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 for updating partial pheromone and opposite pheromone. As a result, the global search of the proposed ACO algorithm can be significantly enhanced in terms of calculating optimal path compared to the conventional ACO algorithm. Simulation results of the proposed approach show better performances in terms of the shortest 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 significantly improved by the hardware design of embedded applications.