Performing Department
(N/A)
Non Technical Summary
Background:The poultry industry is the largest meat industry in the world, and the United States is the world's No. 1 producer of poultry meat. The consumption of chicken products has steadily increased in recent decades, and this demand will likely continue into the foreseeable future. While the current poultry industry is centralized and designed to produce food efficiently, several operations such as meat deboning rely heavily on manual labor. The COVID-19 pandemic demonstrated that this reliance on manual labor makes the system vulnerable to disruptions. Manufacturing tasks in these facilities required many workers to stand side-by-side, without the ability to telework or operate equipment remotely. During the pandemic, the infection spread quickly among meat processing workers, disrupting the supply chain. High human-food contact can also lead to cross-contamination resulting in food safety recalls. The poultry and meat industry is currently facing unprecedented challenges of labor shortages and food and worker safety.The meat processing industry stands to benefit by more fully embracing transformative technical principles such as sensing, advanced robotics, and artificial intelligence. That said, the current capabilities of robotics and automation cannot yet compete with the dexterity and flexibility of human workers. Animals are highly variable, requiring intelligent and adaptive automation to handle the soft and variable meat tissues. With the U.S. meat manufacturing industry gradually recovering from the COVID-19 pandemic, now is the opportune time for the meat processing industry to reinvent itself and play a major role in addressing global protein needs, increasing processing efficiency, minimizing meat quality loss, alleviating the pressure of labor force shortage, protecting worker safety, improving worker welfare, and the work environment.Overall goals and objectives:The vision of the Center for Scalable and Intelligent Automation in Poultry Processing (CSI-APP) is to incorporate advanced technologies in robotics, artificial intelligence, digital sensing, biosensing, and food safety to provide U.S. poultry processing industry scalable and intelligent solutions to meet the rising national and global demand in poultry products. The long-term goal focuses on transforming current mass manufacturing protocols in large, centralized processing plants to "mass customization" protocols suitable for processing plants in different scales to overcome the inherent variability associated with raw biological materials and humans. Large-scale individualization can be achieved economically through the integration of digital and physical systems ("industry 4.0" principles). In pursuit of the vision and the long-term goal, CSI-APP will strategically target value creation and technological innovation by performing focused engineering research and extension activities by following four unifying objectives in this proposal:Objective 1: Scalable poultry manufacturing. The team will create ascalable plant-ready intelligent robotic deboning systemcapable of performing at parity with (or even exceeding) human deboners for the most skilled task in the plant: shoulder cutting of front-halves. Artificial intelligence algorithms will be developed to handle the high biological variability of meat.Objective 2: Virtual reality-based workforce transformation. The labor shortage is a major challenge for the meat industry. It takes considerable time to train an individual to perform dexterous jobs like meat deboning. Due to high line speeds in a cold, humid environment, there are injuries resulting in labor shortages. During the pandemic, the infection spread quickly among meat processing workers, disrupting the supply chain. Virtual reality can transform, diversify, and distribute the workforce in space and time. Using the proposed VR technology, someone will be able to stay in a comfortable environment and virtually operate a robot to debone meat in a processing plant remotely. This has the potential to reduce labor shortage and create job opportunities everywhere, including rural areas.Objective 3: Sensor and robotic-based product evaluation and bio-mapping for enhancing food quality and safety. A mobile robotic platform containing biosensors for rapid estimation of bacteria will be developed. The biosensors will provide initial biomapping of bacteria in the processing plant and identify the best areas to collect swab samples of the product and environmental surfaces for food safety evaluations. The final biomap will be used to guide sanitation and management decisions. An imaging system will also be developed for detecting foreign objects like small plastics in meat and food quality evaluation.Objective 4: Research and extension integration: create an innovation ecosystem through technology development/transfer and workforce education. Research and extension activities will be integrated, accelerating the technology transformation to better meet stakeholders' needs. Planned activities include surveys to identify barriers, workshops for disseminating information about advanced technologies, demonstration exhibits at industry conferences, and one-on-one technical support for industries considering implementation of these technologies.Expected Outcomes:CSI-APP is structured to (1) enhance the robustness and scalability of precision manufacturing in meat processing and chicken deboning; (2) distribute the workforce in space and time using virtual reality systems; (3) improve food quality and safety in processing plants using intelligent automation, real-time vision sensing, biosensing and biomapping; and (4) collect stakeholder feedback of digitalization transformation in the meat industry and disseminate the technology to the stakeholders. This contribution will be significant because it is expected to transform the poultry industry to a more digitized and automated industry, with enhanced labor safety and food quality/safety. The scalable and transferrable technology is expected to be adaptable to smaller chicken processors, which is beneficial for the economic development of rural areas. A distributed network of smaller producers/processors that can also supply chickens to local clients efficiently to protect the food supply from aggressive attacks and the spread of pathogens. On fundamental, applied, and extension levels, the long-term outcomes of CSI-APP can be adapted to allied food industries benefiting the U.S. and global economy, but the potential impact of CSI-APP goes far beyond this. Making the mass customization of protein manufacturing a reality will contribute to long-term environmental sustainability in food production and to well-being around the world by providing a safe and affordable source of protein.Project Team:CSI-APP connects four core institutes: University of Arkansas System Division of Agriculture, Georgia Tech Research Institute, University of Nebraska-Lincoln, and Fort Valley State University, along with a key collaborator from USDA ARS National Poultry Research Center. An interdisciplinary team from the four institutions aims to uncover the engineering and technologies to enable scalable, intelligent, efficient, safe, and transformable meat manufacturing systems to enhance worker safety, food safety and process efficiency. CSI-APP's Industrial board consists of 12 representative stakeholders related to the project from (1) poultry companies in large, medium, and small sizes; (2) food manufacturing and automation companies; and (3) industry associationswith backgrounds spanning poultry production, poultry processing, food technologies, and intelligent food system development.
Animal Health Component
40%
Research Effort Categories
Basic
40%
Applied
40%
Developmental
20%
Goals / Objectives
The vision of the Center for Scalable and Intelligent Automation in Poultry Processing (CSI-APP) is to incorporate the advanced technologies in robotics, artificial intelligence, digital sensing, biosensing, and food safety to provide U.S. poultry processing industry scalable and intelligent solutions to meet the rising national and global demand in poultry products. The long-term goal focuses on transforming current mass manufacturing protocols in large, centralized processing plants to "mass customization" scale-neural protocols suitable for processing plants in different scales to overcome the inherent variability associated with raw biological materials and humans, and large-scale individualization can be achieved economically through the integration of digital and physical systems (e.g., "industry 4.0" principles). In pursuit of the vision and the long-term goal, in short-term, CSI-APP will strategically target value creation and technological innovation by performing focused engineering research and extension activities by following four unifying objectives to bond bioproducts, human and sensing/robotics technologies:Objective 1: Scalable poultry manufacturing: lot size of one for robotic processing of chicken carcasses. Specifically, the objective is to create a scalable plant-ready intelligent robotic deboning system capable of performing at parity with (or even exceeding) human deboners for the most skilled task in the plant: shoulder cutting of front-halves. The human benchmark is 35 birds/minute and lost yield of 2% of total yield weight.Objective 2: Virtual reality-based workforce transformation.The objective 2 is to bridge the gap between fully manual and fully autonomous operations by leveraging human intelligence and robotic endurance. To develop and deploy the fully automated system proposed in Objective 1 needs to collect large amounts of human operational data, and requires long-term validation and optimization. To accelerate the data collection and robotic deployment, a VR based human-in-the-loop robotics will be developed in this objective to facilitate necessary steps that will allow for select manual operations within a poultry processing operation to be performed via robots. This VR based robotic system will allow a worker remotely collaborate with robotic devices in order to jointly accomplish processing tasks in a facility. The online human decision making results collected in the VR system will also be used for optimizing the fully automated system in the objective 1.Objective 3: Robots for robots: sensor and robotic-based product evaluation, bio-mapping and decision making in the processing plant to address the new challenges in food quality and safety raised by robotic manufacturing.The goal of Objective 3 is to create new 'robots for robots' protocols to design a new set of robotic and sensor solutions to address the emerging food safety and quality challenges brought by automated meat manufacturing solutions. The related challenges and questions include unknown pathogen transmission patterns, the new requirements of sanitization protocols, potential product quality degradation and the introduction of new FO contaminations. To fill the above gaps, specifically, CSI-APP team proposed a new proactive mobile swab sampling robot platform to collect the environmental surface swab samples with onsite pathogen detection and pathogen transmission pattern visualizations. The outputted quantitative results will also be used for designing and optimizing current sanitization protocols. Additionally, an all-in-one hyperspectral imaging based non-invasive FO contamination detection and food quality evaluation system will be developed and integrated with previous processing lines for online food quality control.Objective 4: Research and extension integration: create an innovation ecosystem through technology development/transfer and workforce education.The Objective 4 is to integrate the research and extension activities and accelerate the technology transformation to better meet the stakeholders needs via activities including interviews, workshops, industry conferences, and demonstration exhibits.
Project Methods
Objective 1: Scalable poultry manufacturing:1) Development of parametric bird physiology models: To achieve accurate and robust lot size of one robotic poultry deboning knife trajectories, it is important to predict the geometric coordinates of non-visible anatomical features of interest (output) from the visible external carcass features (input). The internal anatomical features will be partially modeled off-line from CT scans. The visible features will be collected on-line from dual RGB-D cameras. Machine learning models will be built to map the relationships between inputs and outputs.2) Applying learning from demonstration (LfD) methods to refine knife cutting paths. LfD methods will be incorporated to allow expert human deboners to inform/optimize robot knife paths that achieve maximal yield while avoiding bone chips. Expert users will use the instrumented knife that will contain a microprocessor and an IMU (inertial measurement unit) for measuring knife angular velocity and acceleration. Neural networks will be used to learn the hidden cost function from bird physiology models, using the collected expert data as training sets.3) Research and implementation of feedback control based on knife forces. Closed-loop feedback control based on knife forces will be designed in order to deal with errors and disturbances. The robot knife tool will be instrumented with a dedicated force/torque sensor to detect 3-axis translational and rotational loadings on the blade as deboning is performed. Methods such as model predictive control will be explored.Objective 2:2.1 Build human-robot collaborative interfaces with poultry plant operations utilizing virtual reality environments. This step attempts to comprehensibly model the interface of robotic devices, human operators, and processing plant environments, which will assist to transfer human operators' decisions to transformative robotic movements (deboning and trimming). In detail, the worker will operate the VR system in the well-conditioned controlling room, and the robot arms will the mounted on the processing lines. The VR space and real-world space will be well-calibrated, and the robot utilizing all sensor information will be capable of interpreting human commands and provide automation on a manual labor task. The system robustness under different WIFI bandwidth constraints will be evaluated. 2.2 Adaptation of artificial intelligenceIn objective 1, a LfD method has been proposed where human operations can be tracked and recorded from external IMU sensors. VR system proposed here offers a quicker and easier solution to record human operations. Combining with robotic perception to localize the characterize the object, the machine learning based LfD dataset can be easily established. The graceful integration of LfD and machine learning algorithms into robotic poultry tasks includes three stages utilizing active learning ideas: (1) user performs the task and generates the initial training dataset of input images and user annotations. (2) machine learning models continuously trains on acquired data and starts making predictions that are verified by the user, (3) machine learning approach incorporates uncertainty estimation and only requests user assistance or intervention when having low confidence in its prediction or encountering an unusual situation.Objective 3: Sensor and robotic based product evaluation and bio-mapping3.1 Development of mobile robot platform for swab sample collection:A mobile robotic platform will be developed in the objective to automatically collect meat product and environmental surface swab samples for food safety evaluations. The facility map will be preloaded into the system and the vehicle will move following pre-designated routes and acquire swab samples. The complete mobile robotic platform is expected to run in the processing plant three times a day to timely generate bio-maps to visualize the potential pathogen transmission patterns in poultry processing facilities to guide efforts to minimize biosafety risks.3.2. Biosensor based in-field pathogen detectionThe biosensor will be placed at the wrist of the mobile robot arm. This will allow for on-site rapid and automated screening of Salmonella and Campylobacter, using immunomagnetic separation and quantum dot-based fluorescent sensing. This will generate preliminary biomapping of the facilities.3.3 Biomapping and robotic sanitizationBased on identified hotspots on the preliminary biomap, the mobile robots will collect swabs for traditional microbial enumeration method. The biomapping tool and its outputted results will also be used for developing, optimizing, and validating the sanitization protocols. With the mobile robotic swab platform, the effect of sanitation can be easily and continuously monitored and evaluated.2.4 Hyperspectral-imaged based foreign object (FO) detection and food quality evaluationsAn integrated online hyperspectral imaging system will be established and validated in the subsection for fillet quality evaluation. The system is composed of a detector (900-1700 nm) and broad band illumination source (tungsten-halogen lamps), which will run in the line scanning reflective mode and be integrated following robotic deboning belt. Pixel-level multi-task deep learning model will be integrated in the system for simultaneous food quality evaluation and FO identification. The model will take the pixel spectrum as the input, and the output will be the FO categories and fillet texture properties.Objective 4: Extension4.4.2.1 Semi-structured interviews to collect stakeholder's feedback on CSI-APP innovationsCo-PI McQuillan and her team at UNL will conduct surveys from two key sets of stakeholders: (1) Current poultry processing workers and (2) communities and community members who could develop small CSI-APP facilities with the help of the new center. Similar social science research and extension activities will also be conducted in FVSU to evaluate the automation in action for potential technology transformation to the red meat industry. Besides the social awareness of the stakeholders for the newly developed technologies, the team will also evaluate how robotic technologies could be a relief for front line workers from long-term physical injuries using 3D camera systems.4.2. Organized workshops, conferences, and exhibitsSurveyswill be conducted with two key sets of stakeholders: (1) Current poultry processing workers and (2) Potential for new poultry processing facilities.Educational Workshops: New technologies will be introduced into existing educational workshops, Poultry 101 (twice per year) and Poultry 201 (once per year), A new workshop, Poultry 301, is expected to be launched in year 3 and 4, which will solely focus on new technologies in processing. GTRI will host the International Food Automation Conference (IFAN), which is an event specifically designed to engage engineering and technology decision-makers in the food, poultry, and meat manufacturing sector. GTRI will also host an exhibit booth on the floor of the International Production & Processing Expo (IPPE), which is a large tradeshow that attracts participation from all over the world.