Overview

Autonomous systems and robotics are entering a new era-one, where machines do not simply follow pre-programmed rules, but instead learn, imagine, and generate behaviours using data-driven models. Generative AI for Autonomous Systems and Robotics: From Synthetic Sensor Data to AI-Generated Behaviours and Missions explores this transformation. As robots evolve from reactive devices to predictive, adaptive, and creative agents, generative models play an essential role in shaping perception, planning, interaction, and safety. These models can imagine trajectories before they are executed, synthesize environments never encountered in reality, and infer human intent from partial signals. This book is designed to make these emerging ideas accessible, connecting foundational concepts with practical applications across perception, motion planning, simulation, coordination, and safety. Each chapter builds on the previous to provide a cohesive understanding of the generative tools that are redefining the robotics stack. Without assuming advanced mathematical or machine learning expertise, the content explains how synthetic sensor streams, world models, trajectory generators, digital twins, and swarm behaviour models fit within modern autonomous systems. The goal is not only to introduce the techniques, but also to emphasise responsible deployment, highlighting trust, transparency, risk, and safety at every stage. Whether you are a student, practitioner, researcher, or robotics enthusiast, this book offers a grounded and intuitive roadmap for building the next generation of intelligent, generative, and resilient autonomous systems.

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