未知环境中基于相对观测量的多机器人合作定位研究
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摘要
随着机器人技术在各个领域的广泛应用和发展,在研究和应用双重需求的推动下,多机器人系统的研究已受到国内外研究机构和产业界的重视,并逐步成为一个充满活力、充满挑战性的领域。在一些面向任务的应用中,如军事领域、航空航天领域、服务行业、机器人足球赛等,多机器人的协同协作发挥了重要作用。多机器人的协同协作离不开机器人的位置信息,如何利用多机器人的协同合作来提高整个机器人群的定位能力、定位精度,无疑是多机器人研究中一个重要且关键的课题。
本文围绕多机器人研究中基本且关键的定位问题,深入研究了多机器人群在未知环境中基于相对观测量和自身运动进行协同、合作定位的技术。针对系统结构、观测量选择、运动轨迹优化及粒子滤波器的应用及改进等方面进行了深入的研究。
论文首先深入研究了多机器人合作定位的系统模型,在此基础上,推导了利用扩展卡尔曼滤波方法融合运动信息及相对观测信息同时定位机器人群中每个机器人的滤波方程,详细分析了基于相对方位观测量的定位特点,针对利用相对方位观测量时某些相对分布条件下易出现滤波发散的情况,提出了一种改进的EKF滤波方法,提高了算法的鲁棒性和可靠性,并通过计算机仿真和Nubot机器人平台上获得的实验结果和数据,验证了合作定位的有效性及改进算法的实用性。
其后在合作定位系统模型的基础上,针对随着机器人群规模的增大,导致定位计算量迅速增长这一问题,主要研究了如何在众多的观测量中选择那些更有利于合作定位的观测信息,提出了一种基于熵的观测量选择算法以选择具有较大信息增益的观测量,从而提高了定位的效率和实时性,并对此算法进行了计算机仿真,比较分析了观测量的信息增益及个数对定位精度、计算时间的影响,证明了该算法的正确性和有效性;论文分析讨论了如何将集中式的滤波计算分布到各个机器人平台上,实现分布式的定位计算,以降低计算的复杂度,增强系统的灵活性。
论文还讨论了衡量相对位置分布及运动轨迹的优化准则,在此基础上,深入分析了在多种不同情形下机器人基于相对距离观测量的定位误差分布;研究了多机器人在给定起点和终点的情况下,如何运动才能使合作定位的精度更高,在一定约束条件下给出了一种寻求最优轨迹的方法,并通过计算机仿真证明,对机器人群的运动轨迹进行规划设计,可以明显提高合作定位精度。
针对未知环境中具有不同观测能力的机器人群的全局定位问题,研究了粒子滤波及其与扩展卡尔曼滤波相结合的应用,在此基础上,提出了将粒子滤波与扩展卡尔曼滤波相结合的定位模型和方法,充分利用粒子滤波器的鲁棒性、适应性与扩展卡尔曼滤波的高效率、实时性,从而使定位的稳健性和实时性得到进一步提高,并通过计算机仿真和Nubot机器人平台上获得的实验数据,验证了该方法的有效性。
针对初始误差大及观测误差较大的情况,对Unscented Kalman Filter进行了改进(IUKF),与EKF、UKF算法相比,新算法不仅适应能力强、稳定性高,而且收敛速度快、跟踪误差小,是一种稳健的定位跟踪算法;以IUKF来产生重要性概率密度函数,提出了改进的Unscented Particle Filter(IUPF),得到了更稳健的滤波特性和更好的滤波精度,并通过计算机仿真和实测数据,验证了改进的IUPF的性能;为提高计算的实时性,针对高斯粒子滤波器提出了并行的算法和结构,并利用有五台计算机组成的多处理机系统验证了并行算法的可行性。
在论文的最后,总结了整个论文的工作,指出了进一步研究探索的方向。
Robotics has already been widely developed and used in many fields. Urged by investigation and application, research of multi-robot system has received more and more attention, and it is gradually becoming an active field full of challenge. In some tasks, such as military operation, aviation, services and RoboCup, cooperation of multi-robot is extremely important. In order to cooperate, robots must know their location. So it is an important and key problem to improve the capabilities and accuracy of cooperative localization.
This work focuses on the basic and important problem in multiple robots research: cooperative localization. It addresses the problem of cooperative localization of a mobile robot team based on the relative observations and motions in unknown environments. A few issues are investigated, including the complete model structure of cooperative localization, selection of measurements, optimal motion strategies, and application and improvement of Particle Filter.
First, the complete model of cooperative localization of multi-robot is discussed. Based on the model, the Extended Kalman Filter is used to fuse the data of motion and the relative measurements to localize every member of the mobile robot team. The filter equations are deduced and the characteristics of the observations are analyzed thoroughly. Contraposing the unconverged cases of localization in some conditions based on only relative bearings, we mend the filter process so as to improve the practicability and reliability. Simulations and real experiments on the Nubot platform have been done to prove the validity of the method.
Second, we consider the problem of the increasing computational burden as the size of the team is becoming large. We discuss how to select the better measurements among all of those ones obtained by the group. We present a method to select those measurements which yield the most information gain in estimating robots location. The seleted measurements are used to update the whole group pose estimation and the covariance matrix. It ensures the necessary localization accuracy and meantime reduces the computational burden, so as to improve the reliability and real-time of localization. We compare the localization accuracy and the computation time by using different number of measurements. Simulation results show that the proposed method can effectively improve the efficiency in dealing with multi-robot localization, especially when the group is large. The distributed filter computation is discussed. A few communicating filters cooperate to process the localization computation by exchange of information. This decentralized structure is helpful to reduce the complexity of computation and increase the flexibility of the system.
Then, we also discuss the problem of optimal trajectories of multiple robots. The localization covariance is taken as the cost function. Error distribution of localization based on relative distance in different situations is analyzed thoroughly. Under some constraints we study the optimal motion strategies of the multiple robots so that the localization uncertainty can be minimized. Simulation experiments prove that the optimal trajectories can improve the accuracy of cooperative localization than the general motion strategies.
The cooperative localization of heterogeneous robots without their initial positions is investigated. The implementations of Particle Filter (PF) and combination of EKF and PF in cooperative localization are studied. We present a method to combine EKF with PF in dealing with multi-robot absolute localization. It makes use of the efficiency and real-time of EKF, and the robust and adaptability of PF to improve the localization performance. Simulations and real data experiment acquired with Nubot platform have been done to verify the method. We propose an improved Unscented Kalman Filter (IUKF). Its performance is more robust than UKF in dealing with noisy measurements. We propose the use of the IUKF for proposal generation to combine new observation with prior probability distribution. Based on it, the improved Unscented Particle Filter (IUPF) is presented. Through simulation and real data experiment, The IUPF is proved to have better performance than UPF. The parallel structure of Gaussian Particle Filter is presented to process the computation in order to get real time implementation. This parallel structure is verified by a group of five computers system.
Finally, we summarize the general work of this thesis and give a short outlook on possible future research.
本文围绕多机器人研究中基本且关键的定位问题,深入研究了多机器人群在未知环境中基于相对观测量和自身运动进行协同、合作定位的技术。针对系统结构、观测量选择、运动轨迹优化及粒子滤波器的应用及改进等方面进行了深入的研究。
论文首先深入研究了多机器人合作定位的系统模型,在此基础上,推导了利用扩展卡尔曼滤波方法融合运动信息及相对观测信息同时定位机器人群中每个机器人的滤波方程,详细分析了基于相对方位观测量的定位特点,针对利用相对方位观测量时某些相对分布条件下易出现滤波发散的情况,提出了一种改进的EKF滤波方法,提高了算法的鲁棒性和可靠性,并通过计算机仿真和Nubot机器人平台上获得的实验结果和数据,验证了合作定位的有效性及改进算法的实用性。
其后在合作定位系统模型的基础上,针对随着机器人群规模的增大,导致定位计算量迅速增长这一问题,主要研究了如何在众多的观测量中选择那些更有利于合作定位的观测信息,提出了一种基于熵的观测量选择算法以选择具有较大信息增益的观测量,从而提高了定位的效率和实时性,并对此算法进行了计算机仿真,比较分析了观测量的信息增益及个数对定位精度、计算时间的影响,证明了该算法的正确性和有效性;论文分析讨论了如何将集中式的滤波计算分布到各个机器人平台上,实现分布式的定位计算,以降低计算的复杂度,增强系统的灵活性。
论文还讨论了衡量相对位置分布及运动轨迹的优化准则,在此基础上,深入分析了在多种不同情形下机器人基于相对距离观测量的定位误差分布;研究了多机器人在给定起点和终点的情况下,如何运动才能使合作定位的精度更高,在一定约束条件下给出了一种寻求最优轨迹的方法,并通过计算机仿真证明,对机器人群的运动轨迹进行规划设计,可以明显提高合作定位精度。
针对未知环境中具有不同观测能力的机器人群的全局定位问题,研究了粒子滤波及其与扩展卡尔曼滤波相结合的应用,在此基础上,提出了将粒子滤波与扩展卡尔曼滤波相结合的定位模型和方法,充分利用粒子滤波器的鲁棒性、适应性与扩展卡尔曼滤波的高效率、实时性,从而使定位的稳健性和实时性得到进一步提高,并通过计算机仿真和Nubot机器人平台上获得的实验数据,验证了该方法的有效性。
针对初始误差大及观测误差较大的情况,对Unscented Kalman Filter进行了改进(IUKF),与EKF、UKF算法相比,新算法不仅适应能力强、稳定性高,而且收敛速度快、跟踪误差小,是一种稳健的定位跟踪算法;以IUKF来产生重要性概率密度函数,提出了改进的Unscented Particle Filter(IUPF),得到了更稳健的滤波特性和更好的滤波精度,并通过计算机仿真和实测数据,验证了改进的IUPF的性能;为提高计算的实时性,针对高斯粒子滤波器提出了并行的算法和结构,并利用有五台计算机组成的多处理机系统验证了并行算法的可行性。
在论文的最后,总结了整个论文的工作,指出了进一步研究探索的方向。
Robotics has already been widely developed and used in many fields. Urged by investigation and application, research of multi-robot system has received more and more attention, and it is gradually becoming an active field full of challenge. In some tasks, such as military operation, aviation, services and RoboCup, cooperation of multi-robot is extremely important. In order to cooperate, robots must know their location. So it is an important and key problem to improve the capabilities and accuracy of cooperative localization.
This work focuses on the basic and important problem in multiple robots research: cooperative localization. It addresses the problem of cooperative localization of a mobile robot team based on the relative observations and motions in unknown environments. A few issues are investigated, including the complete model structure of cooperative localization, selection of measurements, optimal motion strategies, and application and improvement of Particle Filter.
First, the complete model of cooperative localization of multi-robot is discussed. Based on the model, the Extended Kalman Filter is used to fuse the data of motion and the relative measurements to localize every member of the mobile robot team. The filter equations are deduced and the characteristics of the observations are analyzed thoroughly. Contraposing the unconverged cases of localization in some conditions based on only relative bearings, we mend the filter process so as to improve the practicability and reliability. Simulations and real experiments on the Nubot platform have been done to prove the validity of the method.
Second, we consider the problem of the increasing computational burden as the size of the team is becoming large. We discuss how to select the better measurements among all of those ones obtained by the group. We present a method to select those measurements which yield the most information gain in estimating robots location. The seleted measurements are used to update the whole group pose estimation and the covariance matrix. It ensures the necessary localization accuracy and meantime reduces the computational burden, so as to improve the reliability and real-time of localization. We compare the localization accuracy and the computation time by using different number of measurements. Simulation results show that the proposed method can effectively improve the efficiency in dealing with multi-robot localization, especially when the group is large. The distributed filter computation is discussed. A few communicating filters cooperate to process the localization computation by exchange of information. This decentralized structure is helpful to reduce the complexity of computation and increase the flexibility of the system.
Then, we also discuss the problem of optimal trajectories of multiple robots. The localization covariance is taken as the cost function. Error distribution of localization based on relative distance in different situations is analyzed thoroughly. Under some constraints we study the optimal motion strategies of the multiple robots so that the localization uncertainty can be minimized. Simulation experiments prove that the optimal trajectories can improve the accuracy of cooperative localization than the general motion strategies.
The cooperative localization of heterogeneous robots without their initial positions is investigated. The implementations of Particle Filter (PF) and combination of EKF and PF in cooperative localization are studied. We present a method to combine EKF with PF in dealing with multi-robot absolute localization. It makes use of the efficiency and real-time of EKF, and the robust and adaptability of PF to improve the localization performance. Simulations and real data experiment acquired with Nubot platform have been done to verify the method. We propose an improved Unscented Kalman Filter (IUKF). Its performance is more robust than UKF in dealing with noisy measurements. We propose the use of the IUKF for proposal generation to combine new observation with prior probability distribution. Based on it, the improved Unscented Particle Filter (IUPF) is presented. Through simulation and real data experiment, The IUPF is proved to have better performance than UPF. The parallel structure of Gaussian Particle Filter is presented to process the computation in order to get real time implementation. This parallel structure is verified by a group of five computers system.
Finally, we summarize the general work of this thesis and give a short outlook on possible future research.
引文
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