Review of Intelligent Control Systems with Robotics

Abstract

The interaction between humans and robots provides several important elements in improving the productivity of the tool in mechanical technology because the development of many complex tasks takes place through the application of multi-use robots technology that is self-adapted in these processes in various industrial or medical applications, which greatly contributed to the development of human life and achieving ways well-off Existing automated control frameworks have shaken up the construction business, making them highly adaptable and easy to use. This paper examines current and upcoming types of control frameworks and their implementation in mechanical technology, and the function of artificial intelligence in applying autonomy. In addition, he is expected to reveal insights into the various issues around control frameworks and the different approaches to fixing them. In addition, it proposes the basics of the application of autonomous control frameworks and various types of control frameworks in mechanical technology. Each type of control framework has its pros and cons which are discussed in this paper. Another type of robot control framework that upgrades the difficulty of the quest stage is man-made brainpower. Part of the speculations used in man-made reasoning, for example, artificial intelligence (AI) such as fuzzy logic, neural network, and genetic algorithm, are shown in this paper. Finally, a part of the joint effort between mechanical autonomy, personnel, and innovation is indicated. Coordinated human effort, for example, kinematic signal recognition used in games and versatile upper arm-based robots used in the clinical field for individuals with disabilities. Later on, it is only natural that the importance of different sensors is built up, thus expanding the knowledge and activity of the robot into a modern field. The data in this paper was collected based on many international references in publishing houses such as Springer, Elsevier, MDPI, IEEE, and others.

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

“…Traditional obstacle avoidance algorithms are invalid when the information of the obstacle is incomplete or completely unknown. The intelligent control needs data or experience to design [51][52]. Reinforcement learning (RL), unlike other artificial intelligence algorithms, is a learning method that does not require any rules [53][54][55][56].…”

Principle Operation of a Line Follower Robot

“…When an obstacle's knowledge is lacking or unknowable, conventional obstacle avoidance algorithms are useless. Designing an intelligent control requires knowledge or experience [38][39]. Contrary to other artificial intelligence algorithms, reinforcement learning (RL) is a learning technique that doesn't need any rules [40][41][42][43].…”

Principle Operation of a Line Follower Robot

“…Designing an intelligent control requires knowledge or experience [54,55]. Contrary to other artificial intelligence algorithms, reinforcement learning (RL) is a learning technique that doesn't need any rules [55][56][57][58]. RL is a machine learning technique that modifies the environment by using the environment's feedback as an input.…”