Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Principles of artificial intelligence
Principles of artificial intelligence
An algorithm for planning collision-free paths among polyhedral obstacles
Communications of the ACM
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Egocentric affordance fields in pedestrian steering
Proceedings of the 2009 symposium on Interactive 3D graphics and games
The fringe-saving A* search algorithm: a feasibility study
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficient incremental search for moving target search
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Incremental Phi*: incremental any-angle path planning on grids
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
| Hi-index | 0.00 |
A* graph search effectively computes the optimal solution path from start nodes to goal nodes in a graph, using a heuristic function. In some applications, the graph may change slightly in the course of its use and the solution path then needs to be updated. Very often, the new solution will differ only slightly from the old. Rather than perform the full A* on the new graph, we compute the necessary OPEN nodes from which the revised solution can be obtained by A*. In this "Differential A*" algorithm, the graph topology, transition costs, or start/goals may change simultaneously. We develop the algorithm and discuss when it gives an improvement over simply reapplying A*. We briefly discuss an application to robot path planning in configuration space, where such graph changes naturally arise.