Research in the International Journal of Computational Vision and Robotics could lead to faster and more accurate robots for high-precision tasks in factories.
Delta robots are parallel computer-controlled machines that have a fixed base and a set of three arms connected to a platform. They are typically used for pick-and-place applications in industries like packaging, assembly, electronics fabrication, pharmaceutical production, and food processing. They can work very quickly, making precise movements for even delicate tasks. Unlike serial robots, the parallel kinematics of delta robots means arms and actuators work together to move the platform.
Riyadh A. Sarhan, Zaid H. Rashid, and Mohammed S. Hassan of the Technical University in Babylon, Iraq, are working to make delta robots even more reliable and have developed a novel control system that boosts their ability to make swift, precise movements. In their paper, they integrate fuzzy logic with an adaptive neuro-fuzzy inference system (ANFIS). This hybrid technology combines the best aspects of artificial neural networks and fuzzy logic to manage the complex kinematics, the mathematical description of the robot’s movements, in order to improve performance significantly.
The improvement in control of precision delta robots should allow manufacturers to increase speed, quality, and overall efficiency on their production lines. Moreover, there is the potential in this hybrid control approach to allow delta robots to be more responsive to and to compensate for changes in their environment.
As industries continue to look for ways to improve automation, the research offers step towards faster, more accurate robotic systems.
Sarhan, R.A., Rashid, Z.H. and Hassan, M.S. (2025) ‘Motion control of 3-DoF delta robot using adaptive neuro fuzzy inference system’, Int. J. Computational Vision and Robotics, Vol. 15, No. 7, pp.1–16.