Abstract
Given a map in which each position is associated with a traversabihty cost, the path planning problem is to find a minimum-cost path from a source position to every other position in the map. The paper proposes a dynamic programming algorithm to solve the problem, and analyzes the exact number of operations that the algorithm takes. The algorithm accesses the map in a highly regular way, so it is suitable for parallel implementation. The paper describes two general methods of mapping the dynamic programming algorithm onto the linear systolic array in the Warp machine developed by Carnegie Mellon. Both methods have led to efficient implementations on Warp. It is concluded that a linear systolic array of powerful cells like the one in Warp is effective in implementing the dynamic programming algorithm for solving the path planning problem
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