面向动态高遮挡环境的移动机器人自适应位姿跟踪算法  

Self-adaptive Pose-tracking Algorithm for Mobile Robots in Dynamic and Highly-occluded Environments

作　　者：王勇[1,2] 陈卫东[1,2] 王景川[1,2] 肖鹏[3] 

机构地区：[1]上海交通大学自动化系系统控制与信息处理教育部重点实验室,上海200240 [2]哈尔滨工业大学机器人技术与系统国家重点实验室,黑龙江哈尔滨150001 [3]国网山东省电力公司电力科学研究院,山东济南250002

出　　处：《机器人》2015年第1期112-121,共10页Robot

基　　金：国家863计划资助项目(2012AA041403);国家自然科学基金资助项目(61175088;61221003);机器人技术与系统国家重点实验室开放基金资助项目(SKLRS2011ZD01)

摘　　要：在不同动态、高遮挡场景下(比如食堂、地铁站等),障碍物遮挡的程度各有区别,而地图的特征也不尽相同,移动机器人的位姿跟踪精度会受到较大的影响.针对上述问题,本文提出了一种自适应位姿跟踪算法.核心思想是利用移动机器人定位能力,对粒子滤波中基于里程计的建议分布函数进行修正,以此得到准确、鲁棒的定位结果.其中,定位能力不仅可以反映已知地图(不同场景和噪声)还能反映动态障碍物给定位带来的影响.进而,为了保证算法在不同动态、高遮挡场景下的鲁棒性,对融合过程中调节激光观测和里程计测量之间可信度的参数进行了改进设计.通过仿真和实验,验证了改进的两传感器可信度参数的有效性,同时,也验证了本文算法在动态、高遮挡场景下具有较好的定位精度和鲁棒性.In dynamic and highly-occluded environments such as a cafeteria, metro station etc. the pose-tracking accuracy of mobile robots will be greatly influenced since the occlusion degree and map features are different. To solve this problem, a self-adaptive pose-tracking algorithm is proposed. The main idea is to ensure the accurate and robust robot localization through correcting the odometer-based proposal distribution function （PDF） in particle filter （PF） based on the localizability, which is defined to evaluate the influences of both the dynamic obstacles and prior-map （different structures and uncertainty） on localization. Furthermore, to guarantee the robustness in different dynamic and highly-occluded environments, the relia-bility parameter between the observations of laser range-finder （LRF） and the measurements of odometer in fusion process is improved. The simulation and experimental results demonstrate that the improved reliability parameter is valid, and the proposed algorithm is accurate and robust for pose-tracking in dynamic and highly-occluded environments.

关 键 词：移动机器人 定位 动态高遮挡环境 定位能力 粒子滤波 自适应算法 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]