# Advanced Applications of Industrial Robotics: New Trends and Possibilities
## 3 Recent Achievements in Industrial Robotics Classified according to Implementation Area
### Food Industry
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**Objective** & **Technology** & **Approach** & **Improvement** & **Ref.** \\ \hline A comparative study of robot pose optimisation using static and dynamic stiffness models for different cutting scenarios. & KUKA KR 500-3 industrial robot, aluminium 6061 & Complete pose (CP) and the decoupled partial pose (DPP) methods. & A dynamic model-based robot pose optimisation & \\  & & Complete pose (CP) and the decoupled partial pose (DPP) & yields significant improvement over a static model-based optimisation for cutting conditions where the time-varying cutting forces approach the robot’s natural frequencies. A static model-based optimisation is sufficient when the frequency content of the cutting forces is not close to the robot’s natural frequencies. & \\ \hline The feasibility and validity of proposed stiffness identification and configuration optimisation methods. & KUKA KR500 industrial robot & Robot stiffness characteristics and optimisation methods. Point selection method & The smooth processing strategy improves optimization efficiency, ensuring minimal stiffness loss. According to the machining results of a cylinder head of a vehicle engine, the milling quality was improved obviously after the configuration optimisation, and the validity of these methods are verified. & \\ \hline Real-time compensation setup. & A standard KUKA KR120R2500 PRO industrial end-effector & Real-time Closed Loop Compensation & Real-time metrology feedback cannot fully compensate for the sudden error spikes caused by the backlash. The mitigation strategy of automatically reducing feed rate (ASC) was demonstrated to reduce backlash error significantly. However, ASC considerably increases the cycle time for a toolpath involves many direction reversals and leads to uneven cutter clip load and variation in surface finish. Backlash, therefore, remains the largest source of residual error for a robot under real-time metrology compensation. & Real-time metrology feedback cannot fully compensate for the sudden error spikes caused by the backlash. The mitigation strategy of automatically reducing feed rate (ASC) was demonstrated to reduce backlash error significantly. However, ASC considerably increases the cycle time for a toolpath involves many direction reversals and leads to uneven cutter clip load and variation in surface finish. Backlash, therefore, remains the largest source of residual error for a robot under real-time metrology compensation. & A general ITC model for robotic assembly contains all the information needed for task-level planning; BIM and image-based modeling are used to calibrate robot pose for the unification of the robot coordinate system, construction area, and assembly task, a simple conversion process is presented to convert the 3D placement point coordinates of each brick into the robotic control instructions. In the process of experimental verification, task-level planning can maintain the same accuracy as that of the traditional method but saves time when facing more complex tasks. & A programming model which enables robot assembly programs to be executed in reverse. Temporarily switching the direction of program execution can be an efficient error recovery mechanism. Additional benefits arise from supporting reversibility in robotic assembly automatically derived disassembly sequences. & \\ \hline The control strategies for robotic Fifi assembles and robotic assembly technologies. & Robotic Fifi assembly assembly technologies. & Typical peg-in-hole (PiH) assembly methods & The system outperforms the operator performing the same task with magnified visual feedback regarding both completion time and the number of successful insertions. The proposed strategies can correctly diagnose the assembly process’s position errors and effectively realise error recovery. & \\ \hline An overview of computer vision for preoperative, intraoperative, and postoperative surgical stages to assist with planning, tool detection, identification, pose tracking, and augmented reality, for surgical skill assessment and retrospective analysis of the procedure. & An overview & An overview & An overview & [96] \\ \hline \hline \end{tabular}
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