动态多行人环境中基于最优交互避碰的机器人导航
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摘要
在动态的多行人环境中,服务机器人仅依赖于自身传感器、以第一人称视角自主导航时.机器人自主定位的不确定性以及对周围行人运动状态估计的不确定性均增加,这给机器人导航决策带来了困难.为解决这个问题,提出一种基于最优交互避碰的机器人自主导航法.本方法采用一种改进的粒子PHD滤波法即NP-PHDF法跟踪多个行人的状态.NP-PHDF法结合了卡尔曼粒子滤波及PHD滤波优点,因此它可以跟踪数目变化的多个目标,能够跟踪突然的加减速以及急转弯运动,并且能够抵抗遮挡.同时,与基于粒子滤波的机器人自主定位法类似,NP-PHDF法使得行人运动状态的不确定性能够以粒子的分布来度量.为降低状态估计的不确定性,本文提出一种"圈粒子"的粒子圈存法从粒子的分布中提取机器人和行人的真实状态.算法的有效性在实际场景实验中得到了验证.
In dynamic multi-pedestrian environments, while a service robot navigates only relying on its own sensors with the first-person perspective, both the uncertainties of robot localization and the estimation of people's states are increased, which hinder the navigation decision of a service robot. To solve this problem, a local collision avoidance method based on the optimal reciprocal collision avoidance(ORCA) is proposed. In this method, the states of multiple pedestrians are estimated by a variant of particle-PHD filter, i.e. NP-PHDF for multi-target tracking. NP-PHDF is a combination of Kalman particle filter and PHD filter and has their advantages. So it can not only track time-varying number of targets with sudden motion changes such as abrupt acceleration/deceleration or steep turn, but also resist block among pedestrians. Meanwhile, similar as robot localization which uses particle filter, the uncertainties of estimation for pedestrians can be represented by the distribution of particles. To reduce the uncertainties, an encircling-particles method is proposed to refine the true states of robot and pedestrians from the probabilistic particle distribution. The effectiveness of the proposed technique is demonstrated through experiments in real environments.
In dynamic multi-pedestrian environments, while a service robot navigates only relying on its own sensors with the first-person perspective, both the uncertainties of robot localization and the estimation of people's states are increased, which hinder the navigation decision of a service robot. To solve this problem, a local collision avoidance method based on the optimal reciprocal collision avoidance(ORCA) is proposed. In this method, the states of multiple pedestrians are estimated by a variant of particle-PHD filter, i.e. NP-PHDF for multi-target tracking. NP-PHDF is a combination of Kalman particle filter and PHD filter and has their advantages. So it can not only track time-varying number of targets with sudden motion changes such as abrupt acceleration/deceleration or steep turn, but also resist block among pedestrians. Meanwhile, similar as robot localization which uses particle filter, the uncertainties of estimation for pedestrians can be represented by the distribution of particles. To reduce the uncertainties, an encircling-particles method is proposed to refine the true states of robot and pedestrians from the probabilistic particle distribution. The effectiveness of the proposed technique is demonstrated through experiments in real environments.
引文
1张铮,李振波,胡寿伟,等.混合传感器网络中的移动机器人导航方法.机器人,2014,36(3):316–321.
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4 Ulrich I,Borenstein J.VFH*:Local obstacle avoidance with look-ahead verification.Proc.of the International Conference of Roboticsand Automation(ICRA).2000,3.2505–2511.
5 Hennes D,Claes D,Meeussen W,Tuyls K.Multi-robot collision avoidance with localization uncertainty.Proc.of the 11 th International Conference on Autonomous Agentsand Multiagent Systems.2012,2.672–679.
6 王勇,陈卫东,王景川,肖鹏.面向动态高遮挡环境的移动机器人自适应位姿跟踪算法,机器人,2015,(1):241–252.
7 Alonso-Mora J,Breitenmoser A,Rufli M,Beardsley PA,Siegwart R.Optimal reciprocal collision avoidance for multiple non-holonomic robots.Distributed Autonomous Robotic Systems-The 10th International Symposium.Lausanne,Switzerland.2010.203–216.
8 Ishibashi M,Iwashita Y,Kurazume R.Noise-estimate particle phd filter.Proc.IEEE World Automation Congress(WAC).Waikoloa,HI.2014.784–789.
9 Fox D.Adapting the sample size in particle filters through kld-sampling.The International Journal of Robotics Research,2003,22(12):985–1003.
10 Arras KO,Mozos OM,Burgard W.Using boosted features for the detection of people in 2nd range data.IEEE International Conference on Robotics and Automation.2007.3402–3407.
2 陈洋,赵新刚,韩建达.移动机器人三维路径规划方法综述.机器人,2010,32(4):568–576.
3 Pradhan N,Burg T,Birchfield S.Robot crowd navigation usingpredictive position fields in the potential function framework.Proc.of the American Control Conference.2011,5991384.4628–4633.
4 Ulrich I,Borenstein J.VFH*:Local obstacle avoidance with look-ahead verification.Proc.of the International Conference of Roboticsand Automation(ICRA).2000,3.2505–2511.
5 Hennes D,Claes D,Meeussen W,Tuyls K.Multi-robot collision avoidance with localization uncertainty.Proc.of the 11 th International Conference on Autonomous Agentsand Multiagent Systems.2012,2.672–679.
6 王勇,陈卫东,王景川,肖鹏.面向动态高遮挡环境的移动机器人自适应位姿跟踪算法,机器人,2015,(1):241–252.
7 Alonso-Mora J,Breitenmoser A,Rufli M,Beardsley PA,Siegwart R.Optimal reciprocal collision avoidance for multiple non-holonomic robots.Distributed Autonomous Robotic Systems-The 10th International Symposium.Lausanne,Switzerland.2010.203–216.
8 Ishibashi M,Iwashita Y,Kurazume R.Noise-estimate particle phd filter.Proc.IEEE World Automation Congress(WAC).Waikoloa,HI.2014.784–789.
9 Fox D.Adapting the sample size in particle filters through kld-sampling.The International Journal of Robotics Research,2003,22(12):985–1003.
10 Arras KO,Mozos OM,Burgard W.Using boosted features for the detection of people in 2nd range data.IEEE International Conference on Robotics and Automation.2007.3402–3407.