Planning as search: a quantitative approach
Artificial Intelligence
Solving the find-path problem by good representation of free space
Autonomous robot vehicles
Symbolic and Geometric Connectivity Graph Methods for Route Planning in Digitized Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topological direction-giving and visual navigation in large environments
Artificial Intelligence - Special volume on computer vision
Hierarchical optimization of optimal path finding for transportation applications
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
Dynamic Clustering of Maps in Autonomous Agents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM
Artificial Intelligence
Modelling a Hierarchy of Space Applied to Large Road Networks
IGIS '94 Proceedings of the International Workshop on Advanced Information Systems: Geographic Information Systems
A multiple layer model to compare RNA secondary structures
Software—Practice & Experience
Speed control of a mobile robot using neural networks and fuzzy logic
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Two-way D* algorithm for path planning and replanning
Robotics and Autonomous Systems
Abstraction and multiple abstraction in the symbolic modeling of the environment of mobile robots
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
A multiple graph layers model with application to RNA secondary structures comparison
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
A multi-agent control architecture for a robotic wheelchair
Applied Bionics and Biomechanics
| Hi-index | 0.14 |
The use of hierarchical graph search for finding paths in graphs is well known in the literature, providing better results than plain graph search regarding computational costs in many cases. This paper offers a step forward by including multiple hierarchies in a graph-based model. Such a multihierarchical model has the following advantages: First, a multiple hierarchy permits us to choose the best hierarchy to solve each search problem; second, when several search problems have to be solved, a multiple hierarchy provides the possibility of solving part of them simultaneously; and third, solutions to the search problems can be expressed in any of the hierarchies of the multiple hierarchy, which allows us to represent the information in the most suitable way for each specific purpose. In general, multiple hierarchies have proven to be a more adaptable model than single-hierarchy or nonhierarchical models. This paper formalizes the multihierarchical model, describes the techniques that have been designed for taking advantage of multiple hierarchies in a hierarchical path search, and presents some experiments and results on the performance of these techniques.