Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Efficient memory-bounded search methods
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Memory-bounded bidirectional search
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Memory-Bounded A* Graph Search
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Divide-and-Conquer Frontier Search Applied to Optimal Sequence Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Planning conditional shortest paths through an unknown environment: a framed-quadtree approach
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 3 - Volume 3
Sweep A*: Space-Efficient Heuristic Search in Partially Ordered Graphs
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Novel indoor mobile robot navigation using monocular vision
Engineering Applications of Artificial Intelligence
Journal of Artificial Intelligence Research
A: an efficient near admissible heuristic search algorithm
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Studies in Semi-Admissible Heuristics
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper introduces a novel variant of the A^@? path planning algorithm, which we call Light-assisted A^@? (or LA^@? for short). The LA^@? algorithm expands less nodes than A^@? during the search process, especially in scenarios where there are complex-shaped obstacles in the path between the start and goal nodes. This is achieved using the concept of (virtual) light which identifies and demotes dead-end paths blocked by obstacles, thus ensuring that the search stays focused on promising paths. Three path planning problems are used to test the performance of LA^@?. These include path finding in a grid cluttered by randomly placed obstacles, robot navigation in a map containing multiple solid walls, and finally mazes. The results of these experiments show that LA^@? can achieve orders of magnitude improvement in performance over A^@?. In addition, LA^@? results in near-optimal solutions that are very close to the optimal path obtained by the conventional A^@? algorithm.