针对传统蚁群算法在移动机器人路径规划中存在搜索盲目性、收敛速度慢及路径转折点多等问题,提出了一种基于改进蚁群算法的移动机器人路径规划算法。首先,利用跳点搜索(Jump Point Search,JPS)算法不均匀分配初始信息素,降低蚁群前期盲...针对传统蚁群算法在移动机器人路径规划中存在搜索盲目性、收敛速度慢及路径转折点多等问题,提出了一种基于改进蚁群算法的移动机器人路径规划算法。首先,利用跳点搜索(Jump Point Search,JPS)算法不均匀分配初始信息素,降低蚁群前期盲目搜索的概率;然后,引入切比雪夫距离加权因子和转弯代价改进启发函数,提高算法的收敛速度、全局路径寻优能力和搜索路径的平滑程度;最后,提出一种新的信息素更新策略,引入自适应奖惩因子,自适应调整迭代前、后期的信息素奖惩因子,保证了算法全局最优收敛。实验仿真结果表明,在不同地图环境下,与现有文献结果对比,该算法可以有效地缩短路径搜索的迭代次数和最优路径长度,并提高路径的平滑程度。展开更多
Ocular dominance(OD) is referred to the superiority of one eye over the other when the visual sense is produced[1]. This condition can be considered as a preference for the particular laterality of the eye during a mo...Ocular dominance(OD) is referred to the superiority of one eye over the other when the visual sense is produced[1]. This condition can be considered as a preference for the particular laterality of the eye during a mono-visual task or the relative contributions of visual signal transduction between eyes[2]. Empirically, OD tendencies are exhibited in habitual and subconscious behavior.展开更多
文摘针对传统蚁群算法在移动机器人路径规划中存在搜索盲目性、收敛速度慢及路径转折点多等问题,提出了一种基于改进蚁群算法的移动机器人路径规划算法。首先,利用跳点搜索(Jump Point Search,JPS)算法不均匀分配初始信息素,降低蚁群前期盲目搜索的概率;然后,引入切比雪夫距离加权因子和转弯代价改进启发函数,提高算法的收敛速度、全局路径寻优能力和搜索路径的平滑程度;最后,提出一种新的信息素更新策略,引入自适应奖惩因子,自适应调整迭代前、后期的信息素奖惩因子,保证了算法全局最优收敛。实验仿真结果表明,在不同地图环境下,与现有文献结果对比,该算法可以有效地缩短路径搜索的迭代次数和最优路径长度,并提高路径的平滑程度。
基金supported by the Shanghai Clinical Three-Year Action Plan-Major Clinical Research Project[SHDC2020CR2041B]Xuhui District Health and Family Planning Commission Key Disease Joint Project[XHLHGG201807]。
文摘Ocular dominance(OD) is referred to the superiority of one eye over the other when the visual sense is produced[1]. This condition can be considered as a preference for the particular laterality of the eye during a mono-visual task or the relative contributions of visual signal transduction between eyes[2]. Empirically, OD tendencies are exhibited in habitual and subconscious behavior.