## 2 Literature Review
The focus was on geometric errors using the ideal kinematic model. The algorithm used the Jacobian matrix [2; 9] to form the linear relationship. The experiment was executed under ideal conditions. Recent research has emerged as part of the MANU 602 project in the domain of realtime robot error correction [10]. The group was able to achieve positional accuracy of 0.05 mm by doing realtime error correction. The system is not a generic solution for kinematic calibration for general industrial robots. It is heavily based on FANUC's dynamic path modification (DPM) software package. C-Track was used for the sensor measurements, which is an optical CMM. The system is also dedicatedly performs the error correction locally as there is no evidence for it to work on a busy and open network. The system relied on the DPM, which makes its use specific to Fanuc. Within a theoretical environment, realtime error compensation in 3 DoF has been conducted by Harbin Institute of Technology, China [11]. They analysed the errors using Jacobian and applied BP neural networks to compensate for the errors in 1000 iterations. The experiment was performed in a simulation environment by using the MATLAB Neural Network Toolbox. They were able to achieve an accuracy of 0.08 mm in the simulation environment. Research was carried out at the Dalian University of Technology, China, in 2015 for correcting the positional errors in a 6 DOF industrial robot [12]. The work was executed in a simulation environment and they used the SIASUN SR165C (SIASUN Robot & Automation CO., Ltd, Shenyang, China) robot for kinematics study. They were able to reduce the total position error from 0.541 to 0.07 mm, which is an 87% reduction in the error. For academic dynamic error correction, a study was carried out by a Canadian university, Ecole de technologie superieure, Montreal, for which a Mecademic's Meca500 robot with a 0.5 kg payload was used [13]. The system requires permanent attachment of the external measurement device and was able to achieve circular radial accuracy of about 0.100 mm. The effect of the system for linear paths with different payload robots is unknown. Proportional-integral-derivative (PID) is the basic controller to be used in a control system that can handle the present and past, and can predict future errors based on the information received [14]. The MPC (model predictive control) is well known and used in a variety of industrial applications. For example, it performs very well where speed, time and optimization are the issues. It has been proven in a case study for analysing and maintaining water level, in which MPC is superior to PID when speed and performance are concerned [15]. In the study, it showed PID loss with 5.5% more packet delays as compared to MPC, and the difference increased over time. The packet delays/communication issuesaffect robot performance by introducing delay in the overall process. If the robot is waiting for the error to come back and is static during that period, the static period can cost the factory line in both performance and expense. Hence it is important to compensate for the delays. Most of the literature presented above dealt with industrial robots and related areas. The error correction has been widely researched, but mostly carried out with an expensive metrology device dedicated to operational use. The gap here is to combine the realtime error correction with the network so that the resources can be shared amongst other operations, thus significantly reducing cost and increasing flexibility.