Robotics Science Series

1: Adaptive control: Explores the foundation of adaptive control, adjusting to dynamic systems in real time. 2: Control theory: Introduces fundamental principles of control theory, vital for system stability and performance. 3: Hinfinity methods in control theory: Discusses Hinfinity methods, enhancing robustness in uncertain systems. 4: Lyapunov stability: Examines Lyapunov’s direct method for assessing system stability in nonlinear systems. 5: System identification: Focuses on techniques for identifying system dynamics from inputoutput data for control design. 6: Model predictive control: Covers predictive control methods used in optimizing performance over a finite time horizon. 7: Quantitative feedback theory: Explores feedback systems designed to improve system performance through quantitative measures. 8: Robust control: Looks at designing control systems that are resilient to system uncertainties and disturbances. 9: Advanced process control: Delivers advanced methods for optimizing industrial processes and ensuring control accuracy. 10: Nonlinear control: Discusses control techniques for handling nonlinearities, a crucial aspect in robotics. 11: Hinfinity loopshaping: Focuses on improving system performance by shaping the loop gain using Hinfinity methods. 12: Miroslav Krstić: Highlights Krstić’s contributions to adaptive control, particularly in robust stabilization techniques. 13: Dragoslav D. Šiljak: Investigates Šiljak’s work on stability and robust control, influencing modern control systems. 14: Moving horizon estimation: Introduces a technique used for realtime state estimation in dynamic systems. 15: Wassim Michael Haddad: Discusses Haddad’s influence on stability analysis and robust control in adaptive systems. 16: Linear parametervarying control: Explores control strategies for systems with parameters that vary over time. 17: Nonlinear system identification: Focuses on methods for identifying nonlinear system models for improved control. 18: Multiple models: Delves into the use of multiple models for controlling systems with varying dynamics. 19: Petros A. Ioannou: Investigates Ioannou’s contributions to adaptive and robust control, shaping modern practices. 20: Frank L. Lewis: Explores Lewis’ work in intelligent systems and control, bridging robotics and adaptive control. 21: Control engineering: Provides a comprehensive look at engineering principles for designing and analyzing control systems.

1. Evolutionary Computation: Introduction to evolutioninspired computing models. 2. Genetic Programming: Examines adaptive systems for evolving programs. 3. Genetic Algorithm: Analyzes the power of genetic optimization techniques. 4. Evolutionary Algorithm: Discusses algorithms driven by biological evolution. 5. Bioinspired Computing: Looks at natureinspired computational models. 6. Evolutionary Programming: Explores simulation of evolution in problemsolving. 7. Crossover (Genetic Algorithm): Details gene recombination processes. 8. Mutation (Genetic Algorithm): Reviews mutation’s role in diversity. 9. Chromosome (Genetic Algorithm): Describes genetic data structures. 10. Metaheuristic: Explores frameworks for finding nearoptimal solutions. 11. Evolution Strategy: Investigates adaptive mechanisms for optimization. 12. Effective Fitness: Defines fitness evaluation in evolutionary contexts. 13. Premature Convergence: Warns of early optimization pitfalls. 14. Genetic Representation: Examines data encoding in genetic algorithms. 15. Memetic Algorithm: Covers hybrid algorithms combining genetic and local searches. 16. Humanbased Computation: Reviews human influence in computation. 17. Lateral Computing: Examines lateral interactions in computational systems. 18. Natural Computing: Explores computing grounded in natural processes. 19. Artificial Life: Introduces lifelike systems and their applications. 20. Soft Computing: Investigates flexible, approximate computation methods. 21. Neuroevolution of Augmenting Topologies: Delves into evolving neural networks.

1: Bioinspired robotics: Explores the core principles and motivations behind robotics inspired by nature. 2: Biomimetics: Discusses how designs from nature are replicated in technology to solve engineering challenges. 3: Microbotics: Examines the creation of tiny robots mimicking biological systems for precision tasks. 4: Snakebot: Analyzes the design and function of snakeinspired robots for complex navigational tasks. 5: Dario Floreano: Highlights the contributions of Dario Floreano to the field of bioinspired robotics. 6: Animal locomotion: Investigates the various modes of movement found in the animal kingdom. 7: Robot locomotion: Looks at the techniques and mechanisms used for robot movement and stability. 8: Fish locomotion: Delves into how fish movement principles are applied in robotic designs. 9: Synthetic setae: Explores innovations in robotic adhesion inspired by the natural design of setae. 10: Zero moment point: Discusses the concept crucial for maintaining balance in robotic locomotion. 11: Metin Sitti: Examines Metin Sitti's significant research and advancements in soft robotics. 12: Legged robot: Analyzes the mechanics and design principles behind robots with legs. 13: Neurorobotics: Investigates the integration of neural networks in robotic systems for intelligent behavior. 14: Rhex: Discusses the unique design of Rhex, a robot inspired by insect locomotion. 15: Whegs: Explores the innovative whegs mechanism for enhanced robot mobility over rough terrain. 16: Robotics: Provides an overview of the robotics field, highlighting its evolution and future prospects. 17: Opensource robotics: Examines the impact of opensource platforms on collaborative robotics research. 18: Tactile sensor: Discusses the development of tactile sensors inspired by human touch and its applications. 19: LAURON: Analyzes the design and functionality of LAURON, a biomimetic robot inspired by insect movement. 20: Soft robotics: Explores soft robotics' unique capabilities and potential for versatile applications. 21: Robot fish: Highlights the design and application of robotic fish for environmental monitoring.

1: Autonomous Research Robot: This chapter introduces the core principles of autonomous research robots, laying the foundation for the book. 2: Lidar: Learn how Lidar technology plays a crucial role in navigation and perception for autonomous systems. 3: Autonomous Robot: Delve into the structure and function of autonomous robots, examining key components and their interdependencies. 4: Robotic Mapping: Understand how robots create and interpret maps of their environment for efficient navigation and task completion. 5: Simultaneous Localization and Mapping: Explore the crucial process of simultaneous localization and mapping (SLAM) that allows robots to navigate unknown areas. 6: PatrolBot: A case study of PatrolBot, a robot designed for security applications, demonstrating practical implementation. 7: Unmanned Ground Vehicle: Investigate the design and function of unmanned ground vehicles, emphasizing their military and commercial applications. 8: Stanley (vehicle): Learn about Stanley, the autonomous vehicle that won the 2005 DARPA Grand Challenge, and its engineering breakthroughs. 9: Automated Guided Vehicle: Discover how automated guided vehicles are transforming industries like logistics and manufacturing. 10: Mobile Robot: Explore the evolution of mobile robots and their impact on automation in various fields. 11: Positioning System: Understand the importance of positioning systems in robotics, ensuring precise location tracking for autonomous operations. 12: Player Project: An introduction to the Player Project, which offers software for robot control and simulation. 13: Indoor Positioning System: Learn how indoor positioning systems enhance robots' ability to navigate in complex indoor environments. 14: Robot Navigation: Dive into the algorithms and technologies that allow robots to navigate effectively and autonomously. 15: Webots: Explore Webots, a simulation platform that supports the development and testing of autonomous robots. 16: Mobile Robot Programming Toolkit: Understand the tools and techniques used to program mobile robots, enhancing their autonomy and functionality. 17: Inertial Navigation System: Learn how inertial navigation systems allow robots to maintain accurate positioning without external references. 18: Willow Garage: Explore the contributions of Willow Garage to the development of opensource software and hardware for robotics. 19: CajunBot: A look at CajunBot, a unique robot project with applications in academic research and development. 20: National Robotics Engineering Center: Discover the innovations coming from the National Robotics Engineering Center, a leader in autonomous robot development. 21: Alcherio Martinoli: Learn about the contributions of Alcherio Martinoli to the field of multirobot systems and autonomous research.

1: Robotics: An introduction to the principles and applications of robotics technology. 2: Biomimetics: Exploring how nature inspires robotic designs and solutions. 3: Humanoid robot: An overview of robots designed to mimic human movements and behavior. 4: Swarm robotics: Investigating the collective behavior of multirobot systems. 5: Passive dynamics: Understanding how robots use minimal energy to achieve movement. 6: Mobile robot: Examining various types of robots designed for mobility and navigation. 7: Ballbot: A look at robots that balance on a ball for dynamic movement. 8: Obstacle avoidance: Techniques for robots to navigate through challenging environments. 9: Selfreconfiguring modular robot: Innovative systems that adapt their shape for tasks. 10: Adaptable robotics: The importance of flexibility in robotic systems for varied applications. 11: Agricultural robot: An exploration of robots revolutionizing farming practices. 12: Flower robot: Unique robots inspired by floral structures for various tasks. 13: Tactile sensor: Understanding the role of touch sensors in robotic perception. 14: LAURON: A case study of a quadruped robot developed for realworld applications. 15: Bioinspired robotics: Examining designs inspired by biological systems. 16: Neural control of limb stiffness: Insights into the control mechanisms for robot limbs. 17: Oussama Khatib: Celebrating the contributions of a leader in the robotics field. 18: Cloud robotics: The role of cloud computing in enhancing robotic capabilities. 19: Soft robotics: A look into flexible robots that can adapt to their surroundings. 20: Articulated soft robotics: Understanding the structure and function of soft robotic arms. 21: Continuum robot: Innovative designs allowing for versatile and flexible movement.

1: BEAM robotics: Explore the fundamental principles driving bioinspired autonomous robots. 2: Embedded system: Understand the backbone tech enabling control in complex robotics applications. 3: Mark Tilden: Discover the mind behind BEAM robotics and his revolutionary robotics approach. 4: Behaviorbased robotics: Delve into robots designed to exhibit lifelike behavioral responses. 5: Heliostat: Learn about robotic heliostats and their role in solar energy applications. 6: Solarroller: Study solarpowered BEAM robots with dynamic energyefficient designs. 7: Crawler (BEAM): Analyze BEAM crawlers and their movement inspired by biological organisms. 8: Analog robot: Examine analogcontrolled robots and their streamlined circuitry. 9: Mobile robot: Understand the technology behind autonomous, movementfocused robots. 10: HERO (robot): Get insights into HERO’s role in educational and developmental robotics. 11: Brosl Hasslacher: Uncover the contributions of Brosl Hasslacher to BEAM robotics. 12: Stiquito: Explore Stiquito, the versatile insectlike robot used in educational settings. 13: RS Media: Learn about RS Media, the multimedia robot that brings interactive experiences. 14: Roboquad: Discover Roboquad’s fourlegged design, balancing stability with flexibility. 15: Webots: Dive into Webots, a simulator tool that advances robot research and design. 16: Braitenberg vehicle: Investigate these unique robots that mimic cognitive responses. 17: IISc Guidance, Control and Decision Systems Laboratory: Overview the lab’s pioneering research in autonomous robotics. 18: Elmer and Elsie (robots): Examine the early robot prototypes that led to behaviorbased robotics. 19: Microprocessor: Understand the microprocessor’s crucial role in robotics control and function. 20: Microcontroller: Explore microcontrollers that provide essential computing power for robots. 21: AVR microcontrollers: Review the AVR family, integral to many modern robotics applications.

1: Artificial intelligence: This chapter introduces AI, outlining its evolution and core principles as the cornerstone of robotics. 2: Machine learning: Explores how machines learn from data and improve over time, a crucial component of AIdriven robotics. 3: Symbolic artificial intelligence: Covers symbolic AI's focus on rules and logic, essential for developing reasoning capabilities in robots. 4: Neats and scruffies: Delves into two approaches to AI, comparing structured versus heuristic methods in robotic development. 5: Peter Norvig: Examines Norvig's contributions to AI, focusing on his work in search algorithms and decisionmaking processes. 6: Artificial Intelligence: A Modern Approach: This chapter dives into the textbook by Stuart Russell and Peter Norvig, a key reference for AI practitioners. 7: Stuart J. Russell: Analyzes Russell's influential theories on AI, particularly his work on rational agents in robotics. 8: Artificial general intelligence: Discusses the concept of AGI and its potential to create robots with humanlike cognitive abilities. 9: AI takeover: Investigates the concerns surrounding AI surpassing human intelligence and its implications for robotics. 10: Computational intelligence: Explores the intersection of computation and intelligence, with emphasis on neural networks in robotics. 11: Synthetic intelligence: Looks at the creation of AI through artificial means, advancing the capabilities of robots. 12: Intelligent agent: Defines intelligent agents and how they are designed to operate autonomously in dynamic environments. 13: History of artificial intelligence: Traces the history of AI, providing a context for its current applications in robotics and beyond. 14: Philosophy of artificial intelligence: Discusses the ethical considerations and philosophical debates surrounding AI's role in society. 15: AI winter: Examines the periods of AI stagnation, offering lessons on overcoming obstacles in AI and robotics development. 16: Timeline of artificial intelligence: Provides a chronological account of key AI milestones, offering insights into its growth in robotics. 17: GOFAI: Introduces Good OldFashioned AI, explaining its foundational influence on modern robotic intelligence systems. 18: AI alignment: Discusses the alignment problem, focusing on how AI systems can be designed to align with human values. 19: Supervised learning: Focuses on supervised learning techniques and their application in training robots for specific tasks. 20: Neural network (machine learning): Covers neural networks and their importance in machine learning, with practical applications in robotics. 21: Pattern recognition: Explores pattern recognition techniques used by robots to process sensory data and make decisions.

1: Artificial neural network: Explore the basics and broad significance of neural networks. 2: Perceptron: Understand the building blocks of singlelayer learning models. 3: Jürgen Schmidhuber: Discover the pioneering research behind modern networks. 4: Neuroevolution: Examine genetic approaches to optimizing neural architectures. 5: Recurrent neural network: Investigate networks with memory for sequential data. 6: Feedforward neural network: Analyze networks where data moves in a single direction. 7: Multilayer perceptron: Learn about layered structures enhancing network depth. 8: Quantum neural network: Uncover the potential of quantumassisted learning models. 9: ADALINE: Study adaptive linear neurons for pattern recognition. 10: Echo state network: Explore dynamic reservoir models for temporal data. 11: Spiking neural network: Understand biologically inspired neural systems. 12: Reservoir computing: Dive into specialized networks for timeseries analysis. 13: Long shortterm memory: Master architectures designed to retain information. 14: Types of artificial neural networks: Differentiate between various network models. 15: Deep learning: Grasp the depth and scope of multilayered networks. 16: Learning rule: Explore methods guiding neural model training. 17: Convolutional neural network: Analyze networks tailored for image data. 18: Vanishing gradient problem: Address challenges in network training. 19: Bidirectional recurrent neural networks: Discover models that process data in both directions. 20: Residual neural network: Learn advanced techniques to optimize learning. 21: History of artificial neural networks: Trace the evolution of this transformative field.

1. Developmental robotics: Introduction to robotics inspired by human growth and learning. 2. Domo (robot): Study of Domo, a robot designed for interaction with human environments. 3. Humancentered computing: Exploration of systems that prioritize human interaction and usability. 4. Computational intelligence: Insight into computational models of intelligence in robotics. 5. Cognitive architecture: Examination of the structures enabling robot reasoning and learning. 6. Cognitive robotics: Insights into robots that mimic humanlike perception and problemsolving. 7. Robot learning: Overview of machine learning as applied to autonomous robotic adaptation. 8. Enactivism: Analysis of embodied learning through physical and environmental interaction. 9. Programming by demonstration: Techniques for teaching robots through human action examples. 10. Leonardo (robot): Case study on Leonardo, a robot trained for social learning. 11. Max Planck Institute for Psycholinguistics: Research insights on robotic language processing. 12. Domaingeneral learning: Exploration of robots learning across diverse, unspecific tasks. 13. Infant cognitive development: Comparisons of robotic learning to human infant cognition. 14. Basic science (psychology): Psychological principles foundational to robotic behavior. 15. Morphogenetic robotics: Study of robots that adapt based on biological growth patterns. 16. Evolutionary developmental robotics: Robotics inspired by natural evolutionary processes. 17. Situated approach (artificial intelligence): Contextual AI based on realworld environments. 18. Embodied cognition: Understanding cognition as grounded in physical embodiment. 19. JeanChristophe Baillie: Contributions to robotics and interactive learning systems. 20. Aude Billard: Innovations in social and learningbased robotics applications. 21. Intrinsic motivation (artificial intelligence): Motivationdriven behaviors in autonomous AI.

"Robot Locomotion" offers a comprehensive exploration into the fascinating world of robotic movement and its parallels to the natural world. A vital read for professionals, students, and enthusiasts in robotics and engineering, this book breaks down complex locomotion concepts into digestible sections, offering both theoretical insights and practical applications. Whether you’re an undergraduate, graduate, or hobbyist, this work serves as a valuable resource for deepening your understanding of robot mobility systems. Chapters Brief Overview: Robot locomotion: Introduction to the fundamental principles of robot movement, from basic mechanics to advanced systems. Bipedalism: Explores the challenges and technologies enabling twolegged robots to mimic humanlike walking. Walking: Detailed study of walking mechanisms, including various walking styles and their implementation in robotics. Jumping: Investigates how robots can emulate the dynamics of jumping, with a focus on energy efficiency and agility. Gait: Examines the role of gait patterns in robotic design, including the impact of different gaits on robot efficiency. Flying squirrel: Bioinspired approach, focusing on the jumping and gliding capabilities of the flying squirrel, applicable to robotics. Rectilinear locomotion: Focuses on the study and use of straightline movement, particularly in wheeled and tracked robots. Animal locomotion: A comparison between animal movement and robotic design, exploring natureinspired techniques for efficiency. Fish locomotion: Focuses on how aquatic robots mimic the unique propulsion techniques of fish, perfect for underwater exploration. Flying and gliding animals: Discusses the aerodynamic principles behind the flight and gliding of animals, relevant for aerial robots. Terrestrial locomotion: Covers the mechanics of landbased movement, emphasizing the balance between stability and speed in terrestrial robots. Facultative bipedalism: Investigates animals’ ability to shift between quadrupedalism and bipedalism, with implications for versatile robots. Legged robot: Detailed look at robots with legs, covering the mechanics, algorithms, and design choices that allow for mobility. Origin of avian flight: Delves into the evolutionary history of bird flight and its influence on the development of flying robots. Human skeletal changes due to bipedalism: Discusses how human evolution influenced robot design, particularly for bipedal movement. Leg: Indepth analysis of leg design in robots, focusing on structure and movement optimization for realworld applications. Comparative foot morphology: Explores how different animal foot structures contribute to locomotion, informing robot foot design. Role of skin in locomotion: Examines the function of skin in human and animal movement, influencing soft robotics and material design. Bioinspired robotics: Investigates the growing field of bioinspired robotics, where animal locomotion patterns are used to inform robotic design. Arm swing in human locomotion: Studies the dynamics of arm swing and its effect on human and robot walking efficiency. Walking vehicle: Focuses on vehicles designed to walk, combining principles of robotics and engineering for versatile terrain navigation. By reading "Robot Locomotion," you will not only gain technical knowledge but also insight into how nature influences robotics, creating solutions for a range of realworld challenges. The book’s interdisciplinary approach makes it an essential addition to the libraries of professionals, students, and hobbyists interested in the future of robotics.

1: Behaviorbased robotics: Introduces the principles that guide behavior based systems in robotics. 2: Subsumption architecture: Explores a layered architecture for building complex robotic behaviors. 3: BEAM robotics: Discusses simple, efficient robots designed to mimic biological behaviors. 4: Bioinspired computing: Examines how biological systems inspire computational approaches in robotics. 5: Luc Steels: Highlights contributions to robotics and language evolution from this key researcher. 6: Social simulation: Investigates how social interactions among agents inform behaviorbased designs. 7: Rodney Brooks: Covers the revolutionary ideas brought forth by this pioneer in robotics. 8: Simultaneous localization and mapping: Explains methods for a robot to navigate and map environments. 9: Multiagent system: Discusses systems where multiple robots interact and collaborate. 10: Physical symbol system: Explores how physical entities can manipulate symbols for problemsolving. 11: Modelbased reasoning: Analyzes reasoning processes in robots using internal models of the environment. 12: Intelligent agent: Defines agents capable of autonomous action in dynamic environments. 13: Embodied cognitive science: Connects physical embodiment and cognitive processes in robotics. 14: Nouvelle AI: Introduces new artificial intelligence approaches influencing behaviorbased robotics. 15: Activity recognition: Discusses techniques for robots to recognize and respond to human activities. 16: Apprenticeship learning: Explores how robots can learn from observing others. 17: Situated approach (artificial intelligence): Emphasizes the importance of context in AI decisionmaking. 18: Winnertakeall in action selection: Explains decisionmaking processes in competitive environments. 19: Elmer and Elsie (robots): Case study of specific robots showcasing behaviorbased principles. 20: Symbolic artificial intelligence: Examines the role of symbols in the cognitive abilities of robots. 21: Decentralised system: Discusses the advantages of decentralized architectures in robotic systems.

1: Android Science: Explore the foundational principles and key theories that define android science today. 2: Android (robot): Understand the technical aspects and evolutionary journey of humanoid robots designed to replicate humans. 3: Humanoid Robot: Delve into the unique characteristics and challenges faced by robots designed with humanlike features. 4: Masahiro Mori (roboticist): Learn about the pioneering work of Masahiro Mori and his impact on robotics research. 5: Uncanny Valley: Examine the psychological response to robots that resemble humans but fall short, creating discomfort. 6: Social Robot: Investigate robots designed to interact socially, bridging the gap between humans and machines. 7: David Hanson (robotics designer): Discover David Hanson's role in designing lifelike robots and advancing the field. 8: Developmental Robotics: Focus on how robots learn and adapt over time, mimicking the developmental stages of humans. 9: Actroid: Dive into the Actroid series of robots, known for their realistic appearance and expressions. 10: Social Affordance: Understand how robots can design interactions that encourage human engagement and cooperation. 11: Human–Robot Interaction: Explore the dynamics of how humans and robots communicate and collaborate. 12: Affective Design: Learn how robots are being designed to understand and respond to human emotions. 13: Lucy Suchman: Discover Lucy Suchman’s contribution to understanding the social dynamics of humanrobot interaction. 14: Uncanny: A closer look at the concept of "uncanny" in robotics, and how it impacts human perception. 15: Hiroshi Ishiguro: Study the innovative work of Hiroshi Ishiguro, a leader in creating robots that mirror human behavior. 16: The Media Equation: Understand how humans perceive robots as social actors in media and reallife scenarios. 17: Embodied Cognition: Delve into how the body and the mind work together in the design and interaction of robots. 18: Telenoid R1: Examine the Telenoid R1 robot and its role in emotional and social robotics research. 19: Artificial Empathy: Explore the concept of artificial empathy and how robots might develop the ability to feel and respond. 20: Julie Carpenter: Learn about Julie Carpenter’s research into how humans and robots relate to one another. 21: Robots in Literature: Conclude with a look at the portrayal of robots in literature, highlighting their cultural significance.

1: Computer Vision: This chapter introduces the field of computer vision, discussing how machines process visual data to mimic human vision. 2: Machine Vision: Focuses on industrial applications of vision systems, such as quality control and automation. 3: Image Analysis: Explores techniques for interpreting and manipulating images, from basic transformations to complex segmentation tasks. 4: Optical Flow: Details how optical flow methods are used to track motion in video and images, essential for robotics and animation. 5: Gesture Recognition: Covers the technology behind recognizing human gestures, a key element in humancomputer interaction. 6: 3D Scanning: Discusses methods for capturing threedimensional data of objects, fundamental for virtual reality and modeling. 7: Pose (Computer Vision): Examines algorithms used to determine the position and orientation of objects in 3D space. 8: Stereo Cameras: Explores stereo vision techniques to create 3D depth maps from 2D images, widely used in robotics. 9: Articulated Body Pose Estimation: Investigates methods to estimate body pose, a crucial area in surveillance and interactive technology. 10: Active Vision: Discusses systems that can control their own viewpoint to improve the quality of vision, enabling better decisionmaking in robots. 11: Activity Recognition: Examines how computer vision systems can interpret human activities, applying this to surveillance, healthcare, and more. 12: 3D Reconstruction: Focuses on converting 2D images into 3D models, which is critical for virtual environments and simulations. 13: Structuredlight 3D Scanner: Describes techniques for 3D scanning using structured light, offering high accuracy for detailed models. 14: Visual Odometry: Explains how systems track their own movement through the analysis of visual input, essential in autonomous vehicles. 15: Timeofflight Camera: Introduces this technology, used in depth sensing for applications like robotics and augmented reality. 16: Finger Tracking: Looks at the techniques for tracking finger movements, key to interactive systems and humanrobot interfaces. 17: Chessboard Detection: Explains how computer vision can detect chessboards for camera calibration and feature extraction in robotics. 18: Visual Computing: Discusses the interdisciplinary field combining computer vision and computing, crucial for AI and robotics systems. 19: Smart Camera: Delves into the use of advanced cameras that can process images and make decisions autonomously, paving the way for intelligent systems. 20: Flexible Manufacturing System: Explores the role of computer vision in enhancing flexibility and efficiency in automated manufacturing. 21: InspecVision: Covers the application of computer vision for precision inspection in industrial settings, improving quality control and efficiency.

1: Anthrobotics: An introduction to the concept of anthropomorphic robots and their potential in reshaping industries. 2: Robot: Explores the fundamentals of robotics, including design, function, and their societal roles. 3: Industrial robot: Focuses on the evolution of robots in manufacturing, revolutionizing efficiency and precision. 4: Automation: Discusses the impact of automation on labor, business processes, and the economy. 5: Interactivity: Examines the importance of robots in enhancing humanmachine interaction and collaboration. 6: Service robot: Investigates the use of robots in sectors like healthcare, hospitality, and customer service. 7: Domo (robot): Highlights the role of the Domo robot in personal assistance and caregiving. 8: Robotic arm: Delivers insights into robotic arms' versatile applications, from assembly lines to surgery. 9: History of robots: A historical overview of robotic evolution, tracing its journey from concept to modernday innovation. 10: Anthropomorphism: Explores the human tendency to attribute human traits to robots and its psychological impact. 11: Robotics: A broad exploration of robotics, focusing on technological advances and societal integration. 12: Luis de Miranda: Examines the contributions of Luis de Miranda to the development of humanlike robots. 13: Domestic robot: Discusses the emerging field of domestic robots and their impact on home life. 14: Cobot: Focuses on collaborative robots designed to work alongside humans in various industries. 15: Fourth Industrial Revolution: Explores how robotics plays a pivotal role in this technological transformation. 16: Cloud robotics: Delves into the role of cloud computing in enhancing robotic capabilities and connectivity. 17: Companion robot: Investigates the growing demand for robots designed to offer emotional and psychological support. 18: Track technology: Explains the development of trackbased robots and their role in mobility and logistics. 19: Android (robot): Analyzes the creation of androids and their ability to closely mimic human behavior and appearance. 20: Humanoid robot: Focuses on humanoid robots, emphasizing their potential for work in environments that require humanlike interaction. 21: Three Laws of Robotics: Discusses Asimov’s famous laws, their ethical implications, and modern interpretations.

1: Selfdriving car: Understand the core concepts behind autonomous vehicles. 2: Advanced driverassistance system: Explore the tech supporting autonomous functions. 3: Vehicular automation: Learn about the tiers of automation in modern vehicles. 4: Automatic parking: Discover systems making parking effortless and safe. 5: Waymo: Dive into the journey of a pioneering selfdriving tech company. 6: Mobileye: Uncover the contributions of Mobileye to autonomous vision tech. 7: History of selfdriving cars: Trace the evolution of selfdriving vehicles. 8: Apple car project: Explore Apple's secretive venture into autonomous driving. 9: Robotaxi: Discover the rise and implications of autonomous taxis. 10: Nvidia Drive: Learn about Nvidia's impact on the selfdriving ecosystem. 11: Tesla Autopilot: Examine Tesla’s advancements in semiautonomous driving. 12: Selfdriving car liability: Understand legal considerations of autonomous tech. 13: Cruise (autonomous vehicle): Get insights on GM’s autonomous subsidiary, Cruise. 14: Lane centering: Study a key feature for safe and efficient driving. 15: Selfdriving truck: Explore automation’s role in freight and logistics. 16: Openpilot: Delve into opensource contributions to autonomous driving. 17: Pony.ai: Learn about this innovative autonomous vehicle company. 18: Aurora Innovation: Discover Aurora’s role in autonomous technology. 19: Impact of selfdriving cars: Assess the societal effects of autonomous vehicles. 20: Regulation of selfdriving cars: Examine regulations guiding safe deployment. 21: Automotive safety: Understand the systems ensuring safety in selfdriving tech.

1: Cloud robotics: An introduction to cloud robotics, explaining how cloud infrastructure supports robots' processing and storage capabilities. 2: Client–server model: A detailed look at the clientserver architecture that facilitates communication between robots and cloud servers. 3: Neuromorphic computing: Explores how neuromorphic computing mimics the brain's neural networks, advancing robotic learning and decisionmaking. 4: Simultaneous localization and mapping: Focuses on the integration of cloud computing to optimize realtime robot mapping and localization. 5: Computational intelligence: Delves into computational intelligence techniques used to improve robots' autonomous decisionmaking in cloud environments. 6: Neuroinformatics: Examines the role of neuroinformatics in bridging neural computing and robotics within the cloud. 7: Robot learning: Discusses machine learning strategies for robots, leveraging cloud resources to enhance learning and adaptation. 8: Gregory Dudek: Highlights the contributions of Gregory Dudek to the field of robotics and his influence on cloudbased robotics research. 9: Edge computing: Explores how edge computing is integrated with cloud robotics to process data closer to the source, improving efficiency. 10: Cyber–physical system: An analysis of the cyberphysical systems used in cloud robotics to link physical robots with cloudbased data and software. 11: Cloud computing: Covers cloud computing fundamentals, emphasizing its importance in the development and evolution of cloud robotics. 12: Deep learning: Examines deep learning techniques in robotics, showing how robots use cloudbased deep learning models for enhanced autonomy. 13: Google Brain: A look at how Google Brain contributes to AI and cloudbased robotics, revolutionizing machine learning models for robots. 14: AI accelerator: Explores how AI accelerators power cloud robotics, boosting robots’ capabilities with advanced computational power. 15: Amir Hussain (cognitive scientist): Reviews Amir Hussain’s work on cognitive robotics and how it informs cloud robotics development. 16: Fog robotics: Investigates fog computing and its synergy with cloud robotics to process data and enhance robot performance at the edge. 17: Multitask optimization: Discusses methods for multitask optimization, ensuring that cloud robots efficiently handle complex tasks simultaneously. 18: Aude Billard: Examines Aude Billard's groundbreaking work in robotic learning and its integration with cloud systems for improved robot behavior. 19: Juyang Weng: Highlights Juyang Weng’s contributions to robotics, particularly in cognitive modeling and cloudbased robot intelligence. 20: Cache (computing): Provides insights into cache computing and how caching techniques optimize cloud robotics for better performance. 21: Peertopeer: Concludes with an exploration of peertopeer networking in cloud robotics, enabling decentralized and efficient communication between robots.

1: Unmanned aerial vehicle: Introduction to UAV fundamentals, designs, and applications. 2: AAI RQ7 Shadow: Overview of this key tactical UAV and its mission capabilities. 3: Surveillance aircraft: Insights into UAVs designed for surveillance and monitoring. 4: Unmanned combat aerial vehicle: Analysis of UAVs in military combat roles. 5: Micro air vehicle: Exploration of miniaturized UAVs for specialized uses. 6: AeroVironment: Look into this UAV pioneer and its product innovations. 7: Boeing Insitu MQ27 ScanEagle: Detailed profile of this UAV’s operational features. 8: History of unmanned combat aerial vehicles: Evolution of combat UAVs over time. 9: Elbit Hermes 450: Examination of this tactical UAV’s impact in the field. 10: Prioria Robotics Maveric: Introduction to the unique design of this versatile UAV. 11: DRDO Ghatak: Insights into India’s advanced combat UAV developments. 12: Delivery drone: Overview of UAVs in modern logistics and delivery solutions. 13: Unmanned aircraft system simulation: Importance of simulation in UAV training. 14: Regulation of unmanned aerial vehicles: Discussion on global UAV regulations. 15: Unmanned aerial vehicles in the United States military: Analysis of UAVs in U.S. defense. 16: Autonomous aircraft: Exploration of fully autonomous UAV capabilities. 17: Drones in wildfire management: Role of UAVs in natural disaster response. 18: Aerial base station: How UAVs support communication infrastructure. 19: Veronte Autopilot: Advanced UAV control systems and their applications. 20: AAI RQ2 Pioneer: Profile of this early yet significant tactical UAV. 21: IAI RQ5 Hunter: Examination of UAV legacy and its role in modern warfare.

1: Bayesian network: Delve into the foundational concepts of Bayesian networks and their applications. 2: Statistical model: Explore the framework of statistical models crucial for data interpretation. 3: Likelihood function: Understand the significance of likelihood functions in probabilistic reasoning. 4: Bayesian inference: Learn how Bayesian inference enhances decisionmaking processes with data. 5: Pattern recognition: Investigate methods for recognizing patterns in complex data sets. 6: Sufficient statistic: Discover how sufficient statistics simplify data analysis while retaining information. 7: Gaussian process: Examine Gaussian processes and their role in modeling uncertainty. 8: Posterior probability: Gain insights into calculating posterior probabilities for informed predictions. 9: Graphical model: Understand the structure and utility of graphical models in representing relationships. 10: Prior probability: Study the importance of prior probabilities in Bayesian reasoning. 11: Gibbs sampling: Learn Gibbs sampling techniques for efficient statistical sampling. 12: Maximum a posteriori estimation: Discover MAP estimation as a method for optimizing Bayesian models. 13: Conditional random field: Explore the use of conditional random fields in structured prediction. 14: Dirichletmultinomial distribution: Understand the Dirichletmultinomial distribution in categorical data analysis. 15: Graphical models for protein structure: Investigate applications of graphical models in bioinformatics. 16: Exponential family random graph models: Delve into exponential family random graphs for network analysis. 17: Bernstein–von Mises theorem: Learn the implications of the Bernstein–von Mises theorem in statistics. 18: Bayesian hierarchical modeling: Explore hierarchical models for analyzing complex data structures. 19: Graphoid: Understand the concept of graphoids and their significance in dependency relations. 20: Dependency network (graphical model): Investigate dependency networks in graphical model frameworks. 21: Probabilistic numerics: Examine probabilistic numerics for enhanced computational methods.

1: Bionics: Explores the core concepts, blending biology and robotics for groundbreaking results. 2: Biomedical engineering: Examines the medical applications of bionic systems for human benefit. 3: Biomimetics: Discusses technology inspired by biological systems to solve complex problems. 4: Bioinspired computing: Analyzes computing techniques rooted in natural processes. 5: Janine Benyus: Profiles the biomimicry pioneer and her influence on bionic applications. 6: Biorobotics: Reviews robots mimicking biological functions for enhanced adaptability. 7: Neuroprosthetics: Explores advancements in robotic prosthetics for neural integration. 8: Rahul Sarpeshkar: Highlights this key figure's contributions to bionics and bioengineering. 9: Biological engineering: Examines the crossover of biology and engineering in robotics. 10: Biomaterial: Investigates materials derived from or inspired by biology. 11: Biomimetic material: Focuses on materials designed to mimic biological properties. 12: Cyborg: Looks at the merging of human biology with robotics for enhanced abilities. 13: Bionic (disambiguation): Clarifies the terminology and scope of "bionic" in various fields. 14: Biomimicry Institute: Covers the organization's impact on bioinspired technologies. 15: Werner Nachtigall: Honors the researcher's foundational work in biomimetics. 16: Bioinspired robotics: Discusses robots inspired by biological movements and adaptations. 17: Biomimetic architecture: Reviews architecture influenced by natural forms and systems. 18: Bioinspiration: Highlights diverse applications of biologyinspired design in technology. 19: Bioinspired photonics: Explores photonics inspired by biological visual systems. 20: Biochemical engineering: Discusses biochemical processes applied in robotic functions. 21: Biocompatibility: Addresses how bionics can harmonize with human biology safely.

1: Data mining: This chapter introduces the fundamentals of data mining, focusing on how algorithms and tools are applied to analyze large datasets in robotics. 2: Machine learning: Explores the intersection of data mining and machine learning, demonstrating how models can be trained to recognize patterns and make predictions in robotic systems. 3: Text mining: Delves into text mining, showing how robotic systems can extract useful information from unstructured textual data. 4: Association rule learning: Introduces association rule mining techniques to uncover hidden relationships in data, crucial for improving decisionmaking in robots. 5: Unstructured data: Discusses the challenges and methods for dealing with unstructured data, such as images or audio, in the context of robotics. 6: Concept drift: This chapter explains how machine learning models adapt over time as new data introduces changes, impacting robot performance. 7: Weka (software): Covers the use of Weka, a popular opensource software for data mining, to implement various mining algorithms in robotic applications. 8: Profiling (information science): Focuses on profiling techniques used to understand the behavior of systems and predict future actions, enhancing robotics decisionmaking. 9: Data analysis for fraud detection: Explores how data mining can help robots identify fraud and anomalies in various fields, such as finance or security. 10: ELKI: Provides a deep dive into the ELKI framework, useful for advanced data mining techniques and applied to robotics systems. 11: Educational data mining: Investigates how educational data mining can improve robotassisted learning environments and personalized education. 12: Knowledge extraction: Examines the process of extracting valuable insights from large datasets, guiding robots to make better decisions. 13: Data science: Introduces data science as an integral part of robotics, offering the foundation for building smarter, more capable robots. 14: Massive Online Analysis: Discusses techniques for processing massive datasets in realtime, ensuring robots can adapt to new information instantaneously. 15: Examples of data mining: This chapter presents realworld examples of data mining applications in robotics, showcasing its practical utility. 16: Artificial intelligence: Explores how artificial intelligence integrates with data mining techniques to empower robots with advanced decisionmaking capabilities. 17: Supervised learning: Focuses on supervised learning models and how they are used to train robots for specific tasks through labeled data. 18: Neural network (machine learning): Introduces neural networks and how they mimic human brain functions, essential for advanced robotics and autonomous systems. 19: Pattern recognition: Discusses pattern recognition techniques that allow robots to identify objects, gestures, or speech from raw data. 20: Unsupervised learning: Covers unsupervised learning techniques that allow robots to learn from data without predefined labels, enabling greater autonomy. 21: Training, validation, and test data sets: Explains the crucial role of data sets in evaluating and refining machine learning models, improving robotic accuracy and reliability.

1: Computational neuroscience: Explore the interdisciplinary field of computational neuroscience, examining the role of mathematical models and simulations in understanding neural systems. 2: Neuroscience: Understand the fundamental principles of neuroscience, focusing on brain structure and function, and its relationship with robotics. 3: Bioinspired computing: Discover how biological processes inspire new computational models, contributing to the design of artificial intelligence systems. 4: Neuromorphic computing: Investigate neuromorphic computing, where computing systems are modeled after the brain’s architecture, enabling more efficient processing. 5: Behavioral neuroscience: Learn about how behavior is driven by neural systems, with a focus on decisionmaking and cognitive processes in robotics. 6: Binding problem: Delve into the binding problem, a challenge in neuroscience that addresses how the brain integrates disparate information into a cohesive experience. 7: Christof Koch: Explore the work of Christof Koch and his contributions to understanding consciousness and the brain’s neural processes. 8: Neural network (biology): Examine biological neural networks and their implications for artificial neural network models used in robotics and AI systems. 9: Metastability in the brain: Understand the concept of metastability, describing the brain's ability to remain in multiple states, aiding its adaptability. 10: Neural oscillation: Study neural oscillations and their role in coordinating brain activity, providing insight into brain wave interactions with robotics. 11: Neuroinformatics: Learn about neuroinformatics and its role in data management and analysis of brain activity to model neural processes. 12: David Heeger: Dive into the contributions of David Heeger in understanding brain processing and computational models used in neuroscience. 13: Brain simulation: Gain insights into brain simulation technologies that model the brain’s complexity and their applications in robotics. 14: Models of neural computation: Investigate various models of neural computation, exploring how algorithms mimic brain functions in robotic systems. 15: Dynamical neuroscience: Learn how dynamic systems theory applies to neuroscience, enhancing understanding of brain activity in robotics. 16: Dehaene–Changeux model: Explore the Dehaene–Changeux model of brain functioning, linking cognition with neural circuits in robots. 17: Nervous system network models: Understand how network models of the nervous system contribute to developing more efficient robotic systems. 18: Predictive coding: Discover predictive coding and its relevance in understanding perception, learning, and decisionmaking in both the brain and robotics. 19: Simon Stringer: Explore Simon Stringer’s research in computational neuroscience and its influence on developing braininspired robotic models. 20: Kanaka Rajan: Examine Kanaka Rajan’s work in applying computational neuroscience to develop more robust and adaptive robotic systems. 21: V1 Saliency Hypothesis: Delve into the V1 Saliency Hypothesis, which focuses on how the brain processes visual attention and its implications for robotics and AI.

1: Biorobotics: Introduces the fundamental concept of biorobotics, blending biological processes with robotic systems for enhanced humanmachine interaction. 2: Biomedical engineering: Explores the role of engineering in developing medical devices and technologies that bridge the gap between biology and technology. 3: Prosthesis: Covers the development of artificial limbs and devices that restore lost functionality and improve quality of life for amputees. 4: Cyberware: Discusses the integration of cybernetic technologies to augment or replace human biological systems for enhanced abilities. 5: Synthetic biology: Focuses on the design and construction of new biological parts, systems, and organisms to create innovative solutions for health and environment. 6: Bionics: Explores the application of biological principles in designing mechanical systems that mimic biological processes for human benefit. 7: Gene gun: Details the technology used to introduce foreign DNA into cells, enabling genetic modifications and advances in medical treatments. 8: Neuroprosthetics: Examines the development of devices that interface directly with the nervous system to restore lost sensory or motor functions. 9: Passive dynamics: Looks at how passive components in robotics mimic biological systems, allowing for more efficient and natural movements. 10: Wetware computer: Investigates the concept of using biological materials as computational elements to create advanced, biobased computing systems. 11: Neural engineering: Focuses on the design of technologies that interact with the nervous system to restore or enhance sensory and motor functions. 12: Biomechatronics: Combines mechanical engineering, biology, and electronics to develop devices that integrate seamlessly with the human body. 13: Biomechanical: Examines the mechanical properties of biological systems and how these principles are applied in designing more effective medical devices. 14: Biological engineering: Discusses the engineering techniques used to manipulate biological systems for a range of applications in medicine, agriculture, and environmental sustainability. 15: Hybrot: Introduces hybrid robots, which combine biological and mechanical components, offering new possibilities in robotics and bioengineering. 16: Insert (molecular biology): Explores the role of molecular biology in genetic modification and how these techniques contribute to advancements in robotics. 17: Robotic prosthesis control: Focuses on how robotic prosthetics are controlled, examining the technologies that enable seamless interaction with the user’s nervous system. 18: Hazards of synthetic biology: Investigates the ethical and safety concerns surrounding synthetic biology, including risks of unintended consequences. 19: Biochemical engineering: Explores the principles of biochemical engineering and how they are applied to enhance the functionality and sustainability of biorobotic systems. 20: Biocompatibility: Discusses the critical importance of ensuring that robotic devices are compatible with human biology to minimize rejection or adverse reactions. 21: Organ printing: Examines the emerging field of organ printing, where bioprinting technology is used to create functional organs for medical applications.

1: Cognitive robotics: An introduction to how robots can be designed to simulate human cognitive abilities. 2: Cognitive science: Exploring the interdisciplinary science behind cognition and its role in robotics. 3: Subsumption architecture: Understanding how simple behaviors combine for complex actions in robots. 4: Artificial consciousness: Examining the controversial topic of whether machines can achieve consciousness. 5: Symbolic artificial intelligence: Discussing symbolic AI and its applications in cognitive robotics. 6: Cognitive model: Introducing models that mimic human cognitive processes for robotic design. 7: Soar (cognitive architecture): Delving into the Soar architecture and its role in intelligent decisionmaking. 8: Developmental robotics: Exploring how robots can learn from their environment, similar to human development. 9: Cognitive architecture: Understanding the structures that support robotic cognition and problemsolving. 10: Intelligent agent: Defining intelligent agents and their behavior within autonomous systems. 11: Embodied cognitive science: Investigating how cognition is linked to physical embodiment in robotics. 12: Enactivism: Introducing the theory of cognition that emphasizes interaction with the environment. 13: Moravec's paradox: Analyzing the gap between highlevel reasoning and lowlevel physical tasks in robotics. 14: Neurorobotics: Exploring the integration of neural models into robotic systems for advanced cognition. 15: Object Action Complex: Understanding how robots recognize and interact with objects in dynamic environments. 16: LIDA (cognitive architecture): An indepth look at the LIDA model and its applications in cognitive robotics. 17: Situated approach (artificial intelligence): Examining how AI adapts and operates in realworld settings. 18: Embodied cognition: Highlighting how physical presence and sensory feedback impact robotic intelligence. 19: Predictive coding: Understanding how robots use prediction to interpret sensory information and guide actions. 20: Cognitive neuroscience: Exploring how insights from neuroscience influence robotic cognitive architectures. 21: Cognition: A comprehensive review of cognition and its application to the design of intelligent robots.

1: Biomimetics: Introduction to biomimicry's transformative power in modern technology. 2: Microbotics: Insights into miniature robots inspired by microorganisms. 3: Bionics: Merging biology and engineering for advanced robotic functions. 4: Dario Floreano: Profile of a pioneer in bioinspired robotics. 5: Lotus effect: Natural selfcleaning surfaces in technology applications. 6: Bionic architecture: Natureinspired structures for sustainable design. 7: Biomimetic material: Materials science innovations from nature. 8: Robotics: Evolution and breakthroughs influenced by biological systems. 9: Wilhelm Barthlott: Contributions to biomimetic surface technology. 10: Biomimicry Institute: Institution promoting naturebased solutions. 11: Biomimetic antifouling coating: Technology inspired by marine life to resist fouling. 12: Bioinspired robotics: Integration of organic and robotic systems. 13: Biomimetic architecture: Bioinspired architectural advancements. 14: Electronic skin: Flexible, responsive skins mimicking human touch. 15: Soft robotics: Robots with adaptable, lifelike flexibility. 16: Robot fish: Aquatic robots mimicking real fish movements. 17: Selfcleaning surfaces: Surfaces emulating nature's lowmaintenance features. 18: Bioinspiration: Broader applications of natureinspired innovations. 19: Bioinspired photonics: Photonic technologies derived from natural light control. 20: Silvia Vignolini: Innovator in bioinspired photonic materials. 21: Javier G. Fernandez: Pioneer in sustainable biomaterials.

1: Feedback: This chapter introduces the fundamental concept of feedback and its significance in controlling dynamic systems. 2: Electronic oscillator: Learn how oscillators generate repetitive waveforms crucial for robotics and signal processing. 3: Amplifier: Explore how amplifiers enhance weak signals, making them integral to robotic circuits and feedback systems. 4: Multivibrator: This chapter explains multivibrators and their use in generating timing pulses for digital circuits in robots. 5: Operational amplifier: Dive into the workings of operational amplifiers and their role in creating precise control systems. 6: Loop gain: Understand loop gain's impact on the stability and response of feedback systems in robotic applications. 7: Phaselocked loop: Learn how phaselocked loops synchronize signals, essential for robotics' communication and control. 8: Negativefeedback amplifier: Explore how negative feedback improves amplifier performance and reduces distortion in robotic applications. 9: Relaxation oscillator: This chapter covers relaxation oscillators, which provide timing signals for digital robotic systems. 10: Negative feedback: Delve deeper into negative feedback’s ability to stabilize and optimize robotic circuits. 11: Positive feedback: Discover how positive feedback can enhance system performance but also introduces instability in robotics. 12: Negative resistance: Learn about negative resistance and its unique properties that can be used in robotic electronics. 13: Regenerative circuit: Explore regenerative circuits and how they amplify signals in robotic control systems. 14: Schmitt trigger: Understand how Schmitt triggers convert noisy signals into clean, sharp transitions in robotics. 15: Colpitts oscillator: This chapter covers the Colpitts oscillator and its application in generating stable frequencies for robotics. 16: RC oscillator: Learn about RC oscillators and their application in timing and frequency generation for robotic systems. 17: Wien bridge oscillator: Discover the Wien bridge oscillator’s role in precision frequency generation, vital for robotics. 18: Ring oscillator: This chapter explains ring oscillators and their role in providing clock signals for robotic systems. 19: Parasitic oscillation: Learn how parasitic oscillations affect electronic systems and how to mitigate their effects in robotics. 20: Flipflop (electronics): Understand flipflops and their use in storing binary data, fundamental for robotic control systems. 21: Comparator applications: Explore the use of comparators in decisionmaking circuits, essential for robotics' sensory processing.

1: Cumulative Distribution Function – Introduces the CDF and its foundational role in probability. 2: Cauchy Distribution – Examines this key probability distribution and its applications. 3: Expected Value – Discusses the concept of expected outcomes in statistical processes. 4: Random Variable – Explores the role of random variables in probabilistic models. 5: Independence (Probability Theory) – Analyzes independent events and their significance. 6: Central Limit Theorem – Details this fundamental theorem’s impact on data approximation. 7: Probability Density Function – Outlines the PDF and its link to continuous distributions. 8: Convergence of Random Variables – Explains convergence types and their importance in robotics. 9: MomentGenerating Function – Covers functions that summarize distribution characteristics. 10: ProbabilityGenerating Function – Introduces generating functions in probability. 11: Conditional Expectation – Examines expected values given certain known conditions. 12: Joint Probability Distribution – Describes the probability of multiple random events. 13: Lévy Distribution – Investigates this distribution and its relevance in robotics. 14: Renewal Theory – Explores theory critical to modeling repetitive events in robotics. 15: Dynkin System – Discusses this system’s role in probability structure. 16: Empirical Distribution Function – Looks at estimating distribution based on data. 17: Characteristic Function – Analyzes functions that capture distribution properties. 18: PiSystem – Reviews pisystems for constructing probability measures. 19: Probability Integral Transform – Introduces the transformation of random variables. 20: Proofs of Convergence of Random Variables – Provides proofs essential to robotics reliability. 21: Convolution of Probability Distributions – Explores combining distributions in robotics.

1: Electronic stability control: Explore the fundamentals of ESC, its components, and its role in vehicle safety. 2: Antilock braking system: Understand how ABS prevents wheel lockup during braking, improving control. 3: Toyota Matrix: Examine the implementation of stability control in the Toyota Matrix model and its impact. 4: Traction control system: Learn about TCS and its function in maintaining traction during acceleration. 5: Advanced driverassistance system: Discover how ADAS integrates with ESC for enhanced driving support. 6: Electronic brakeforce distribution: Investigate how EBD optimizes brake force to individual wheels for safety. 7: Electronic throttle control: Delve into ETC and its significance in precise vehicle acceleration management. 8: Drive by wire: Understand the transition from mechanical to electronic controls and its implications. 9: Audi RS 6: Analyze the application of advanced stability control in the performanceoriented Audi RS 6. 10: Jeep Patriot: Explore how stability systems enhance the offroad capabilities of the Jeep Patriot. 11: Cornering brake control: Learn how cornering brake control assists in maintaining stability during turns. 12: Brakebywire: Examine the advantages of electronically controlled brakes over traditional systems. 13: Vehicle safety technology: Investigate the broader spectrum of safety technologies in modern vehicles. 14: Mitsubishi SAWC: Understand the Super AllWheel Control system and its integration with stability tech. 15: Mitsubishi AWC: Explore the Active Wheel Control system and its impact on vehicle dynamics. 16: Collision avoidance system: Learn how ESC plays a crucial role in collision prevention technologies. 17: Sensotronic Brake Control: Delve into advanced braking technologies and their impact on vehicle control. 18: Vehicle Dynamics Integrated Management: Examine how VDIMS coordinates multiple systems for optimal performance. 19: Honda Accord (North America eighth generation): Review how the Accord integrates stability features for safety. 20: Sudden unintended acceleration: Understand the mechanisms and safety protocols surrounding this phenomenon. 21: Crosswind stabilization: Learn about technologies that assist in stabilizing vehicles during crosswinds.

Discover the transformative world of Software Engineering through the lens of Robotics Science. This book is an essential resource for professionals, students, and enthusiasts seeking a deeper understanding of software engineering's principles and its profound role in robotics. Explore how innovation in programming drives the future of intelligent systems, automation, and cuttingedge technologies. Chapters Brief Overview: 1: Software engineering – Explore the foundation of creating reliable, efficient systems. 2: Computing – Examine computational methods and their role in software design. 3: Programmer – Discover the vital role programmers play in robotics innovation. 4: Software Engineering Body of Knowledge – Gain insights into SE best practices and standards. 5: Computer engineering – Learn how hardware and software converge in robotics systems. 6: Software engineering professionalism – Understand ethical standards in tech development. 7: Gerard J. Holzmann – Learn from this pioneer’s impact on SE and formal verification. 8: Harlan Mills – Delve into Mills’ contributions to structured programming. 9: Certified software development professional – Explore credentials shaping SE careers. 10: Enduser development – Understand programming accessible to nonprofessionals. 11: Mary Shaw (computer scientist) – Appreciate Shaw’s vision in software architecture. 12: Elaine Weyuker – Discover Weyuker’s contributions to software testing methods. 13: Software construction – Unpack the practices of creating quality software systems. 14: Programming ethics – Reflect on ethical dilemmas and solutions in SE. 15: Alexander L. Wolf – Learn from Wolf’s work in distributed systems and SE research. 16: Tore Dybå – Explore Dybå’s insights into agile methods and empirical SE. 17: Laurie Williams (software engineer) – Examine Williams’ research in collaborative coding. 18: Barbara Kitchenham – Discover the importance of Kitchenham’s metrics and evaluation. 19: Computer programming – Gain a comprehensive view of programming’s evolution. 20: Computer science – Understand the broader context of CS in software engineering. 21: Quantum computing – Glimpse the futuristic integration of quantum tech in robotics. This book equips you to navigate challenges in roboticsdriven SE, ensuring its insights are invaluable for academic growth, career advancement, and personal enrichment. A musthave for anyone intrigued by the intersections of engineering, technology, and intelligent automation.

1: Evolutionary robotics: Introduces the core principles and evolution of autonomous robotic systems, emphasizing how robots can evolve through trial and error, similar to natural selection. 2: Evolutionary computation: Explains the computational techniques inspired by evolutionary biology, such as genetic algorithms, used to solve complex optimization problems in robotics. 3: Neuroevolution of augmenting topologies: Discusses a groundbreaking approach where neural networks evolve, including both structure and weights, to optimize robotic performance. 4: Neuroevolution: Explores the process of evolving artificial neural networks to enhance the capabilities of robots, focusing on their learning and adaptability. 5: Evolvable hardware: Delivers an overview of hardware systems that evolve in response to changing environmental conditions, bringing evolutionary concepts into physical robotic systems. 6: Sbot mobile robot: Examines the Sbot mobile robot, a key example of how evolutionary robotics techniques have been applied to realworld robotic platforms. 7: Dario Floreano: Highlights the contributions of Dario Floreano, a leading researcher in evolutionary robotics, whose work has significantly shaped the field. 8: Inman Harvey: Explores the research of Inman Harvey and his innovative approaches in the integration of evolutionary algorithms with robotic systems. 9: Phil Husbands: Focuses on the work of Phil Husbands in the area of autonomous robot behavior and his contributions to the application of evolutionary methods in robotics. 10: Stefano Nolfi: Investigates Stefano Nolfi's contributions to neuroevolution and his work on creating robots that learn and evolve in dynamic environments. 11: Neurorobotics: Covers the exciting field of neurorobotics, where robotics and neuroscience converge to develop robots that can mimic biological intelligence. 12: Artificial development: Describes the emerging field of artificial development, where evolutionary and developmental principles are applied to create more complex, adaptive robotic systems. 13: HyperNEAT: Introduces the HyperNEAT framework, an advanced method for evolving neural networks that generate complex robotic behaviors and structures. 14: Morphogenetic robotics: Focuses on morphogenetic robotics, where robots selforganize and adapt their physical forms through evolutionary processes. 15: Evolutionary developmental robotics: Examines how combining evolutionary algorithms with developmental robotics leads to the creation of robots that grow and learn over time. 16: Dave Cliff (computer scientist): Discusses the work of Dave Cliff, whose research in artificial life and evolutionary algorithms has influenced the development of adaptive robots. 17: Artificial life: Explores the relationship between artificial life and robotics, discussing how creating lifelike behavior in robots can lead to more intelligent systems. 18: Jordan Pollack: Highlights Jordan Pollack’s work in artificial evolution, particularly in relation to developing systems that mimic natural processes to improve robotic performance. 19: Sabine Hauert: Focuses on Sabine Hauert’s contributions to multirobot systems and how evolutionary principles can improve collaborative robot behavior. 20: Pavan Ramdya: Explores the work of Pavan Ramdya, whose research in robotics and neurobiology integrates the study of movement and behavior in autonomous robots. 21: Genetic programming: Concludes with a look at genetic programming, a method used to evolve programs that control robot behavior, facilitating automation in complex tasks.

In the rapidly evolving field of robotics, sensors play a critical role in enabling machines to perceive and interact with their environment. The book "Sensor," part of the "Robotics Science" series by Fouad Sabry, delves into the diverse types of sensors that are integral to modern robotics. This comprehensive guide is designed for professionals, undergraduate and graduate students, and enthusiasts looking to deepen their understanding of sensors and their applications in robotics. Chapters Brief Overview: 1: Sensor: An introduction to the role of sensors in robotics, their types, and essential functions. 2: Transistor: Explores how transistors serve as essential components in sensor technology and robotics. 3: MOSFET: Details the significance of MetalOxideSemiconductor FieldEffect Transistors in sensor applications. 4: Photodiode: Investigates the use of photodiodes in light detection and robotics vision systems. 5: Biosensor: Examines biosensors and their use in robotics, especially in medical and bioengineering fields. 6: Nanosensor: Focuses on the applications of nanosensors in miniaturized robotic systems. 7: Surface plasmon resonance: Explains the concept of surface plasmon resonance and its role in sensor technology. 8: ISFET: Introduces IonSensitive FieldEffect Transistors and their applications in robotics. 9: Chemical fieldeffect transistor: Describes the function of chemical FETs in detecting chemical changes in robotics. 10: Image sensor: Highlights the significance of image sensors in robotic vision and imaging systems. 11: Activepixel sensor: Explores the activepixel sensor's role in imaging technology for robotics. 12: Floatinggate MOSFET: Discusses the floatinggate MOSFET and its impact on sensor storage and technology. 13: Fiberoptic sensor: Investigates fiberoptic sensors and their unique capabilities in robotics and automation. 14: Massimo Grattarola: A focus on Massimo Grattarola's contributions to sensor technology and robotics. 15: Biotransducer: Explores the intersection of biological sensors and transducers in advanced robotics. 16: Fieldeffect transistor: Detailed explanation of fieldeffect transistors and their critical use in robotics sensors. 17: BioFET: Discusses BioFETs and their increasing relevance in biotechnology and robotic applications. 18: CD/DVD based immunoassay: Investigates the use of CD/DVDbased technology in immunoassays within robotics. 19: Piet Bergveld: A detailed look at Piet Bergveld's work and its profound impact on sensor technology. 20: Chemical sensor array: Describes the functionality and applications of chemical sensor arrays in robotics. 21: Semiconductor device: Covers semiconductor devices and their pivotal role in sensor technology and robotics. This book is a musthave resource for anyone involved in the field of robotics or sensor technology. With clear explanations, detailed chapter insights, and an expansive exploration of the many sensors integral to robotics, "Sensor" will help readers gain the knowledge needed to succeed in both academic and professional endeavors.