Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Linear-space best-first search
Artificial Intelligence
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
An algorithm for planning collision-free paths among polyhedral obstacles
Communications of the ACM
Introduction to AI Robotics
Computer Networking: A Top-Down Approach Featuring the Internet
Computer Networking: A Top-Down Approach Featuring the Internet
AI Magazine
Game Programming Gems
Handbook of AI
Coordination for Multi-Robot Exploration and Mapping
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Journal of the ACM (JACM)
A mobius automation: an application of artificial intelligence techniques
IJCAI'69 Proceedings of the 1st international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hierarchical A *: searching abstraction hierarchies efficiently
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Map-based navigation in mobile robots
Cognitive Systems Research
Towards a robust feedback system for coordinating a hierarchical multi-robot system
Robotics and Autonomous Systems
Hi-index | 0.00 |
Multi-robot systems have inherent advantages such as the ability to allocate and redistribute tasks across the team of robots. For multi-robot tasks such as exploration of large environments, some of the available robots may only possess simple embedded controllers with limited memory capacity. However, in some situations these limited robots may be required to perform global path planning to navigate beyond localised regions of the large environment. Global path planning can be problematic for the limited memory robots if they are unable to store the entire map in their local memory. Hence, this paper presents and evaluates a two-tiered path planning technique to permit global path planning. A set of local maps describing the global map is searched using a two-tiered A^* algorithm that executes entirely on the limited memory robots. Planning time, data communication and path length are evaluated for various combinations of local and global maps. Employing smaller local map sizes in large global maps is capable of yielding superior or comparable execution times to non-memory constrained planning.