Design and Implementation of a Low-Cost Microcontroller-Based an Industrial Delta Robot

Abstract

This paper presents a practical design and control for a delta robot based on a low-cost microcontroller. The main purpose of the proposed delta robot is to improve and enhance industrial productivity such as fast pick-and-place tasks and fully autonomous production lines. Additionally, during a global pandemic similar to (COVID-19), some medical and food products suffer from a sudden increase and demand. Moreover, kinematics, workspace dynamics analysis took into consideration an optimized approach to achieve a viable yet efficient model representing them. Furthermore, stress analysis and material selection have been applied, targeting to achieve high customizability of the manipulator linages. Taking availability into considerations, most components are available locally for ease of manufacturing. To add a touch of machine vision to the robot, a camera module is mounted in an optimized fashion to optimize the robot's performance and increase its accuracy. Finally, various interchangeable end effectors can be mounted including a magnetic gripper, vacuum suction cup, soft-robotics grippers, and other types to suit our requirements and needs.

Application of Microcontroller-Based Multipath Servos in Industrial Robot Control Systems

“…Te rudder control process is shown in Figure 3. Te servo contains a reference circuit that generates a reference signal with a period of 20 ms and a width of 1.5 ms. Te actuator control signal is a pulse width modulated PWM signal of 0.5 to 2.5 ms, which corresponds to a steering angle of 0 °to 180 ° [6]. If the control pulse signal does not change, the actuator can remain at a certain angle without moving.…”

“…Tis amount of travel is subdivided into intermediate positions, each of which has a drive signal [22]. Subdividing the travel S into N intermediate positions, the response time required from the 45 °position to the frst position and each subsequent position is: T m0 , T m1 , T m2 , ...T mN , setting the response time relationship as in formulas ( 5) and (6).…”

Adaptive Controller with PID, FOPID, and NPID Compensators for Tracking Control of Electric – Wind Vehicle

“…Advanced control techniques such as adaptive control, variable structure control, fuzzy control, and neural networks can be used to solve this challenge [41]. Although the variable structure controller is simple, it is challenging to put into practice [42], [43]. This is due to the risk of an abrupt shift in the control signal, which could cause the system to malfunction [44].…”