Bertram Raphael's scientific contributions
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Publications (2)
Our paper on the use of heuristic information in graph searching defined a path-finding algorithm, A*, and proved that it had two important properties. In the notation of the paper, we proved that if the heuristic function ñ (n) is a lower bound on the true minimal cost from node n to a goal node, then A* is admissible; i.e., it would find a minima...
Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be...
Citations
... In recent studies, different strategies have been proposed to optimize the path planning for mobile robots based on A * algorithm. For instance, Bennewitz [4] developed a decoupling-based path planning strategy that applies A * algorithm [5] to minimize the overall path length. Guo's approach [6] incorporates speed planning to achieve dynamic path planning with real-time obstacle avoidance [7], while Li introduced the concept of Artificial Untraversable Vertex in the D * Lite method for fast replanning [8]. ...
... Graph networks model locations as nodes and distances as edges to solve for the shortest trajectory between two points. For instance, graph-based methods in the class of shortest path algorithms, such as Djikstra's algorithm and the A* algorithm, use dynamic programming to iteratively search for the shortest path [2]. Tree-based methods, such as the rapidlyexploring random tree* (RRT*), on the other hand, circumvent the inefficiencies arising from random walks in graph-based searches by rapidly growing search trees far away [3]. ...
