Most robotics texts either focus on hardware mechanics or on graduate-level algorithmic treatments; this project fills the gap by framing autonomy as a balance between mechanisms, sensing, actuation, and computation. That perspective makes it practical for third- and fourth-year undergraduates to build algorithmic intuition while working with realistic course projects and simulations.
What Sets It Apart
- Balanced algorithm-hardware view: chapters interleave physical principles (locomotion, forces, kinematics) with perception and planning so readers see how software choices map to mechanical constraints — useful when moving from simulation to hardware.
- Course-ready assets: LaTeX source, problem sets, lecture slides, and Webots-based simulation examples let instructors adopt or adapt materials directly for semester courses.
- Accessible prerequisites: assumes sophomore-level linear algebra, probability, and trigonometry, lowering the barrier compared with graduate texts while covering modern topics like basic neural networks and feature-based vision.
- Open-source collaboration: the book evolves via GitHub contributions, making it easier to track updates, errata, and new perception/planning sections contributed by the community.
Who It's For and Trade-offs
Great fit if you teach or take an undergraduate robotics course and want a single resource that connects mechanics, sensing, and core algorithms with hands-on exercises. Look elsewhere if you need a deeply theoretical graduate treatment of one subfield (e.g., advanced control theory or state-of-the-art deep learning research) or if you require a freely distributable compiled PDF—the repository provides source under CC-BY-NC-ND but the authors restrict published compiled copies for copyright reasons.
