摘要
为提高物流机器人的效率和鲁棒性,基于学科交叉融合设计了一款竞赛类物流机器人。首先设计了机器人的机械臂和夹爪;然后依据功能需求对机器人硬件选型,并设计机器人的工作流程;最后设计机器人的控制系统和视觉系统。控制系统采用麦克纳姆轮解算控制机器人的移动,并运用“前馈+PID+Ping-Pang”的复合控制算法控制机械臂;视觉系统使用形态学操作识别色环。对物流机器人的效率和准确性进行实验验证,结果表明机器人各项性能指标达到了设计技术要求,该研究可为同类机器人的设计提供指导和参考。
[Objective]In today’s logistics operations,the demand for efficiency and robustness in robotic systems is paramount,yet many current models fall short.This research addresses these shortcomings by introducing a newly designed intelligent logistics robot that combines advanced control strategies,enhanced maneuverability,and reliable object handling capabilities.The primary aim is to develop a robot that adaptively handles materials in varied logistics scenarios with increased efficiency and minimal human oversight.[Methods]The design and development of this intelligent logistics robot involved an interdisciplinary approach,integrating advanced mechanical engineering,control theory,and computer vision.①Mechanical Design:The robot features a Mecanum wheelbase for omnidirectional movement,essential for navigating complex industrial environments.Its manipulator arm was engineered with high degrees of freedom and robustness to handle various tasks,ranging from simple pick-and-place operations to complex sorting and assembly.②Control System:The control architecture integrates feedforward elements,PID controllers,and a Ping-Pang algorithm to ensure precise and responsive movements.This hybrid control strategy allows the robot to adapt to real-time environmental feedback and task requirements,with the feedforward control providing a baseline behavior model dynamically adjusted by PID components to minimize trajectory and speed errs,while the ping-pong elements offer rapid responses for sudden task changes.③Vision System:A dual-camera system enables complex visual tasks,including color recognition and spatial analysis.Equipped with algorithms for morphological transformations,the cameras help the robot identify and sort objects based on color coding and shape,integrating seamlessly with its control software for feedback and control loop adjustments.④Testing and Validation:The robot was rigorously tested in a simulated logistics environment mimicking real-world industrial applications.Tasks included material transportation,object sorting based on color and size,and navigating obstacle-laden paths.Performance metrics such as task completion time,error rates in object handling,and system robustness under varying operational conditions were meticulously recorded.[Results]Testing demonstrated that the robot significantly outperformed traditional models in several key areas.The Mecanum wheels enhanced navigation agility,reducing travel time between tasks by over 30%compared to standard wheeled robots.The vision system proved highly effective,achieving a 95%accuracy in object recognition and sorting,even under variable lighting conditions.[Conclusions]This study successfully demonstrated the potential of advanced robotic systems in intelligent logistics operations.The combination of the Mecanum wheeled base with sophisticated control and vision systems enables high efficiency and adaptability.This project lays a solid foundation for future research and development in robotic logistics,suggesting significant operational efficiencies for industrial applications.Future work will focus on scaling the technology for broader applications and integrating machine learning algorithms to further enhance decision-making capabilities.
作者
耿冬妮
赵定坤
周颖婷
孙岩
程博
陈晋市
GENG Dongni;ZHAO Dingkun;ZHOU Yingting;SUN Yan;CHENG Bo;Chen Jinshi(College of Mechanical and Aerospace Engineering,Jilin University,Changchun 130000,China;College of Communication Engineering,Jilin University,Changchun 130000,China)
出处
《实验技术与管理》
CAS
北大核心
2024年第12期156-161,共6页
Experimental Technology and Management
基金
吉林大学2022年研究生教育教学改革研究项目(2022JGY003)。
作者简介
耿冬妮(1985-),女,山东青岛,硕士,高级工程师,主要研究方向为工程教育创新与实践,gengdongni2005@jlu.edu.cn;通信作者:陈晋市(1983-),男,山西晋城,博士,教授,主要研究方向为机械电子工程,spreading@jlu.edu.cn。
