| _version_ | 1866901728175063040 |
|---|---|
| author | Kinstlinger, Samuel Xu, Andrew Chawla, Sameer Fugh, Brian |
| author_facet | Kinstlinger, Samuel Xu, Andrew Chawla, Sameer Fugh, Brian |
| contents | <p>This textbook provides a comprehensive introduction to the core algorithms and paradigms of robotics, with a particular emphasis on machine perception and planning. It is designed for both undergraduate and graduate students who seek a deep, principled understanding of robotics systems.</p> <p>The book’s central teaching philosophy is to derive each algorithm from first principles, explaining when and why it is optimal rather than treating methods as black boxes. By grounding each technique in the foundational goals of robotics, students learn not only how to apply algorithms effectively but also how to assess their limitations and improve upon them. This derivation-driven approach cultivates the analytical mindset needed to contribute new ideas to the field. To support this process, the book integrates concrete examples, intuitive explanations, and visualizations that clarify the rationale behind each method.</p> <p>This textbook is authored by four robotics researchers at the University of Maryland—Samuel Kinstlinger, Andrew Xu, Sameer Chawla, and Brian Fugh—who combine their expertise in perception, planning, machine learning, and robotics. Originally developed as an internal guide for their research team, the book was motivated by a desire to replace black-box explanations with transparent, derivation-driven insights that empower students to understand, apply, and improve robotics algorithms.</p> <p> </p> <p>View textbook for free online at: https://sam-kinstlinger.github.io/Robotics-Perception-Planning-Textbook</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16347923 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Robotics: Perception & Planning Kinstlinger, Samuel Xu, Andrew Chawla, Sameer Fugh, Brian <p>This textbook provides a comprehensive introduction to the core algorithms and paradigms of robotics, with a particular emphasis on machine perception and planning. It is designed for both undergraduate and graduate students who seek a deep, principled understanding of robotics systems.</p> <p>The book’s central teaching philosophy is to derive each algorithm from first principles, explaining when and why it is optimal rather than treating methods as black boxes. By grounding each technique in the foundational goals of robotics, students learn not only how to apply algorithms effectively but also how to assess their limitations and improve upon them. This derivation-driven approach cultivates the analytical mindset needed to contribute new ideas to the field. To support this process, the book integrates concrete examples, intuitive explanations, and visualizations that clarify the rationale behind each method.</p> <p>This textbook is authored by four robotics researchers at the University of Maryland—Samuel Kinstlinger, Andrew Xu, Sameer Chawla, and Brian Fugh—who combine their expertise in perception, planning, machine learning, and robotics. Originally developed as an internal guide for their research team, the book was motivated by a desire to replace black-box explanations with transparent, derivation-driven insights that empower students to understand, apply, and improve robotics algorithms.</p> <p> </p> <p>View textbook for free online at: https://sam-kinstlinger.github.io/Robotics-Perception-Planning-Textbook</p> |
| title | Robotics: Perception & Planning |
| url | https://doi.org/10.5281/zenodo.16347923 |