Robotics in Weaponry using Machine Learning and Engineering

Saurav Mallik, Sandeep Kumar Mathivanan, Basu Dev Shivahare, Sangeetha S.K.B, Prabhu Jayagopal, Somenath Chakraborty

• Publisher: CRC Press
• Year: 2026
• Print ISBN:
  9781041074441
• Edition: 1
• eBook ISBN:
  9781040943755
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

The integration ML with robotics and weaponry is revolutionizing mechanical engineering by enabling intelligent systems that can adapt, learn, and operate autonomously. In robotics, ML allows systems to process vast amounts of data from sensors to make real-time decisions. Robots, whether in industrial settings or autonomous vehicles, can navigate environments, recognize objects, and optimize tasks through reinforcement learning algorithms. In military applications, robotics combined with ML enhances autonomous weapon systems. Unmanned aerial vehicles (UAVs) and autonomous ground systems are increasingly utilized for surveillance, targeting, and even combat roles. These systems employ ML to improve target recognition, threat analysis, and adaptive decision-making in dynamic battle environments . This reduces human risk in conflict zones and can lead to more precise operational outcomes. Mechanical engineering plays a critical role in designing the physical systems that enable robotic mobility, structure, and function. Advanced mechanical systems integrate machine learning for predictive maintenance, fault diagnosis, and condition monitoring in weaponry and industrial robotics.

Mechanical engineers design robots with complex actuators, sensors, and control mechanisms that respond to real-time data processed by machine learning algorithms. The combination of robotics, ML, and mechanical engineering is driving the development of next-generation intelligent systems. These innovations not only improve automation but are also crucial for defence systems, manufacturing, and autonomous vehicle technologies. This synergy promises greater efficiency, adaptability, and autonomy in a range of applications.

Key Features:

• Highlights Real-World Applications
• Explores Advanced AI Techniques
• Addresses Ethical and Security Concerns
• Equips Readers with Hands-On Knowledge
• Forecasts Future Technological Trends

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Preface
• Contents
• List of Contributors
• 1 ARMSec: A Reinforcement-Learned Autonomous Robotic Monitoring System for High-Threat Defense Zones
• 2 Robust Autonomous Navigation in Urban Environments Using ML-Augmented Architectures with Multi-Sensor Fusion
• 3 Mechanically Grounded Optimization Framework for Enhanced Robotic Design and Performance
• 4 Revolutionizing Combat Strategy: An Intelligent Weapon System Architecture for Next-Generation Warfare
• 5 Real-Time Data Processing Framework for Combat-Ready Situational Intelligence using Integrated Sensor Networks
• 6 Deep Learning-Driven Target Recognition for Robotic Weaponry Systems: A Neural Network-Based Approach
• 7 Reinforcement Learning for Adaptive Weapon Navigation and Control in Autonomous Robotic Systems
• 8 Intelligent Robotic Arm Control for Autonomous Weapon Handling Using Deep Reinforcement Learning
• 9 Autonomous Combat Drones and UAV Navigation Using Deep Reinforcement Learning for Target Engagement and Mission Execution
• 10 Deep Reinforcement Learning for Autonomous Ground Vehicle Control in Warfare and Reconnaissance Operations
• 11 DRAGONet: A Deep Reinforcement Learning Framework for Autonomous UAV Navigation in Dynamic and GPS-Denied Environments
• 12 Vision-Aware Path Planning Network (VAPP-Net) for Adaptive Autonomous Navigation in Complex UAV Environments
• 13 DeepFusion-NavNet: A Deep Learning Framework Combining Semantic Segmentation and Reinforcement Learning for Robust Autonomous UAV Navigation
• 14 OptiFlight-Net: A Hybrid Deep Learning and Particle Swarm Optimization Framework for Energy-Efficient and Safe UAV Navigation
• 15 Speed-Adaptive Navigation Network for Real-Time High-Velocity UAV Path Planning with Safety Assurance
• 16 Unmanned Aerial Vehicles and Autonomous Combat Drones
• 17 Machine Learning Fundamentals for Autonomous Systems
• 18 Securing the Future: AI-Powered Weapon Systems, Ethics, and Adversarial Defense
• 19 Evolution of Weapon Systems and Rise of Intelligent Warfare
• 20 Adaptive Control Strategies in Autonomous Vehicles: A Machine Learning Approach
• 21 Visual Monitoring Techniques using AI and Deep Learning for Surveillance and Security
• 22 SARFALS: A Secure and Robust Framework for AI-Driven Autonomous Weapon Systems
• 23 SATNet: A Spatiotemporal AttentionGuided Transformer Network for Robust Surveillance and Real-Time Object Detection
• 24 Adversarial-Aware Transformer-Based Threat Mitigation System for Robotic Defense Units in Battlefield Environments
• 25 Next-Generation Swarm Threat Neutralization: An Intelligent Sensor Fusion and Behavior Prediction System
• 26 An Adversarial-Resilient Multi-Agent AI Framework for Autonomous Robotic Warfare Defense
• Index
• About the Editors