Machines that sense, decide and move — kinematics, motion planning and grasping, where control theory meets hardware.
13 topics · 15 curated works
No prior grounding assumed.
Modern Robotics: Mechanics, Planning, and Control
Kevin M. Lynch & Frank C. Park · 2017
Argues that a robot's configuration space, not its joint angles, is the right object to reason about, and rebuilds kinematics, dynamics and planning…
Assumes you know the vocabulary.
A Survey of Socially Interactive Robots
Terrence Fong, Illah R. Nourbakhsh & Kerstin Dautenhahn · 2003
Distinguishes robots that merely operate near people from robots designed to interact with them socially, and argues the two goals demand different…
+2 more at this level
Primary sources and full treatments.
A Kinematic Notation for Lower-Pair Mechanisms Based on Matrices
Jacques Denavit & Richard S. Hartenberg · 1955
Introduces a systematic four-parameter convention for describing linked mechanisms with matrices, giving every subsequent robot arm a common language…
+10 more at this level
12 of 15 works
Kevin M. Lynch & Frank C. Park
Argues that a robot's configuration space, not its joint angles, is the right object to reason about, and rebuilds kinematics, dynamics and planning consistently from that single choice of representation.
link checked 17 Sept 2026Terrence Fong, Illah R. Nourbakhsh & Kerstin Dautenhahn
Distinguishes robots that merely operate near people from robots designed to interact with them socially, and argues the two goals demand different research questions rather than one set of techniques applied twice.
link checked 17 Sept 2026Roland Siegwart, Illah R. Nourbakhsh & Davide Scaramuzza
Walks a mobile robot from locomotion through perception to planning as one connected pipeline, arguing that a robot's failures are usually failures of the interface between these stages rather than of any one stage alone.
Hugh Durrant-Whyte & Tim Bailey
Argues that a robot cannot localise and map independently in an unknown environment — the two estimates are statistically coupled — and lays out the probabilistic formulation that treats them as one problem.
link checked 17 Sept 2026Jacques Denavit & Richard S. Hartenberg
Introduces a systematic four-parameter convention for describing linked mechanisms with matrices, giving every subsequent robot arm a common language for its kinematics instead of an ad hoc one per design.
K. S. Fu, R. C. Gonzalez & C. S. G. Lee
Treats the industrial robot as an integration problem across four largely separate disciplines — kinematics, control, vision, planning — arguing that automation failures usually happen at the seams between them.
Richard M. Murray, Zexiang Li & S. Shankar Sastry
Grounds manipulator design and grasping in Lie groups and screw theory, so a robot's reachable configurations follow from its geometry rather than from a table of special cases.
link checked 17 Sept 2026Gill A. Pratt & Matthew M. Williamson
Argues that deliberately adding compliance between a motor and its load — rather than fighting for stiffness — makes force control simpler and contact with the world safer.
Steven M. LaValle
Proposes building a space-filling tree by repeatedly extending toward random samples as a way to plan feasible paths through high-dimensional and non-holonomic configuration spaces where classical grid-based planners do not scale.
Sebastian Thrun, Wolfram Burgard & Dieter Fox
Argues that a robot's sensors and actions are both irreducibly noisy, so perception and control should be built on Bayesian probability from the start rather than patched with uncertainty handling afterwards.
Steven M. LaValle
Treats motion planning as search through a continuous configuration space, unifying robotics, computational geometry and control planning as instances of one problem.
link checked 17 Sept 2026Sebastian Thrun et al.
Describes the software architecture that let a modified Volkswagen Touareg drive 132 miles of desert terrain unmanned, arguing that robust probabilistic sensor fusion mattered more than any single perception or planning algorithm.