Control system framework for robots based on real-time application. : A proposed framework and dynamic sampling rate algorithm (DSRA) application in a robotic system for efficient data collection.

Sammanfattning: Industry 4.0 is associated with the technological complexity of designing frameworks for factory automation. In that case, since the robots are used in factory operations, this paper proposes a framework that can be used for (near) real-time applications (RTA) in the robotic system. Real-time application is a time constraint that works within a time frame, making it essential to set up a high-speed system in data computation and processing. Monitoring sensors are exposed to different physical variables, such as noise and vibration temperature, from the system, which leads to inefficient data and delay in the data computations. Adaptive Sampling Algorithm A.S.A. was used to reduce the amount of data to be computed. A self-adaptive software (i.e., Rainbow framework) was used to implement the algorithm—Hardware-in-the-loop (HiL) simulation technique used in the simulation of A.S.A. The hardware used in this scenario is the Speedgoat real-time target machine. The proposed framework was tested in an implementation scenario where the robot system had high latency, above 10ms, and had to be reduced to 5ms and below. The results showed fewer samples were collected from the test signal after implementing the algorithm. Hence reducing high latency and increasing real-time application in robot systems. In summary, the proposed framework could be used to develop real-time applications in robotic systems for Industry 4.0.