基于蚁群势场算法的建筑材料运输机器人智能避障方法  

Intelligent Obstacle Avoidance Method of Building Materials Transportation Robot Based on Ant Colony Potential Field Algorithm

作　　者：郭康康 赵传鑫 GUO Kangkang;ZHAO Chuanxin(Department of Planning and Construction,Beijing Chao-Yang Hospital,Capital Medical University,Beijing 100020,China;Urban Construction Department,Beijing University of Technology,Beijing 100124,China;Research Institute of Medical Architecture Design,China IPPR International Engineering Co.,Ltd.,Beijing 100089,China)

机构地区：[1]首都医科大学附属北京朝阳医院规划建设处,北京100020 [2]北京工业大学城市建设学部,北京100124 [3]中国中元国际工程有限公司医疗建筑设计研究院,北京100089

出　　处：《计算机测量与控制》2024年第10期215-221,共7页Computer Measurement &Control

摘　　要：针对现有建筑材料运输机器人避障中存在的全局寻优能力差,易与移动障碍物发生碰撞的不足,设计了一种蚁群势场算法;首先分析了蚁群算法下蚂蚁个体信息素浓度的累积过程,通过构建人工势场求解引力和斥力的合作,将其作为优选蚁群算法启发因子的重要约束条件;其次引入SA算法对蚁群势场算法做二次优化,将降温的过程视为一个全局优化的过程;最后在局部避碰方面构建了质量点模型,通过评估机器人当前位置、运行速度和障碍物位置等信息建立惩罚函数,并将惩罚函数值降至最低,避免出现与障碍物的碰撞;实验结果显示:提出算法有更高的迭代效率,复杂动态条件下最短行进距离为110.6 m, 4种传统算法的最短行进距离分别为135.5、137.6、137.2和130.4 m,而且在该算法控制下,未出现局部与其他移动机器人的碰撞情况。An ant colony potential field algorithm was designed to solve the problems of poor global optimization ability and easy collision with moving obstacles in existing building material transport robots.Firstly,the accumulation process of individual pheromone concentration under the ant colony algorithm is analyzed,and the cooperation of gravity and repulsion force is solved by constructing an artificial potential field,which is regarded as an important constraint for selecting the heuristic factor of the ant colony algorithm.Secondly,a simulated annealing(SA)algorithm is introduced to optimize the ant colony potential field algorithm twice,and the cooling process is regarded as a global optimization process.Finally,the mass point model is constructed in terms of local collision avoidance,and the penalty function is established by evaluating the robot s current position,running speed and obstacle position,and the penalty function value is reduced to the minimum to avoid collision with obstacles.Experimental results show that the proposed algorithm has higher iterative efficiency,and the shortest travel distance of 110.6 m under complex dynamic conditions,while the shortest travel distance of the four traditional algorithms is 135.5 m,137.6 m,137.2 m and 130.4 m,respectively.Moreover,under the control of the proposed algorithm,there is no local collision with other mobile robots.

关 键 词：蚁群势场 运输机器人 智能避障 启发因子 质量点模型 

分 类 号：TP241[自动化与计算机技术—检测技术与自动化装置]