目的解决导向辊生产车间物料输送AGV的激光传感器存在的信息复杂度低、重复率高,且在不断迭代重采样过程中极易丢失正确位姿附近粒子造成定位失败等问题。方法提出一种基于视觉的自适应蒙特卡洛定位算法。建立相机观测模型和自动导引运...目的解决导向辊生产车间物料输送AGV的激光传感器存在的信息复杂度低、重复率高,且在不断迭代重采样过程中极易丢失正确位姿附近粒子造成定位失败等问题。方法提出一种基于视觉的自适应蒙特卡洛定位算法。建立相机观测模型和自动导引运输车本体运动模型,对观测模型进行去畸变处理,完成相机标定;设计基于视觉的自适应蒙特卡洛算法,获取特征信息,并用词袋模型进行分类,使用激光雷达构建2D栅格地图,采用特征点匹配估计位姿,实现AGV自我精确定位。结果仿真实验结果表明,本文所提算法与传统自适应蒙特卡洛定位(Adaptive Monte Carlo Localization,AMCL)算法相比,可使机器人更加快速地收敛到精度较高的位姿,具有更好的定位性能。结论基于视觉的AMCL算法设计,实现了导向辊生产车间机器人的高精度定位,优化了作业流程,提高了生产线系统智能化运行水平,可为其他场景定位应用提供参考。展开更多
AGVs dispatching, one of the hot problems in FMS, has attracted widespread interest in recent years. It is hard to dynamically schedule AGVs with pre designed rule because of the uncertainty and dynamic nature of AGVs...AGVs dispatching, one of the hot problems in FMS, has attracted widespread interest in recent years. It is hard to dynamically schedule AGVs with pre designed rule because of the uncertainty and dynamic nature of AGVs dispatching progress, so the AGVs system in this paper is treated as a cooperative learning multiagent system, in which each agent adopts multilevel decision method, which includes two level decisions: the option level and the action level. On the option level, an agent learns a policy to execute a subtask with the best response to the other AGVs’ current options. On the action level, an agent learns an optimal policy of actions for achieving his planned option. The method is applied to a AGVs’ dispatching simulation, and the performance of the AGVs system based on this method is verified.展开更多
文摘目的解决导向辊生产车间物料输送AGV的激光传感器存在的信息复杂度低、重复率高,且在不断迭代重采样过程中极易丢失正确位姿附近粒子造成定位失败等问题。方法提出一种基于视觉的自适应蒙特卡洛定位算法。建立相机观测模型和自动导引运输车本体运动模型,对观测模型进行去畸变处理,完成相机标定;设计基于视觉的自适应蒙特卡洛算法,获取特征信息,并用词袋模型进行分类,使用激光雷达构建2D栅格地图,采用特征点匹配估计位姿,实现AGV自我精确定位。结果仿真实验结果表明,本文所提算法与传统自适应蒙特卡洛定位(Adaptive Monte Carlo Localization,AMCL)算法相比,可使机器人更加快速地收敛到精度较高的位姿,具有更好的定位性能。结论基于视觉的AMCL算法设计,实现了导向辊生产车间机器人的高精度定位,优化了作业流程,提高了生产线系统智能化运行水平,可为其他场景定位应用提供参考。
文摘AGVs dispatching, one of the hot problems in FMS, has attracted widespread interest in recent years. It is hard to dynamically schedule AGVs with pre designed rule because of the uncertainty and dynamic nature of AGVs dispatching progress, so the AGVs system in this paper is treated as a cooperative learning multiagent system, in which each agent adopts multilevel decision method, which includes two level decisions: the option level and the action level. On the option level, an agent learns a policy to execute a subtask with the best response to the other AGVs’ current options. On the action level, an agent learns an optimal policy of actions for achieving his planned option. The method is applied to a AGVs’ dispatching simulation, and the performance of the AGVs system based on this method is verified.
