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动态SHO-ACO的焊接机器人路径规划

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移动焊接机器人在户外进行路径规划时的高效性和安全性尤为重要.针对蚁群算法(ACO)信息素以总长度为单个影响因子的缺陷,加入转向次数要素,建立环境适应度函数,从而改进轨迹上信息素增值状况;针对基本蚁群收敛速度慢的问题,借鉴自私羊群算法(SHO)的空间因素,改进启发函数;针对局部最优问题,将SHO的吸引力函数融入信息素变化中再结合环境适应度函数,动态指引蚁群朝向更加优良的轨迹前行;而且针对停滞僵局问题,提出撤离行动与预警行动,确保蚂蚁探路效率;针对传统轮转方法随机性问题,提出了评判拐弯机制以在有目的选择下一节点的同时,计算路径距离方法,降低了算法的复杂程度.SHO-ACO与势场蚁群和传统蚁群算法进行仿真对比实验,结果表明,SHO-ACO在简单环境与复杂环境中均具有优越性.
Path Planning of Welding Robot Based on Dynamic SHO-ACO Algorithm
The efficiency and safety of mobile welding robots for path planning in the outdoors are particularly important.To ad-dress the shortcomings of the Ant Colony Optimization(ACO)pheromone with total length as a single influence factor.The ele-ment of steering times is added to establish the environmental adaptation function.This improves the pheromone value-added situ-ation on the trajectory.To address the problem of slow convergence of the basic ant colony.Borrowing the spatial factor from the Selfish Herds Optimization(SHO),the heuristic function is improved.For the local optimum problem.Incorporate the attraction function of SHO into the pheromone change combined with the environmental fitness function.Dynamically guide the ant colony toward a more optimal trajectory.And for the stagnant deadlock problem,we propose the evacuation action and early warning ac-tion.Ensure the efficiency of ant pathfinding.For the problem of randomness of the traditional rotation method,the evaluation inflection mechanism is proposed to calculate the path distance method while purposefully selecting the next node.The complexity of the algorithm is reduced.SHO-ACO is compared with potential field ant colony and traditional ant colony algorithms in simu-lation experiments.The results show that SHO-ACOis superior in both simple and complex environments.

Mobile Welding RobotPath PlanningSelfish Herds Optimization AlgorithmAnt Colony Optimiza-tionEnvironmental Fitness FunctionPheromone UpdateJudging Turning Mechanism

任红格、宋雪琪、史涛

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华北理工大学电气工程学院,河北 唐山 063210

天津城建大学控制与机械工程学院,天津 300384

天津理工大学天津市电气与电子工程学院复杂系统控制理论与应用重点实验室,天津 300384

移动焊接机器人 路径规划 自私羊群优化算法 蚁群算法 环境适应度函数 信息素更新 评判拐弯机制

国家自然科学基金项目河北省自然科学基金项目

61203343F2018209289

2024

机械设计与制造
辽宁省机械研究院

机械设计与制造

CSTPCD北大核心
影响因子：0.511
ISSN：1001-3997
年,卷(期)：2024.(7)
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