粒子群优化
计算机科学
算法
弹道
最大值和最小值
数学优化
移动机器人
运动规划
人口
多群优化
机器人
地形
人工智能
数学
生物
物理
数学分析
社会学
人口学
生态学
天文
作者
Pedro B. Fernandes,Roberto Célio Limão de Oliveira,João Viana da Fonseca Neto
标识
DOI:10.1016/j.asoc.2021.108108
摘要
This paper presents a new quantum-behaved particle swarm optimization (QPSO) algorithm for the trajectory planning task of mobile robotic vehicles in static and dynamic environments—it is called enhanced diversity particle swarm optimization (EDPSO). The main characteristic of this algorithm is that it has peaks of diversity in its population, making it possible to escape from local minima effectively, avoiding stagnation. Through the proposed PSO, it is possible to obtain safe and efficient routes, avoiding energy waste and maintaining system integrity in several possible applications. The parameters of the proposed algorithm were tuned using the benchmarking functions. The same functions were used to compare the algorithm with those already established in the literature. Once the proposed algorithm showed promising results, it was simulated in four environments, each with different complexities, presenting dangerous regions and terrains unsuitable for robot navigation, and a large number of obstacles or even moving objects. Further, the algorithms used for comparison were also simulated and the EDPSO presented satisfactory results. Through simulations it was possible to notice that the proposed approach resulted in collision-free and planned routes, and the algorithm presented increased exploration features owing to the diversity peaks that occur during the optimization.
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