An improved ant colony optimization (ACO) algorithm is proposed in this paper for improving the accuracy of path planning. The main idea of this paper is to avoid local minima by continuously tuning a setting parameter and the establishment of novel mechanisms by means of partial pheromone updating and opposite pheromone updating. As a result, the global search of the proposed ACO algorithm can be significantly enhanced to derive an optimal path compared to the conventional ACO algorithm. The simulation results of the proposed approach perform better in terms of the short distance, mean distance, and success rate towards optimal paths. To further reduce the computation time, the proposed ACO algorithm for path planning is realized on a FPGA chip to verify its practicalities. Experimental results indicate that the efficiency of the path planning is considerably improved by the hardware design for embedded applications.

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