Xiaohan Wang, and Yanzi Miao
Path planning, particle swarm optimisation algorithm, ant colony optimisation, intelligent robot, fusion algorithm
As robot technology continues to develop and innovate, it is widely applied in various fields of production and life. Path planning (PP) is the cornerstone of autonomous navigation of intelligent robots (IRs). However, the current particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms still have problems, such as slow convergence rate (CR), complexity, and large amount of computation. Therefore, research will improve the hybrid ACO algorithm and PSO algorithm to obtain feasible robot PP. Then, ACO algorithm is used to obtain the optimal solution (OS), and the particle swarm ant colony fusion algorithm is obtained. Compared with PSO and ACO algorithms, the shortest path of the fusion algorithm is 43.78 m, which is closer to the optimal path. In an environment with an obstacle ratio of 0.4, the optimal performance index of the fusion algorithm is 8.84%, and the number of iterations during convergence is 24. Compared with genetic algorithm (GA) and sampling based PP algorithm, CR of this fusion algorithm is faster and the average value of the optimal path is smaller when the obstacle ratio is 0.7. In summary, the fusion algorithm proposed in this research is effective in IR path planning (IRPP). This can improve the path optimisation ability of IRs and provide a basis for exploring more effective global dynamic PP methods in the future.
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