电子测量与仪器学报

黄超,杨泽彬,黄予昕.改进的小龙虾优化算法的移动机器人路径规划优化[J].电子测量与仪器学报,2026,40(1):238-246

改进的小龙虾优化算法的移动机器人路径规划优化

Path planning optimization of mobile robot with improvedcrayfish optimization algorithm

DOI：

英文关键词:mobile robots path planning ACOA algorithm piecewise mapping

基金项目:

作者	单位
黄超	重庆邮电大学信息无障碍工程研发中心重庆400065
杨泽彬	重庆邮电大学信息无障碍工程研发中心重庆400065
黄予昕	重庆邮电大学信息无障碍工程研发中心重庆400065

Author	Institution
Huang Chao	Information Accessibility Engineering R & D Center, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Yang Zebin	Information Accessibility Engineering R & D Center, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Huang Yuxin	Information Accessibility Engineering R & D Center, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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中文摘要:

为解决移动机器人在路径规划过程中路径不平滑、避障不稳定和陷入局部最优的问题,在传统的小龙虾算法(crayfish optimization algorithm, COA)的基础上提出了一种改进的自适应小龙虾优化算法(adaptive crayfish optimization algorithm, ACOA),来优化移动机器人的路径规划。首先添加Piecewise混沌映射初始化种群,使种群生成具有多样性和随机性以此来提高算法的全局搜索能力；其次通过引入自适应的温度参数调整机制,使小龙虾的根据情况调整3种行为,平衡算法在全局规划与局部探索之间的能力,加快算法的收敛速度；最后,通过自适应度函数的设计,加强机器人在路径规划中的避障能力并且达到路径平滑的效果。在仿真实验中,改进后的算法在多种复杂环境中表现出色,路径总长度、迭代收敛速度以及拐点数均优于传统的路径规划方法。ACOA算法于COA算法相比,在3种地图中路径长度分别缩短3.9%、5.3%、17.3%;拐点分别减少40%、33%、45%;且在路径平滑度和避障效果上也有显著提升。研究结果表明,ACOA算法在移动机器人路径规划中具有较高的实用性和稳定性。

英文摘要:

Regarding the issues of non-smooth paths, unstable obstacle avoidance, and getting stuck in local optima in mobile robot path planning, a new adaptive crayfish optimization algorithm (ACOA) based on the traditional crayfish optimization algorithm (COA) is proposed. The algorithm improves robot path planning. First, a Piecewise chaotic map is used to set up the population. It adds diversity and randomness to the population and helps the algorithm search better globally. Next, an adaptive temperature adjustment is used. This helps crayfish change their behaviors to fit the situation and balances global planning and local searching to speed up the convergence of the algorithm. Finally, a special fitness function is designed. It helps robots avoid obstacles better and creates smoother paths. Tests show that the improved algorithm works well in different complex environments. It gives shorter paths, faster convergence, and fewer turns. Compared with the COA algorithm, the ACOA algorithm reduces the path length by 3.9%, 5.3%, and 17.3% in the three maps, and the inflection points are reduced by 40%, 33%, and 45%, respectively. It also smooths paths and better avoids obstacles.

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