Robot manipulators are one of the most widely used mechatronic systems in the industry, whose applications include the assembly of elements, as well as the welding and painting of parts. Due to its great usefulness in the industry, it is very important to study its kinematics, dynamics, and automatic control in engineering careers related to mechatronics and robotics. A characteristic of robot manipulators is that they are usually manufactured with a closed architecture in their automatic control. Once a robot meets its end-of-Life, it is resold, reused, or recycled, which are known as the “3Rs” [
1]. A manipulator is usually classified as unusable equipment when its controller is damaged. The reason is that the cost of its reparation can be expensive. In this case, it could be convenient to propose a low cost methodology to re-manufacture the robot, where its mechanical components can be reused and its control system is redesigned using an open architecture.
In the literature, there are several motion controllers for robot manipulators, some of which are recycled and are employed in experimental educational platforms to validate the theory seen in class. Bomfim et al. [
2] re-manufactured the controller of a robot manipulator for the automotive industry, whose trajectories are designed with the MATLAB and Mach3 programs. Sanfilippo et al. [
3] and Soriano et al. [
4] recycled robotic arms for automation engineering education. The robot in [
3] is useful for student academic training, whose controller cabinet was developed by students using a PLC architecture. On the other hand, the robot in [
4] is built with recycled LEGO pieces, and it is controlled with an Arduino Mega board, which is programmed using the Simulink Support Package for Arduino Hardware. Yen et al. [
5] developed a low-cost collaborative robot that employs a virtual force sensor and stiffness control to safety collision detection and low-precision force control. The authors of [
6,
7,
8] presented educational robot manipulators, whose movements are carried out by means of radio control servomotors that have controllers that cannot be modified. The manipulator in [
6] is operated from a graphical interface, while the robotic arm in [
7] has two cameras to detect, collect, and move objects. On the other hand, Cocota et al. [
8] described the design and development of a 4-DOF manipulator with a low cost of approximately USD 150. Robot manipulators based on Dynamixel servomotors are developed in [
9,
10], where Rivas et al. [
9] presented the control system of a 6-DOF manipulator controlled through the Python software, whereas Kim and Song [
10] designed a mechanism to counterbalance the gravitational torques of a 5-DOF robot arm. Manzoor et al. [
11] developed an experimental platform called AUTAREP, which consists of a robotic arm, model ED7220C, from the ED Corporation. The authors of [
11] replaced the original controller of the manipulator, which has a closed architecture, with a controller manufactured by them. This controller is described in [
12], has an open architecture, is programmed through a graphical user interface (GUI), and it has been implemented as: PID regulator [
13], computed torque controller [
14], and optimal regulator [
15]. On the other hand, sliding mode controllers, an adaptive regulator, and neural networks are, respectively, proposed in [
16,
17,
18,
19,
20,
21], for tracking control of robot manipulators employed for educational and research purposes. In the literature, the development of virtual or simulated robot manipulators is also proposed; for example, the authors of [
22,
23,
24,
25] presented robust controllers validated in simulations with the so-called PUMA 560 robot manipulator. However, these manipulators usually do not contemplate friction and backlash, which are present in a real manipulator and cause tracking errors, limit cycles, and other problems that directly affect the manipulator’s motion control.
This article presents an experimental educational platform based on a recycled 4-DOF robotic arm with gripper, which is employed to teach and study its kinematics, dynamics, and automatic control. The recycled robot reuses the mechanical parts and motors of a manipulator from ED Corporation, model ED7220C, whose controller was damaged. Since its repair cost is high, a in-house design is considered. The proposed experimental platform is an upgraded version of our first work described in [
26] to which several capabilities has been added such as: a force sensor inside the robot gripper to detect objects; artificial vision to locate objects and to pick them up according to its shape and color; an anti-windup technique to a PID controller to improve transient response of the movements; a fine tuning of the controller gains to reduce tracking errors; a graphical interface to interact with a user, and trajectory planning to avoid the collision of the robot with objects. All the programming of the recycled robot is carried-out in MATLAB Simulink. Its motion controller has a decentralized scheme that does not take into account the robot dynamics, and it is applied to five direct current (dc) motors coupled to the robot joints, whose positions are detected by the optical encoders. A parameter identification methodology based on the Recursive Least Squares method is also designed to estimate the parameters of the dc motors, which are subsequently employed to design their controllers and state observers that estimate the joint velocities. Data acquisition of the encoders is realized by two Arduino Mega boards. The communication between these boards and MATLAB-Simulink is carried out in real-time using the open-source ARDUINO IO Toolbox [
27]. The proposed controller has the advantage that it is programmed with a visual environment based on block diagrams that has a higher level of abstraction than the programming language used in the AUTAREP platform [
11]. Furthermore, in comparison with the on by Soriano et al. [
4], the proposed motion controller is developed with a Simulink toolbox that reads encoder signals, thus simplifying their acquisition. Programming the controller and the artificial vision in Simulink has the advantage of monitoring all signals of the controller by means of scopes, and of using blocks that facilitate the design of other control algorithms such as robust, optimal, adaptable, fuzzy, and neural networks, among others. It is worth mentioning that the proposed experimental educational platform is a key element of the Robotics Laboratory of the Faculty of Mechanical and Electrical Engineering (FIME) at the Universidad de Colima in Mexico, where undergraduate students validate the theory seen in courses of robotics and automatic control, and they also use the robot for research purposes. For example, it was used by three undergraduate students during their final degree projects, whose achievements are reflected in this manuscript. Similarly, the robot has also been used in internal workshops to motivate students to join and remain at the FIME, as well as to show them the importance of robotics and automatic control.