为解决玻璃幕墙注胶行业在生产加工过程中面临的困难,如成本高、效率低和难以实现全面智能化和自动化等问题,设计并研发了一种基于数字孪生的玻璃幕墙注胶机器人检测控制系统。首先,使用建模软件SolidWorks建立了注胶机器人的仿真模型,...为解决玻璃幕墙注胶行业在生产加工过程中面临的困难,如成本高、效率低和难以实现全面智能化和自动化等问题,设计并研发了一种基于数字孪生的玻璃幕墙注胶机器人检测控制系统。首先,使用建模软件SolidWorks建立了注胶机器人的仿真模型,并搭建仿真实体机器人。接着,通过虚拟机器人运动仿真平台CoppeliaSim构建虚拟场景,实现了机器人注胶过程的运动仿真,并进行机器人运动控制与检验。在此过程中,利用Visual Studio 2022设计了人机交互界面和基于C#的串口通信模块,以实现界面、仿真平台和机器人的有效通信,达到虚拟模型与注胶机器人的动态实时交互的目的。在系统控制检测方面,对机器人进行了正逆运动学分析,结合包围盒算法和GJK算法实现了机器人碰撞检测。实验结果表明,注胶机器人能够有效响应控制指令,响应延迟小于200 ms,界面能够实时显示运动参数,参数误差小于5%,动态交互成功实现。该系统为玻璃幕墙注胶行业提供了技术支持与解决方案,有效提升了生产效率并降低了成本。展开更多
The paper presents a fuzzy Q-learning(FQL)and optical flow-based autonomous navigation approach.The FQL method takes decisions in an unknown environment and without mapping,using motion information and through a reinf...The paper presents a fuzzy Q-learning(FQL)and optical flow-based autonomous navigation approach.The FQL method takes decisions in an unknown environment and without mapping,using motion information and through a reinforcement signal into an evolutionary algorithm.The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are“dense”or“thin”which has a relationship with the proximity of objects.The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component.The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python.展开更多
Planning a reasonable driving path for trucks in mining areas is a key point to improve mining efficiency.In this paper,a path planning method based on Rapidly-exploring Random Tree Star(RRT∗)is proposed,and several o...Planning a reasonable driving path for trucks in mining areas is a key point to improve mining efficiency.In this paper,a path planning method based on Rapidly-exploring Random Tree Star(RRT∗)is proposed,and several optimizations are carried out in the algorithm.Firstly,the selection process of growth target points is optimized.Secondly,the process of selecting the parent node is optimized and a Dubins curve is used to constraint it.Then,the expansion process from tree node to random point is optimized by the gravitational repulsion field method and dynamic step method.In the obstacle detection process,Dubins curve constraint is used,and the bidirectional RRT∗algorithm is adopted to speed up the iteration of the algorithm.After that,the obtained paths are smoothed by using the greedy algorithm and cubic B-spline interpolation.In addition,to verify the superiority and correctness of the algorithm,an unmanned mining vehicle kinematic model in the form of frontwheel steering is developed based on the Ackermann steering principle and simulated for CoppeliaSim.In the simulation,the Stanley algorithm is used for path tracking and Reeds-Shepp curve to adjust the final parking attitude of the truck.Finally,the experimental comparison shows that the improved bidirectional RRT∗algorithm performs well in the simulation experiment,and outperforms the common RRT∗algorithm in various aspects.展开更多
文摘为解决玻璃幕墙注胶行业在生产加工过程中面临的困难,如成本高、效率低和难以实现全面智能化和自动化等问题,设计并研发了一种基于数字孪生的玻璃幕墙注胶机器人检测控制系统。首先,使用建模软件SolidWorks建立了注胶机器人的仿真模型,并搭建仿真实体机器人。接着,通过虚拟机器人运动仿真平台CoppeliaSim构建虚拟场景,实现了机器人注胶过程的运动仿真,并进行机器人运动控制与检验。在此过程中,利用Visual Studio 2022设计了人机交互界面和基于C#的串口通信模块,以实现界面、仿真平台和机器人的有效通信,达到虚拟模型与注胶机器人的动态实时交互的目的。在系统控制检测方面,对机器人进行了正逆运动学分析,结合包围盒算法和GJK算法实现了机器人碰撞检测。实验结果表明,注胶机器人能够有效响应控制指令,响应延迟小于200 ms,界面能够实时显示运动参数,参数误差小于5%,动态交互成功实现。该系统为玻璃幕墙注胶行业提供了技术支持与解决方案,有效提升了生产效率并降低了成本。
文摘The paper presents a fuzzy Q-learning(FQL)and optical flow-based autonomous navigation approach.The FQL method takes decisions in an unknown environment and without mapping,using motion information and through a reinforcement signal into an evolutionary algorithm.The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are“dense”or“thin”which has a relationship with the proximity of objects.The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component.The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python.
文摘Planning a reasonable driving path for trucks in mining areas is a key point to improve mining efficiency.In this paper,a path planning method based on Rapidly-exploring Random Tree Star(RRT∗)is proposed,and several optimizations are carried out in the algorithm.Firstly,the selection process of growth target points is optimized.Secondly,the process of selecting the parent node is optimized and a Dubins curve is used to constraint it.Then,the expansion process from tree node to random point is optimized by the gravitational repulsion field method and dynamic step method.In the obstacle detection process,Dubins curve constraint is used,and the bidirectional RRT∗algorithm is adopted to speed up the iteration of the algorithm.After that,the obtained paths are smoothed by using the greedy algorithm and cubic B-spline interpolation.In addition,to verify the superiority and correctness of the algorithm,an unmanned mining vehicle kinematic model in the form of frontwheel steering is developed based on the Ackermann steering principle and simulated for CoppeliaSim.In the simulation,the Stanley algorithm is used for path tracking and Reeds-Shepp curve to adjust the final parking attitude of the truck.Finally,the experimental comparison shows that the improved bidirectional RRT∗algorithm performs well in the simulation experiment,and outperforms the common RRT∗algorithm in various aspects.
