# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 6 Discussion
#### Practical Applications
In [39], they indicate that the robotic arm is a device consisting of a movable chain of links connected by joints, which can perform repetitive operations at speeds and with precision that far exceed human operators. They are used to enhance the quality and efficiency of the manufacturing process in the industry, generally for assembly, painting, welding, and pick-and-place operations of parts, among others. However, one of the most challenging problems in completing these tasks is determining the target location of the end effector of the robotic arm. Based on the visual servoing algorithm, the study uses inverse kinematics to execute the pick-and-place operation. First, an object recognition algorithm is implemented to identify the object to be grasped, and then an obstacle avoidance algorithm is developed. The results of the study show that good system performance has been achieved with the three algorithms: first, the object recognition algorithm; second, the obstacle avoidance algorithm; and finally, the pick-and-place operation based on visual servoing. It concludes that the visual servoing algorithm of the robotic arm is suitable for pick-and-place applications. In [40], they indicate that their study focuses on designing an automatic system for an automotive company in China that allows for high-precision localization and grasping using a proprietary robotic arm guided by monocular vision, meeting the efficiency and precision requirements of automatic gripping of the robotic arm. Furthermore, they indicate that traditional robotic arms can only move according to a planned trajectory and cannot obtain external information, which limits their range of application. To enhance the flexibility of robotic arms, control technologies utilizing visual information obtained from cameras have been developed in recent decades. The authors propose a nonlinear multiparallel surface calibration method based on an adaptive k-segment master curve algorithm, which improves the efficiency of a traditional camera calibration algorithm in calibration accuracy, providing an intelligent, robust, flexible, low-cost, and non-invasive selection system that allows for the precise detection and location of flat objects randomly placed on a conveyorbelt using a single camera. The technology has been successfully applied to a gripping and welding system for automobile parts with FANUC and ABB robots. It can be noted that most of the investigated works require previously simulating control strategies for the robotic arm oriented toward industry, medicine, agriculture, pharmaceuticals, mining, and others, in order to obtain a safe and rapid way to evaluate the robot's behavior. Additionally, the possibility of implementing innovative control technologies in an industrial proprietary robot is a case of application with high demand; the use of visual information obtained from cameras to generate efficient and precise trajectories associated with the movements of a robotic arm in the detection and manipulation of objects is highlighted [38; 40]. It is evident that all areas rely on automation and robotics, making it essential to invest in research on robots with varying DoF, depending on the application.