Description

Mathematical Modeling in Robotics and Artificial Intelligence

Intelligent Robotics, Machine Learning, Computer Vision, and Autonomous Systems (Vol. 2)

Modern robotics has evolved into a multidisciplinary field where Artificial Intelligence, Machine Learning, Computer Vision, Autonomous Navigation, Sensor Fusion, and Mathematical Modeling work together to create intelligent systems capable of learning, adapting, and making autonomous decisions.

Mathematical Modeling in Robotics and Artificial Intelligence: Intelligent Robotics, Machine Learning, Computer Vision, and Autonomous Systems (Vol. 2) continues the journey from Volume 1 by introducing advanced AI-powered robotic technologies used in self-driving vehicles, industrial automation, humanoid robots, smart manufacturing, autonomous drones, healthcare robotics, and intelligent cyber-physical systems.

This volume presents a comprehensive study of trajectory optimization, reinforcement learning, deep neural networks, computer vision, SLAM, LiDAR modeling, autonomous robots, human-robot interaction, predictive maintenance, digital twins, and future AI-driven robotics. Mathematical concepts are combined with practical implementation using MATLAB, Python, ROS, Gazebo, and Webots to help readers bridge theory with real-world robotic applications.

Whether you are an engineering student, AI researcher, robotics developer, automation engineer, or academic professional, this book provides the advanced knowledge required to design, model, simulate, and optimize intelligent robotic systems.

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What You’ll Learn

✔ Advanced Trajectory Generation

✔ Polynomial Trajectories

✔ Cubic Spline Trajectories

✔ Trajectory Optimization

✔ Time Parameterization

✔ Velocity Constraints

✔ Energy Efficient Motion Planning

✔ Deep Learning for Trajectory Tracking

✔ Machine Learning in Robotics

✔ Supervised Learning

✔ Unsupervised Learning

✔ Reinforcement Learning

✔ Motion Prediction

✔ Deep Neural Networks

✔ CNN Applications

✔ RNN Applications

✔ LSTM Networks

✔ Transfer Learning

✔ Deep Reinforcement Learning

✔ Computer Vision

✔ Image Processing

✔ Object Detection

✔ Object Localization

✔ SLAM

✔ 3D Mapping

✔ LiDAR Modeling

✔ Depth Sensors

✔ AI-Based Perception

✔ Human Robot Interaction

✔ Cognitive Robotics

✔ Emotion Recognition

✔ Gesture Recognition

✔ Speech Recognition

✔ Intelligent Robot Behavior

✔ Autonomous Mobile Robots

✔ Wheeled Robot Modeling

✔ Differential Drive Robots

✔ Omni Directional Robots

✔ Sensor Fusion

✔ Self Driving Cars

✔ Humanoid Robotics

✔ Gait Generation

✔ Balance Control

✔ Industrial Robot Dynamics

✔ Predictive Maintenance

✔ MATLAB Robotics

✔ ROS Programming

✔ Gazebo Simulation

✔ Webots Simulation

✔ Digital Twins

✔ Quantum Robotics

✔ Ethical Artificial Intelligence

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Table of Contents

Part IV – Trajectory Generation and Optimization

Chapter 10
Trajectory Generation and Optimization

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Part V – AI Integration in Robotic Modeling

Chapter 11
Machine Learning for Robotics

Chapter 12
Computer Vision and Sensor Modeling

Chapter 13
Human-Robot Interaction and Intelligent Behavior

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Part VI – Advanced Applications and Case Studies

Chapter 14
Autonomous Systems and Mobile Robots

Chapter 15
Humanoid and Industrial Robots

Chapter 16
Simulation, Implementation, and Future Trends

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Who Should Read This Book?

This book is ideal for:

• B.Tech Students
• B.E. Students
• M.Tech Students
• MCA Students
• Robotics Engineering Students
• Mechanical Engineering Students
• Mechatronics Students
• Electrical Engineering Students
• Artificial Intelligence Students
• Machine Learning Engineers
• Robotics Engineers
• Automation Engineers
• Computer Vision Engineers
• Embedded Systems Engineers
• Autonomous Vehicle Developers
• AI Researchers
• University Faculty
• Research Scholars
• Industrial Professionals
• Competitive Examination Aspirants

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Key Features

✅ Advanced AI-Powered Robotics

✅ Mathematical Modeling for Intelligent Systems

✅ Machine Learning and Deep Learning Applications

✅ Computer Vision and Sensor Modeling

✅ Autonomous Navigation Techniques

✅ Human-Robot Interaction

✅ Self-Driving Vehicle Concepts

✅ Humanoid Robot Modeling

✅ Digital Twin Technology

✅ MATLAB, Python, ROS, Gazebo, and Webots Integration

✅ Industrial Automation Case Studies

✅ Research-Oriented and Industry-Relevant Content

✅ Suitable for University Courses, Research, and Professional Development

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Why This Book?

The future of robotics lies in the seamless integration of Artificial Intelligence, Machine Learning, Computer Vision, Autonomous Systems, and Mathematical Modeling. Unlike conventional robotics textbooks, this volume combines mathematical foundations with intelligent decision-making algorithms, enabling readers to understand how modern robots perceive environments, plan trajectories, learn from experience, and interact safely with humans.

From trajectory optimization and reinforcement learning to SLAM, self-driving vehicles, humanoid robotics, and digital twins, this book provides a practical and research-oriented approach to intelligent robotics. It is an essential resource for students, researchers, and professionals seeking expertise in AI-driven robotic systems and next-generation automation technologies.

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Book Details

Title: Mathematical Modeling in Robotics and Artificial Intelligence

Subtitle: Intelligent Robotics, Machine Learning, Computer Vision, and Autonomous Systems

Volume: Vol. 2

Author: Anshuman Mishra

Publisher: Anshuman Mishra

Publication Year: 2025

Language: English

Category: Robotics, Artificial Intelligence, Machine Learning, Computer Vision, Autonomous Systems, Engineering Mathematics