Theoretical computer science
Related papers: 20
About
Theoretical computer science provides the mathematical and algorithmic foundations underlying robotics and artificial intelligence systems. It encompasses formal methods, complexity theory, algorithm design, data structures, logic, and computational models that enable rigorous reasoning about how automated systems behave and perform. In robotics and AI, these foundations appear across virtually every application domain: graph-based optimization algorithms power simultaneous localization and mapping (SLAM), probabilistic frameworks enable sensor fusion and motion planning under uncertainty, temporal logic supports formal specification of robot behaviors, and genetic algorithms drive automated design and scheduling. Path planning algorithms, formal verification methods, Petri nets for discrete event systems, and sparse matrix techniques for numerical computation all draw directly from theoretical computer science principles. This breadth matters because it transforms engineering problems into mathematically tractable ones, allowing researchers and practitioners to prove correctness guarantees, bound computational complexity, and design algorithms that scale reliably to real-world complexity. Without these theoretical underpinnings, robotics and AI would lack the rigorous vocabulary needed to build predictable, verifiable, and efficient intelligent systems.
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Top Cited Papers
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
Citations: 13277 • 1992
Probabilistic graphical models : principles and techniques
Daniel L. Koller, Nir Friedman
Citations: 6456 • 2009
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Lydia E. Kavraki, P. Švestka, J.-C. Latombe, M.H. Overmars
Citations: 6256 • 1996
Robot Motion Planning
Jean‐Claude Latombe
Citations: 5429 • 1991
Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
Wolfgang Maass, Thomas Natschläger, Henry Markram
Citations: 4023 • 2002
The university of Florida sparse matrix collection
Timothy A. Davis, Yifan Hu
Citations: 3610 • 2011
G<sup>2</sup>o: A general framework for graph optimization
Rainer Kümmerle, Giorgio Grisetti, Hauke Strasdat, Kurt Konolige, Wolfram Burgard
Citations: 1966 • 2011
A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems
Brian Gerkey, Maja J. Matarić
Citations: 1661 • 2004
A fast procedure for computing the distance between complex objects in three-dimensional space
Éric Gilbert, Daniel Johnson, S. Sathiya Keerthi
Citations: 1470 • 1988
A Tutorial on Graph-Based SLAM
Giorgio Grisetti, Rainer Kümmerle, Cyrill Stachniss, Wolfram Burgard
Citations: 1300 • 2010
Coverage for robotics – A survey of recent results
Howie Choset
Citations: 1189 • 2001
Modeling and control of formations of nonholonomic mobile robots
Jaydev P. Desai, J.P. Ostrowski, Vijay Kumar
Citations: 1155 • 2001
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Francisco Herrera, Manuel Lozano, José Luís Verdegay
Citations: 1137 • 1998
Knowledge in action: logical foundations for specifying and implementing dynamical systems
Citations: 1115 • 2002
GOLOG: A logic programming language for dynamic domains
Hector J. Levesque, Raymond Reiter, Yves Lespérance, Fangzhen Lin, Richard B. Scherl
Citations: 1039 • 1997
Robot Motion Planning: A Distributed Representation Approach
Jérôme Barraquand, Jean‐Claude Latombe
Citations: 988 • 1991
Numerical potential field techniques for robot path planning
Jérôme Barraquand, B. Langlois, J.-C. Latombe
Citations: 885 • 1992
Model checking for programming languages using VeriSoft
Patrice Godefroid
Citations: 828 • 1997
The focussed D* algorithm for real-time replanning
Anthony Stentz
Citations: 820 • 1995
Containment Control in Mobile Networks
M. Ji, Giancarlo Ferrari‐Trecate, Magnus Egerstedt, Annalisa Buffa
Citations: 801 • 2008