Navigation and mapping in large-scale space
AI Magazine
Competitive algorithms for on-line problems
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Qualitative navigation for mobile robots
Artificial Intelligence
The Canadian Traveller Problem
SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
Dynamic Programming: Models and Applications
Dynamic Programming: Models and Applications
Finite State Markovian Decision Processes
Finite State Markovian Decision Processes
ICALP '89 Proceedings of the 16th International Colloquium on Automata, Languages and Programming
Coping with uncertainty in a control system for navigation and exploration
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
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For high level path planning, environments are usually modeled as distance graphs, and path planning problems are reduced to computing the shortest path in distance graphs. One major drawback of this modeling is the inability to model uncertainties, which are often encountered in practice. In this paper, a new tool, called U-graph, is proposed for environment modeling. A U-graph is an extension of distance graphs with the ability to handle a kind of uncertainty. By modeling an uncertain environment as a U-graph, and a navigation problem as a Markovian decision process, we can precisely define a new optimality criterion for navigation plans, and more importantly, we can come up with a general algorithm for computing optimal plans for navigation tasks.