基于SLAM算法的机器人智能激光定位技术的研究
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摘要
为了提高机器人行走的自主地图规划能力,需要进行机器人智能定位设计,提出一种基于同时定位与地图创建技术(SLAM)的机器人智能激光定位技术。采用激光传感器量化跟踪融合方法构建机器人同时定位地图创建模型,根据机器人的环境适应度建立机器人运动空间环境,在存在障碍物的环境下采用最短路径寻优算法进行机器人信息素规划,在运动方向一致性条件下设计SLAM算法构建避障规则,结合激光传感技术实现机器人的移动路径定位。仿真结果表明,采用该方法进行机器人智能激光定位精度较高,机器人的路径规划准确性较好,避障能力较强。
In order to improve the ability of autonomous map planning for robot walking,it is necessary to design the intelligent location of robot. A robot intelligent laser positioning technology based on simultaneous localization and map creation technology( SLAM) is proposed. Using the laser sensor quantization tracking fusion method to construct the robot location map creation model,according to the robot environment fitness,the robot motion space environment is established. In the presence of obstacles,the shortest path optimization algorithm is used for robot pheromone planning,the SLAM algorithm is designed under the condition of consistent motion direction to construct obstacle avoidance rules,and the laser sensing technology is used to locate the mobile path of the robot. The simulation results show that the intelligent laser positioning accuracy is higher,the path planning accuracy is better and the obstacle avoidance ability is better.
In order to improve the ability of autonomous map planning for robot walking,it is necessary to design the intelligent location of robot. A robot intelligent laser positioning technology based on simultaneous localization and map creation technology( SLAM) is proposed. Using the laser sensor quantization tracking fusion method to construct the robot location map creation model,according to the robot environment fitness,the robot motion space environment is established. In the presence of obstacles,the shortest path optimization algorithm is used for robot pheromone planning,the SLAM algorithm is designed under the condition of consistent motion direction to construct obstacle avoidance rules,and the laser sensing technology is used to locate the mobile path of the robot. The simulation results show that the intelligent laser positioning accuracy is higher,the path planning accuracy is better and the obstacle avoidance ability is better.
引文
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[2]柳超,李秀友,黄勇.优化的多模型粒子滤波机动微弱目标检测前跟踪方法[J].信号处理2015,31(9):1131-1137.
[3]柳长安,鄢小虎,刘春阳,等.基于改进蚁群算法的移动机器人动态路径规划方法[J].电子学报,2011,39(5):1220-1224.
[4]阮鹏,潘其坤,谢冀江,等.放电引发非链式DF激光器脉冲特性研究[J].红外与激光工程,2015,44(12):3554-3558.
[5]游有鹏,张宇,李成刚.面向直接示教的机器人零力控制[J].机械工程学报,2014,50(3):10-17.
[6]REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks[M].Advances in Neural Information Processing Systems.Cambridge,USA:MIT Press,2015:91-99.
[7]JOHNS E,LEUTENEGGER S,DAVISON A J.Deep learning a grasp function for grasping under gripper pose uncertainty[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems.Piscataway,USA:IEEE,2016:4461-4468.
[8]邓中亮,张森杰,焦继超,等.基于高精度室内位置感知的大数据研究与应用[J].计算机应用,2016,36(2):295-300.
[9]吴鸿华,穆勇,屈忠锋,等.基于面板数据的接近性和相似性关联度模型[J].控制与决策,2016,31(03):555-558.
[10]MOHAN B,GOVARDHAN A.Online aggregation using MapReduce in MongoDB[J].International Journal of Advanced Research in Computer Science and Software Engineering,2013,3(9):1157-1165.
[11]王强,张合新,孟飞,等.基于结构相似度评价方法的窗口效应[J].激光技术,2016,40(6):902-906.
[12]王磊,郝士琦,张岱,等.大气激光通信中自适应调制编码技术的模式选择阈值[J].激光与光电子进展,2017,54(2):111-117.
[13]钱金菊,王柯,王锐,等.变电站智能机器人巡检任务规划[J].广东电力,2017,30(2):143-149.
[14]王三秀,俞立,徐建明,等.机械臂自适应鲁棒轨迹跟踪控制[J].控制工程,2015,22(2):241-244.
[15]赵石磊,郭红,刘宇鹏.基于轨迹跟踪的线性时滞系统容错控制[J].信息与控制,2015,44(4):469-473.
[16]LIU H,DING Z G,FAN P Z,et al.Precoding design for interference suppression in multi-cell multi-user networks[J].IET Communications,2014,8(9):1534-1540.
[17]HAN S I,LEE J M.Fuzzy echo state neural networks and funnel dynamic surface control for prescribed performance of a nonlinear dynamic system[J].IEEE Transactions on Industrial Electronics,2014,61(2):1099-1112.