Easy impossibility proofs for distributed consensus problems
Distributed Computing
Using backprojections for fine motion planning with uncertainty
International Journal of Robotics Research
The complexity of elementary algebra and geometry
Journal of Computer and System Sciences
Mechanics and planning of manipulator pushing operations
International Journal of Robotics Research
Planning for conjunctive goals
Artificial Intelligence
Complexity of deciding Tarski algebra
Journal of Symbolic Computation
SCG '88 Proceedings of the fourth annual symposium on Computational geometry
Error detection and recovery in robotics
Error detection and recovery in robotics
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
Fine motion planning for dexterous manipulation
Fine motion planning for dexterous manipulation
Provably-good approximation algorithms for optimal kinodynamic robot motion plans
Provably-good approximation algorithms for optimal kinodynamic robot motion plans
Analysis of adaptation and environment
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Information invariants for distributed manipulation
WAFR Proceedings of the workshop on Algorithmic foundations of robotics
Robot Motion Planning
Symbolic and Numerical Computation for Artificial Intelligence
Symbolic and Numerical Computation for Artificial Intelligence
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra, 3/e (Undergraduate Texts in Mathematics)
Spatial Planning: A Configuration Space Approach
IEEE Transactions on Computers
On the capability of finite automata in 2 and 3 dimensional space
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
On the power of the compass (or, why mazes are easier to search than graphs)
SFCS '78 Proceedings of the 19th Annual Symposium on Foundations of Computer Science
Complexity of the mover's problem and generalizations
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
New lower bound techniques for robot motion planning problems
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
On the complexity of kinodynamic planning
SFCS '88 Proceedings of the 29th Annual Symposium on Foundations of Computer Science
A Multimedia Environment for Supporting the Teaching of Robotics Systems
ICDCSW '04 Proceedings of the 24th International Conference on Distributed Computing Systems Workshops - W7: EC (ICDCSW'04) - Volume 7
Simple Robots in Polygonal Environments: A Hierarchy
Algorithmic Aspects of Wireless Sensor Networks
On the Topology of Discrete Strategies
International Journal of Robotics Research
Sensing and Filtering: A Fresh Perspective Based on Preimages and Information Spaces
Foundations and Trends in Robotics
The compass that steered robotics
Logic and Program Semantics
Planning for provably reliable navigation using an unreliable, nearly sensorless robot
International Journal of Robotics Research
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We consider the problem of determining the information requirements to perform robot tasks, using the concept ofinformation invariants. This paper represents our attempt to characterize a family of complicated and subtle issues concerned with measuring robot task complexity. We also provide a first approximation to a purely operational theory that addresses a narrow but interesting special case. We discuss several measures for the information complexity of a task: (a) How much internal state should the robot retain? (b) How many cooperating agents are required, and how much communication between them is necessary? (c) How can the robot change (side-effect) the environment in order to record state or sensory information to perform a task? (d) How much information is provided by sensors? and (e) How much computation is required by the robot? We consider how one might develop a kind of ''calculus'' on (a)-(e) in order to compare the power of sensor systems analytically. To this end, we attempt to develop a notion of information invariants. We develop a theory whereby one sensor can be ''reduced'' to another (much in the spirit of computation-theoretic reductions), by adding, deleting, and reallocating (a)-(e) among collaborating autonomous agents.