预测行人运动的服务机器人POMDP导航  

POMDP Navigation of Service Robots with Human Motion Prediction

作　　者：钱堃[1] 马旭东[1] 戴先中[1] 房芳[1] 

机构地区：[1]东南大学自动化学院复杂工程系统测量与控制教育部重点实验室,江苏南京210096

出　　处：《机器人》2010年第1期18-24,33,共8页Robot

基　　金：国家863计划资助项目(2006AA040202;2007AA041703);国家自然科学基金资助项目(60805032)

摘　　要：为提高室内动态环境下服务机器人对行人的自然避让能力,对人的运动轨迹模式进行建模,在此基础上引入了将行人运动长、短期预测结合起来的方法.为适应传感器噪声及网络延迟等因素所造成的感知—控制回路中的多源不确定性,将人与机器人的相对位置关系建模为部分可观的马尔可夫状态.采用部分可观的马尔可夫决策过程(POMDP)进行多源不确定性下的概率决策,协调控制机器人全局路径规划、反应式运动及速度控制等行为模块.实验结果验证,它能够实现提前避碰的安全导航,因避免反复的曲折与徘徊运动而提高了机器人导航效率.To improve the natural pedestrian-avoidance skills of service robots in indoor dynamic environments, a method of combining long-term and short-term prediction of pedestrian＇s motion is introduced on the basis of modeling humans＇ motion trajectory patterns. In order to accommodate the uncertainties in the perception-control loop of robots, which are mainly caused by sensor noise and time delay in network and other factors, the relative position relation between human and robot is modeled as partially observable Markov state. Partially observable Markov decision process （POMDP） is utilized for probabilistic decision-making under multi-source uncertainties, and the behavior modules of the global path planner, the motion reactor and the speed controller, are coordinated. Experimental results illustrate the performance of safe navigation that can avoid conflicts in advance, as well as the improved robot navigation efficiency by avoiding repeated zigzaging and wandering motion.

关 键 词：预测导航 运动估计 不确定性 POMDP 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]