Modern Robotics
By Kevin M. Lynch, F. Park. 2017
1,089 citations recorded by the source corpus.
This introduction to robotics offers a distinct and unified perspective of the mechanics, planning and control of robots. Ideal for self-learning, or for courses, as it assumes only freshman-level physics, ordinary differential equations, linear algebra and a little bit of computing background. Modern Robotics presents the state-of-the-art, screw-theoretic techniques capturing the most salient physical features of a robot in an intuitive geometrical way. With numerous exercises at the end of each chapter, accompanying software written to reinforce the concepts in the book and video lectures aimed at changing the classroom experience, this is the go-to textbook for learning about this fascinating subject.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/8e2263f4d56a92d6c81830a17c7314e51f12cfba
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In Control and Systems Engineering
Citations in the corpus
- A survey of distributed optimization (2019, methodology): “For example, EL systems have been used to describe many mechanical systems, such as mobile robots, rigid bodies, and autonomous vehicles ( Lynch & Park, 2017; Spong, Hutchinson, & Vidyasagar, 2006 ).”
- iNeRF: Inverting Neural Radiance Fields for Pose Estimation (2020, neutral): “where S = [ω, ν] represents the screw axis, θ the magnitude, [w] represents the skew-symmetric 3× 3 matrix of w, and K(S, θ) = (Iθ+ (1− cos θ)[ω] + (θ− sin θ)[ω](2))ν [14].”
- Magnetic Methods in Robotics (2020, neutral): “Ẏ = JY I{m,Pb, I}İ = ( MY {m} + [ ∂MY {m} ∂F (1) F · · · ∂MY {m} ∂F (8) F ])”
- Nerfies: Deformable Neural Radiance Fields (2020, methodology): “We encode a rigid transform as a screw axis [35] S = (r;v) ∈ R(6).”