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dc.contributor.authorHu, Ai-Ping
dc.contributor.authorGrullon, Sergio
dc.contributor.authorZhou, Debao
dc.contributor.authorHolmes, Jonathan
dc.contributor.authorHolcombe, Wiley
dc.contributor.authorDaley, Wayne
dc.contributor.authorMcMurray, Gary
dc.date.accessioned2011-10-14T15:44:55Z
dc.date.available2011-10-14T15:44:55Z
dc.date.issued2009
dc.identifier.citationHu, A.-P., Grullon, S., Zhou, D., Holmes, J., Holcombe, W., Daley, W., & McMurray, G. (2009). “Intelligent Cutting of the Bird Shoulder Joint”. Georgia Poultry Conference, Athens, GA, September 29-30.en_US
dc.identifier.urihttp://hdl.handle.net/1853/41825
dc.descriptionPresented at the 2009 Georgia Poultry Conference, 29-30 September 2009, Athens, Georgia.en_US
dc.description.abstractDeboning operations are one of the largest users of on-line labor in today’s poultry plants. Efforts have been made over the years to automate this function, but to date have achieved only limited success. The main difficulty in this task is its unstructured nature due to the natural variability in the sizes of birds and their deformable bodies. To increase product safety and quality, the industry is looking to robotics to help solve these problems. This research has focused on developing a new method of automating the deboning of bird front halves. If this task can be automated, the technology would naturally be extended to other cuts and trimming operations in poultry and red meat. To accomplish this goal, the project team has been working for the past four years on the development of a sensor-based intelligent cutting system. This work is based on the development of a model for the cutting of bio-materials that can be extended to the cutting of meat, tendon, ligaments, and bone. When this model is combined with data from the tendon prediction system, the nominal cutting trajectory can be established and adjusted based on the cutting model in conjunction with knowledge of the bird's anatomy. The value in accomplishing this work would be to not only reduce labor costs but also to increase the yield of breast meat and reduce/eliminate bone chips. It is estimated that an increase in yield of a single percentage point could represent several millions of dollars of additional revenue for each and every plant. Current attempts at automation of the shoulder cut impose several percentage points of yield loss in return for lower labor costs. In the manual process, while generally providing a higher yield of breast meat, the quality of the product varies dramatically based on the skill of the worker, and the labor costs are significantly higher. It is the goal of this work to develop a system that eliminates labor and consistently provides a yield similar to the best manual worker. The overall vision for this project requires the development of various technology components that will be unified into a single operational system. This includes a system to identify the initial cutting point, a system to specify the nominal cutting trajectory based on the size of that specific bird, a model to predict the location of the joint and shoulder tendons given the position/orientation of the wing tip, a mathematical model of the cutting process that allows the control system to interpret force/torque data and make intelligent motion commands to avoid cutting through the bone, and a robotic platform capable of executing these commands in real-time.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectBird anatomyen_US
dc.subjectCutting forcesen_US
dc.subjectDeboningen_US
dc.subjectDeformationen_US
dc.subjectForce controlen_US
dc.subjectPoultryen_US
dc.subjectRoboten_US
dc.titleIntelligent Cutting of the Bird Shoulder Jointen_US
dc.typePaperen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatenameGeorgia Tech Research Institute


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