Tactile perception by tissue force characteristics for robotic red eat cutting

Tactile perception by tissue force characteristics for robotic red eat cutting

This research investigates an approach to tactile perception for guiding a cutting tool attached to a robotic system processing red meat. Conventional tactile sensing methods, reliant solely on spatial force values, have met with inconsistent results when addressing the complex cutting conditions in red meat processing. The variability inherent in red meat workpieces, coupled with the deformations induced by processing forces, necessitates an innovative machine perception approach to match the adaptability required in red meat processing tasks. This research explores an alternative approach leveraging temporal sensory data to discriminate meat tissues and tissue interfaces in real-time, thereby informing the trajectory of the cutting tool relative to the position of the deforming meat tissues. The strategy correlates unique characteristic force transients in the force data with predefined key cutting events of the task. While the thesis focuses on developing and validating the tactile perception strategy through experimental setups, it does not extend to full deployment in a robotic system. The methodology has been validated through experimentation using a custom-designed test rig including a 6-axis robotic manipulator, 6-axis force sensor, and high-resolution cameras. The results showed high precision in identifying unique force transients in the data and the key cutting moments in the performed task relative to the cutting tissues and tissue interfaces involved, which were consistent across cuts on comparable tissue arrangements. These principles are relevant across trimming and separation operations, where following tissue interfaces that are not visible during the operation is necessary. The forces exerted at the cutting edge of the knife indicate when the knife is approaching an interface, while the orthogonal side forces detect the behaviour of the deformable meat tissues causing the knife to deviate from a predefined cutting path. The results have enabled the proposal of a simplified machine perception strategy for trimming striploin steak by cutting relative to the real-time position of tissues and tissue interfaces. The investigation has produced new understanding and knowledge on guiding cutting in meat along tissue interfaces, using correct interpretation of force feedback to formulate judgment and cutting strategy ready to be executed. The proposed 'skilled robot system' aims to replicate human operator adaptability for various cutting tasks.

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