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AI in Mechatronics

Vansh Gehlot* and Prashant Singh Rana

Thapar Institute of Engineering and Technology, Patiala, Punjab, India

Abstract

The chapter “AI in Mechatronics” provides a comprehensive overview of how artificial intelligence (AI) techniques can enhance mechatronic systems; it covers core AI approaches, including machine learning, computer vision, soft computing methods, natural language processing, AI planning, and intelligent system design tools. Key real-world applications across areas like manufacturing automation, robotics, vehicles, and human–machine interaction are analyzed. The integration of data-driven and knowledge-based AI methods can endow mechatronics with advanced capabilities around autonomy, environmental perception, reasoning, control, and human collaboration. This creates intelligent self-optimizing systems that continue learning and adapting. Current challenges around model accuracy, data availability, security, and algorithmic transparency are discussed along with emerging opportunities in embedded intelligence and bio-inspired robotics. Overall, the synergistic fusion of AI and mechatronics promises to transform technological systems across industrial and societal domains by enhancing automation, augmenting human capabilities, and enabling next-generation smart mechanized assistants.

Keywords: Machine learning, human–robot collaboration, computer vision, robotic systems, natural language processing, manufacturing automation, self-driving vehicles

1.1 Introduction to AI Techniques for Mechatronics

Mechatronics refers to the synergistic integration of mechanical, electrical, and computer engineering to design smart systems and products capable of exhibiting both information-processing and physical-action capabilities. It combines the core disciplines of mechanics, electronics, control systems, and computing to create high-performance systems with built-in intelligence.

Although mechatronics has its origins in industrial robotics and manufacturing automation, it has grown tremendously in scope and adoption over the past few decades. Today, mechatronic systems and principles are utilized in a diverse range of applications including automotive systems, aerospace systems, industrial production systems, defense systems, material handling systems, healthcare systems, and consumer product design. Figure 1.1 shows the flowchart depicting the design methodology for mechatronics systems, mechatronics integrates various engineering disciplines.

Modern mechatronic systems incorporate sensors, actuators, microprocessors, control systems, and mathematical modeling into product design. This facilitates features like automation, precision, efficiency, adaptability, reconfigurability, safety, reliability, and productivity. For example, an automated robotic manufacturing system may use servo motors and drives for motion control, various sensors for inspection and feedback, PLCs or microcontrollers for regulated actuation and monitoring, and sophisticated control software for supervision, analysis, and coordination [1].

With rapid advances in artificial intelligence (AI) and machine learning, integrating AI techniques into mechatronic systems has become an active area of innovation and research. AI can endow mechatronics with higher levels of intelligence, autonomy, perception, and decision-making capabilities to create smart, self-optimizing systems. This introductory chapter provides an overview of promising AI approaches for modern mechatronics along with the key benefits.

1.1.1 Overview of Key AI Approaches

Several AI techniques hold promise for advancing mechatronics.

• Machine Learning: This allows systems to automatically learn and improve from data without being explicitly programmed. Algorithms identify patterns and build models based on training data. Supervised, unsupervised, and reinforcement learning methods can be used for tasks like prediction, classification, and control optimization in mechatronic systems.

  Figure 1.1 Design methodology for a mechatronic system [15].

• Neural Networks: Inspired by biological neurons, artificial neural networks consist of interconnected nodes that process and transmit data signals. The strengths of connections are adjusted during training to build robust models. Types like convolutional and recurrent neural nets are ideal for perception, analysis, and decision-making in mechatronic applications.
• Fuzzy Logic: This enables representing and reasoning with vague, imprecise information using degrees of truth rather than binary logic. Fuzzy rule–based systems can model complex nonlinear behavior for control and inference in mechatronic systems with uncertainty.

1.1.2 Benefits of Integrating AI in Mechatronic Systems

Integrating appropriate AI techniques enhances mechatronic systems with the following. Figure 1.2 depicts a block diagram illustrating various AI approaches in mechatronics, from machine learning to computer vision and soft computing.

• Increased Autonomy: With machine learning, systems can independently analyze data, identify patterns, and make optimal decisions for a given task. This reduces reliance on constant human oversight.
• Improved Adaptability: AI approaches like neural networks allow systems to adapt in real-time to changes in the environment, tasks, and operating conditions. This facilitates reconfiguration without reprogramming.
• Smarter Control: Advanced control strategies enabled by fuzzy logic and reinforcement learning lead to more efficient, stable, and optimal system performance.
• Enhanced Perception: Sensors coupled with AI-based perception and analysis enable mechatronic systems to accurately interpret ambiguous, complex data from the real world.
• Higher-Level Cognition: With neural networks and machine learning, mechatronic systems can demonstrate capabilities like scene understanding, forecasting, diagnosis, and strategy optimization.

Figure 1.2 Different AI approaches in mechatronics.

1.2 Machine Learning for Mechatronic Systems

A wide repertoire of machine learning techniques can impart different types of learning abilities.

Supervised learning algorithms train predictive models by learning from labeled example inputs and expected outputs. Classification techniques like support vector machines enable tasks like image recognition and fault diagnosis. Regression methods like neural networks can model complex control systems and be used for predictive analytics [2].

Unsupervised learning techniques identify hidden patterns and relationships within unlabeled data. Clustering methods can detect anomalies and group similar data points. Dimensionality reduction can be used for simplifying complex high-dimensional data from diverse mechatronic sensors to enable efficient processing. Association rule mining reveals interesting correlations and connections in large datasets.

Reinforcement learning focuses on optimizing sequential decision-making via trial-and-error interactions with a dynamic environment. The system learns an optimal policy for taking actions to maximize cumulative reward. Dynamic programming, Monte Carlo methods, and temporal difference learning are key techniques applicable to the optimization of mechatronic control policies. Figure 1.3 shows the different parts of machine learning.

Key applications of machine learning in mechatronic systems include adaptive control, predictive maintenance, fault diagnosis, sensor fusion, motion planning, forecasting, multidimensional optimization, natural language interfaces, computer vision for inspection and navigation, and noise filtering. Learning capabilities enhance automation, precision, adaptability, productivity, and reliability [3].

Researchers have successfully demonstrated machine learning use for industrial robots, reusable launch vehicles, autonomous cars, smart prosthetics, and advanced manufacturing systems. As computing power grows exponentially, machine learning will become further ingrained in mechatronic system design.

Figure 1.3 Machine learning map.

1.2.1 Supervised, Unsupervised, and Reinforcement Learning Techniques

Supervised Learning: Algorithms like linear regression, logistic regression, decision trees, support vector machines, and neural networks can be trained on labeled input–output data to learn prediction and classification tasks for mechatronic systems like quality evaluation, fault diagnosis, sensor fusion, trajectory modeling, and process optimization. Figure 1.4 illustrates the accuracy of different machine learning models on relevant mechatronics models.

Unsupervised Learning: Algorithms like k-means clustering, hierarchical clustering, principal component analysis, and autoencoders can find hidden patterns and groupings within complex unlabeled mechatronic data. This enables anomaly detection, novel insights, data compression, noise removal, etc., without explicit training.

Reinforcement Learning: Based on dynamic programming techniques like temporal difference and Q-learning, reinforcement learning agents interact with an environment, select actions, and receive rewards or penalties to automatically determine optimal policies, maximizing long-term reward. Applicable to mechatronic control optimization,...