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Engineering, Electrical & Electronic
In the field of artificial intelligence, control systems for mobile robots have undergone significant advancements, particularly within the realm of autonomous learning.
- autonomous navigation
- optimization DQN (Deep Q-Network)
- robotics
1. Introduction
Industries in the present era are confronted with the challenge of staying abreast of technological progress by fostering innovation and investing in the advancement of efficient equipment and work methods. Over recent years, a notable transformation has taken place, driven by the adoption of artificial intelligence (AI) and the implementation of the DQN algorithm. This paradigm shift has given rise to the emergence of intelligent, adaptable, and dynamic manufacturing processes. By integrating AI and DQN into manufacturing operations, several benefits have been realized, including the automation of manufacturing processes, the seamless exchange of real-time data leveraging cloud computing, and the establishment of interconnected cyberphysical systems. Consequently, this integration has paved the way for the development of control algorithms rooted in machine learning, thereby further augmenting the capabilities of manufacturing processes [1].
The implementation of AI- and DQN-powered autonomous systems, sensors, and actuators has revolutionized the manufacturing industry, enabling companies to achieve elevated quality standards, cost reductions, minimized downtime, and increased competitiveness. This integration has not only been successful, but also serves as a testament to the immense potential for further advancements in the creation of intelligent, efficient, and highly productive manufacturing systems in the future [2,3].
In this new era of the industrial revolution, we are witnessing the emergence of various transformative advancements. These include the deployment of autonomous production lines, the adoption of intelligent manufacturing practices, the implementation of efficient and secure data management systems, and the attainment of unparalleled process quality. These developments have given rise to a heightened sense of competitiveness among companies and have opened up new avenues for productivity across diverse sectors of the market. Notably, these advancements are fueled by innovative approaches and the integration of predictive tools that leverage data analysis to facilitate informed decision-making, thereby significantly reducing risks [4,5,6,7].
In this era of industrial transformation, we are currently witnessing a transformation in manufacturing practices driven by the integration of the DQN algorithm. This has led to the emergence of autonomous production lines, intelligent manufacturing practices, effective and protected data handling, and high-quality processes [8,9]. The use of the DQN algorithm for predictive modeling substantially reduces risks by providing accurate and reliable insights into the manufacturing process. By leveraging the power of AI and the DQN algorithm, manufacturing companies can optimize their operations, reduce costs, improve efficiency, and ultimately gain a competitive edge in the market [10,11].
Recently, mostly in globalized countries, statistical methods have been developed and applied for the analysis of data models, because prediction is important when making decisions, which can be risky and can represent a positive or negative change within a manufacturing process [12,13], and the utilization of machine learning systems has emerged as an innovative approach that utilizes statistical techniques to analyze and optimize algorithms. These algorithms were developed based on insights derived from prior outcomes, leading to the emergence of a regression-based learning methodology, whose application focuses on systems being reconfigured in real-time, and this being carried out in such a way that they automatically find the most optimal and efficient way to perform their respective operations [14,15,16].
2. Autonomous Navigation of Robots
Reinforcement learning (RL) is a type of machine learning that involves an agent interacting with an environment to learn the best actions to take in order to maximize a reward signal. RL has been successfully applied in robotics to solve a variety of tasks, including path determination [17,18,19,20]. Path determination in robotics involves finding a safe and efficient path for a robot to follow in order to complete a task. This can be a challenging problem, as robots need to navigate through unknown environments while avoiding obstacles and minimizing the risk of collisions [21,22,23,24,25,26,27,28].
RL can be used to train a robot to determine the best path to take in order to achieve its goals [29,30,31,32]. The robot can be trained by interacting with its environment and receiving feedback in the form of rewards or penalties based on the actions it takes. For example, the robot may receive a reward for successfully navigating to a particular location, while receiving a penalty for colliding with an obstacle [33,34,35,36,37]. Over time, the robot can use the feedback it receives to learn the optimal path to take in different environments. This allows the robot to adapt to changes in its environment and make decisions in real time based on the current situation [38,39,40,41].
Due to technological advances, the research and implementation of robotic systems are in constant development, trying to optimize self-control, leading their system to be based on autonomous operations and intelligent decision making [42,43]. Especially for movement, different control methods have been designed that vary according to their field of application; however, the most used is the predictive model, which is based on generating a decision based on statistics, which in turn uses a large amount of data in industrial environments [44,45,46,47,48,49].
Various investigations [50,51] indicate that mobile robots are extensively utilized in diverse industrial and commercial settings, often replacing human labor. Instances can be found in warehouses and hospitals, where these robots are responsible for material transportation and assisting workers in repetitive tasks that may have adverse effects on their wellbeing [52,53]. Additionally, it is observed that in many domains involving mobile robots, the processing of a substantial amount of information poses a significant challenge. Within the context of machine learning, this refers to the learning process taking place within an environment that encompasses both fixed and mobile obstacles. The navigation tasks of mobile robots typically involve various optimization concepts, such as cost reduction, shorter trajectories, and minimized processing time. However, in complex and unpredictable industrial environments, adaptability to surroundings becomes essential [54,55,56].
The Deep Q-Network (DQN) algorithm is a popular variant of reinforcement learning that has been successfully applied to a wide range of problems, including path determination in robotics [57,58,59]. In DQN, the agent uses a neural network to approximate the optimal action-value function, which maps states to the expected long-term reward for each possible action. The network is trained using a variant of the Q-learning algorithm, where the agent learns from the transitions between states and the rewards received for each action [60,61,62].
To apply DQN to path determination in robotics, the agent must first be trained on a set of sample environments. During training, the agent explores the environment and learns to predict the optimal action to take in each state. The agent’s performance is evaluated based on its ability to navigate to a specified target location while avoiding obstacles [63,64,65,66].
A novel work is presented in [54], where reinforcement learning is applied; it is mentioned that the robot will learn by training the environment in which it is located through a scoring system designed in a Deep Q network, which later will allow it to take the optimal action or strategy to move to its target position while avoiding obstacles.
Also in [67], it is mentioned that laser sensors are used for the autonomous navigation of mobile robots because they have a wide detection range and high precision; in addition to this research, it is detailed that robots need enough information to correctly detect obstacles and map the environment to subsequently carry out correct navigation.
Programming interfaces have been developed for the design and control of open-source applications, owing to the changes in computer systems and the incorporation of robotics into the industry [68,69]. ROS has the stability to program any type of robotic function, either for a manipulator or a mobile robot; in [70], this framework is used to control the entire flow of information through the tools and libraries included in its API, which contains functional packages that facilitate the creation and communication between nodes.
As mentioned in [71], a successful application developed between big data and machine learning can provide different solutions for solving problems in manufacturing industries, especially in the product life cycle and the entire supply chain. Also in this research, a model based on energy saving is presented using machine learning to determine the optimal trajectory for an industrial robot; the keys to this design are based on data collection and the control algorithm.
For mobile robots, autonomous navigation based on machine learning is the key to high-impact technological advancement; several works [17,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88] indicate that industrial robots that have the appropriate sensors and the specific control algorithm can understand the environment in which they are located, to which humans generally do not have access, such as in war zones or nuclear plants.
The research by [89] shows three different control algorithms based on different techniques; in addition to the development of a simulation environment for the training of the mobile robot, a function focused on Q-learning was also introduced to return the reward value according to the records of executions and thus validate the performance of the robot. The Q-values are reported to be real, which turns the neural network into a regression task, allowing for optimization using the squared error loss function. Overall, the combination of reinforcement learning with the DQN algorithm has shown great promise in enabling robots to autonomously navigate through complex environments and find safe and efficient paths to their goals.
This entry is adapted from the peer-reviewed paper 10.3390/app13127202
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