References: List of Reference Books and Articles on Robotics and Automation

Books

1. General Robotics and Automation

1. Siciliano, B., & Khatib, O. (Eds.). (2016). Springer Handbook of Robotics (2nd ed.). Springer.

   • A comprehensive guide covering various aspects of robotics, including industrial, medical, and autonomous systems.

2. Craig, J. J. (2021). Introduction to Robotics: Mechanics and Control (4th ed.). Pearson.

   • A fundamental book on robotic kinematics, dynamics, and control techniques.

3. Groover, M. P. (2020). Automation, Production Systems, and Computer-Integrated Manufacturing (5th ed.). Pearson.

   • Covers industrial automation, robotics, and manufacturing systems.

2. Artificial Intelligence and Machine Learning in Robotics

4. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

   • A widely used textbook on AI, including applications in robotics.

5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

   • Discusses deep learning techniques used in robotic perception and decision-making.

6. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.

   • Essential for understanding reinforcement learning applications in robotics.

3. Industrial and Collaborative Robotics

7. Kragic, D., Gustafson, J., & Björkman, M. (2019). Interactive Perception and Robot Manipulation. Springer.

   • Covers perception-driven robot manipulation techniques.

8. Bicchi, A., & Kumar, V. (2022). Advances in Robotics and Automation. Elsevier.

   • Discusses the latest trends in industrial automation and robotics.

9. Pires, J. N. (2007). Industrial Robots Programming: Building Applications for the Factories of the Future. Springer.

   • A practical guide to programming industrial robots.

4. Mobile and Autonomous Robotics

10. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. MIT Press.

    • Explores probabilistic approaches to mobile robot localization and mapping (SLAM).

11. Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to Autonomous Mobile Robots (2nd ed.). MIT Press.

    • Covers localization, mapping, and control of mobile robots.

12. Bajracharya, M., Maimone, M. W., & Helmick, D. (2008). "Autonomy for Mars Rovers: Past, Present, and Future." Computer, 41(12), 44-50.

    • Discusses autonomous robotic exploration on Mars.

5. Medical and Soft Robotics

13. Taylor, R. H., & Menciassi, A. (Eds.). (2022). Medical Robotics: Regulatory, Ethical, and Economic Issues. Springer.

    • Covers applications of robotics in surgery and healthcare.

14. Kim, S., Laschi, C., & Trimmer, B. (2013). "Soft Robotics: A Bioinspired Evolution in Robotics." Trends in Biotechnology, 31(5), 287-294.

    • Reviews soft robotics inspired by biological systems.

15. Rus, D., & Tolley, M. T. (2015). "Design, Fabrication, and Control of Soft Robots." Nature, 521(7553), 467-475.

    • Discusses key advancements in soft robotics.

6. Robot Ethics and Safety Considerations

16. Lin, P., Abney, K., & Bekey, G. A. (2017). Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence. Oxford University Press.

    • Explores ethical considerations in robotics and AI.

17. Winfield, A. (2021). The Ethical Roboticist. MIT Press.

    • Discusses safety, responsibility, and ethics in robotics.

18. Borenstein, J., Herkert, J. R., & Miller, K. W. (2017). "The Ethics of Autonomous Cars." The Atlantic.

    • Investigates ethical dilemmas in self-driving vehicle technology.

7. Swarm and Multi-Robot Systems

19. Dorigo, M., & Birattari, M. (2020). Swarm Robotics: Principles, Current Status, and Future Trends. Springer.

    • Covers algorithms and applications of swarm intelligence in robotics.

20. Schranz, M., Di Caro, G. A., & Millard, A. G. (2020). "Swarm Robotic Systems: Current Trends, Challenges, and Future Research Directions." Frontiers in Robotics and AI, 7, 36.

    • A comprehensive review of swarm robotics applications.

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Articles and Research Papers

1. Levine, S., Pastor, P., Krizhevsky, A., & Quillen, D. (2018). "Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection." The International Journal of Robotics Research, 37(4-5), 421-436.

   • Discusses deep learning applications in robotic grasping.

2. Kober, J., Bagnell, J. A., & Peters, J. (2013). "Reinforcement Learning in Robotics: A Survey." The International Journal of Robotics Research, 32(11), 1238-1274.

   • Reviews reinforcement learning approaches in robotics.

3. Brooks, R. A. (1991). "Intelligence Without Representation." Artificial Intelligence, 47(1-3), 139-159.

   • A foundational paper on behavior-based robotics.

4. Arkin, R. C. (1998). "Behavior-Based Robotics." MIT Press.

   • Introduces the principles of behavior-based robotic systems.

5. Choset, H., Hutchinson, S., Kantor, G., & Lynch, K. (2005). Principles of Robot Motion: Theory, Algorithms, and Implementation. MIT Press.

   • Discusses motion planning algorithms for robots.

6. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., & Veness, J. (2015). "Human-Level Control through Deep Reinforcement Learning." Nature, 518(7540), 529-533.

   • A breakthrough paper on deep reinforcement learning for robotic applications.

7. Karaman, S., & Frazzoli, E. (2011). "Sampling-Based Algorithms for Optimal Motion Planning." The International Journal of Robotics Research, 30(7), 846-894.

   • Discusses efficient motion planning methods.

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Conclusion

These books and research articles provide a strong foundation for understanding various aspects of robotics and automation, from AI and machine learning to industrial automation, mobile robotics, medical applications, and ethical considerations. Whether for academic study, research projects, or industry applications, these references cover essential and emerging trends in the field.