically removes inconsistent demonstrations, and estimates the teacher's intended policy. The performance of our approach is validated and demonstrated for fragile and compliant food objects with complex 3D shapes. The proposed approach has a vast range of potential applications in the aforementioned industry sectors.Comment: 8 pages, 7 figures,IROS 201

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

Hand-eye calibration enables proper perception of the environment in which a vision guided robot operates. Additionally, it enables the mapping of the scene in the robots frame. Proper hand-eye calibration is crucial when sub-millimetre perceptual accuracy is needed. For example, in robot assisted surgery, a poorly calibrated robot would cause damage to surrounding vital tissues and organs, endangering the life of a patient. A lot of research has gone into ways of accurately calibrating the hand-eye system of a robot with different levels of success, challenges, resource requirements and complexities. As such, academics and industrial practitioners are faced with the challenge of choosing which algorithm meets the implementation requirements based on the identified constraints. This review aims to give a general overview of the strengths and weaknesses of different hand-eye calibration algorithms available to academics and industrial practitioners to make an informed design decision, as well as incite possible areas of research based on the identified challenges. We also discuss different calibration targets which is an important part of the calibration process that is often overlooked in the design process

Control of a Movable Robot Head Using Vision-Based Object Tracking

This paper presents a visual tracking system to support the movement of the robot head for detecting the existence of objects. Object identification and object position estimation were conducted using image-based processing. The movement of the robot head was in four directions namely  to the right, left, top, and bottom of the robot head. Based on the distance of the object, it shifted the object to many points to assess the accuracy of the process of tracking the object. The targeted objects are detected through several processes, namely normalization of RGB images, thresholding, and object marking. The process of tracking the object conducted by the robot head varied in 40 various object points with high accuracy. The further the object’s distance to the robot, the smaller the corner of the movement of the robot produced compared to the movement of the robot head to track an object that was closer even though with the same distance stimulant shift object. However, for the distance and the shift of the same object, the level of accuracy showed almost the same results. The results showed the movement of the robot head to track the object under the head of the robot produced the movement with a larger angular error compared to the movement of the robot head in another direction even though with the stimulant distance of the same object position and the distance shift of the same object

Robotic Platforms for Assistance to People with Disabilities

People with congenital and/or acquired disabilities constitute a great number of dependents today. Robotic platforms to help people with disabilities are being developed with the aim of providing both rehabilitation treatment and assistance to improve their quality of life. A high demand for robotic platforms that provide assistance during rehabilitation is expected because of the health status of the world due to the COVID-19 pandemic. The pandemic has resulted in countries facing major challenges to ensure the health and autonomy of their disabled population. Robotic platforms are necessary to ensure assistance and rehabilitation for disabled people in the current global situation. The capacity of robotic platforms in this area must be continuously improved to benefit the healthcare sector in terms of chronic disease prevention, assistance, and autonomy. For this reason, research about human–robot interaction in these robotic assistance environments must grow and advance because this topic demands sensitive and intelligent robotic platforms that are equipped with complex sensory systems, high handling functionalities, safe control strategies, and intelligent computer vision algorithms. This Special Issue has published eight papers covering recent advances in the field of robotic platforms to assist disabled people in daily or clinical environments. The papers address innovative solutions in this field, including affordable assistive robotics devices, new techniques in computer vision for intelligent and safe human–robot interaction, and advances in mobile manipulators for assistive tasks

The impact of disruptive technology on the manufacturing process, and productivity, in an advanced manufacturing environment.

Doctoral Degree. University of KwaZulu-Natal, Durban.Disruptive technology plays a critical role in the performance of mechatronic systems in an advanced manufacturing environment. Robots were used to perform pick and place task in a virtual manufacturing environment. Newton-Raphson model, renewal theorem and queuing theory were used to model the disruptive technology and develop decision-making algorithms in an advanced process. The motion of the conveyor belt system starved modeled and simulated to determine suitable design parameters that were compatible with the tasks of the pick and place robot. MATLAB and Engineering Equation Solver (EES) were used to determine static solutions and simulated solutions to the pick and place problem in the advanced manufacturing process. The results from the simulations were used to develop suitable task-dependent operational conditions in the advanced manufacturing environment. The simulation results were used to determine the optimal conveyor speeds required for the robotic tasks. Comparing the throughput rate of the developed system with the simulated system indicated that optimal productivity was achieved when the decision-making algorithms were implemented at the early stages of the manufacturing process