多机器人编队与阵型控制的设计一种融合RH进LF的方法
摘要
在过去的几十年中,所有机器人研究领域的学者和专家在多机器人系统的协同控制投入了相当大的研究,整个机器人研究领域的专家们相信多机器人系统能完成很多单机器人所不能完成的任务。做为多机器人研究领域中的一个重要分支,机器人阵型控制的目标为控制小组中的机器人之间的位置和角度关系,以使得整个机器人小组能如一个单一整体运动。为了能在动态和不确定的环境中执行多样性的任务,小组中的机器人必须能够对环境的改变做出针对性的应对,并且能够对小组任务不可预知的改变做出应对。阵型控制的主要内容包括:可行阵型的分配,目标阵型的形成,阵型的保持,不同阵型之间的转化等等。
这篇论文的主要目的是研发一种新的Leader-Follower(引导者-跟随者,在整篇论文中我们将使用Leader-Follower)控制框架,用以控制一组非完整性(nonholonomic)移动机器人的阵型任务,并且融合Receding-Horizon控制算法以改进机器人小组的阵型收敛速度。整个阵型控制框架可以分为以下几个组要组成部分。
首先,我们提出了一种新的Leader-Follower控制算法,不仅用以控制机器人之间的separation和bearing,而且控制机器人之间的orientation。假定目标separation,bearing和orientation都已经预先给出,我们提出了两种控制方法:Separation-Bearing-Orientation Scheme(SBOS)用以两个机器人的阵型控制和Separation-Separation-Orientation Scheme(SSOS)用以三个机器人的阵型控制。与现有的Leader-Follower方法相比,我们的新算法对机器人之间的orientation做了直接控制,对orientation的直接控制使得我们的Leader-Follower算法更效率,而且能解决很多现存Leader-Follower方法不能解决的阵型问题。例如,当机器人小队在前方遇到危险时,小队必须排成预设队形,有条理的后退。而现存的Leader-Follower并不能解决阵型后退问题,但是这个问题被我们很好的解决了。不仅如此,当延伸我们的方法为Separation-Obstacle-Orientation Scheme(SBOS)时,Leader-Obstacle问题也能很好的得到解决。
其次,我们将Receding-Horizon方法融入到Leader-Follower阵型控制器中去,以改进阵型控制的跟踪误差,进而改进整个机器人小组的阵型控制。在阵型控制中,对跟踪误差的快速收敛是关键而且十分必要的,因为跟踪误差的收敛速度直接决定了整个机器人小组阵型控制的好坏程度。我们通过对Receding-Horizon控制过程的恰当设计,提出了一种新的Leader-Follower Receding-Horizon(RH-LF)阵型控制器。这种控制器能使阵型跟踪误差在Receding-Horizon控制器的terminal-state-region(直译为终态区域,为防止概念混淆和模糊,我们在整篇论文中使用原文terminal-state-region)中能以指数级别收敛。需要指出的一点是,现有的所有阵型控制器只是分析了控制器的稳定性,而对于控制器在稳定性基础上的优化则鲜有研究。
再次,我们在实验室中以一个多机器人小组为平台,做了很多踏实的实验性研究。实验的结果显示出了我们新提出的Leader-Follower Receding-Horizon阵型控制器的优越性,不仅用于常规的阵型控制任务,而且在一些特殊的阵型控制任务中,RH-LF也体现出了其优越性,例如阵型后退实验等等。通过将Receding-Horizon和Leader-Follower控制器想结合,我们提出了一种新的覆盖面更广,效率更高的多机器人阵型控制器。
这篇论文的主要目的是研发一种新的Leader-Follower(引导者-跟随者,在整篇论文中我们将使用Leader-Follower)控制框架,用以控制一组非完整性(nonholonomic)移动机器人的阵型任务,并且融合Receding-Horizon控制算法以改进机器人小组的阵型收敛速度。整个阵型控制框架可以分为以下几个组要组成部分。
首先,我们提出了一种新的Leader-Follower控制算法,不仅用以控制机器人之间的separation和bearing,而且控制机器人之间的orientation。假定目标separation,bearing和orientation都已经预先给出,我们提出了两种控制方法:Separation-Bearing-Orientation Scheme(SBOS)用以两个机器人的阵型控制和Separation-Separation-Orientation Scheme(SSOS)用以三个机器人的阵型控制。与现有的Leader-Follower方法相比,我们的新算法对机器人之间的orientation做了直接控制,对orientation的直接控制使得我们的Leader-Follower算法更效率,而且能解决很多现存Leader-Follower方法不能解决的阵型问题。例如,当机器人小队在前方遇到危险时,小队必须排成预设队形,有条理的后退。而现存的Leader-Follower并不能解决阵型后退问题,但是这个问题被我们很好的解决了。不仅如此,当延伸我们的方法为Separation-Obstacle-Orientation Scheme(SBOS)时,Leader-Obstacle问题也能很好的得到解决。
其次,我们将Receding-Horizon方法融入到Leader-Follower阵型控制器中去,以改进阵型控制的跟踪误差,进而改进整个机器人小组的阵型控制。在阵型控制中,对跟踪误差的快速收敛是关键而且十分必要的,因为跟踪误差的收敛速度直接决定了整个机器人小组阵型控制的好坏程度。我们通过对Receding-Horizon控制过程的恰当设计,提出了一种新的Leader-Follower Receding-Horizon(RH-LF)阵型控制器。这种控制器能使阵型跟踪误差在Receding-Horizon控制器的terminal-state-region(直译为终态区域,为防止概念混淆和模糊,我们在整篇论文中使用原文terminal-state-region)中能以指数级别收敛。需要指出的一点是,现有的所有阵型控制器只是分析了控制器的稳定性,而对于控制器在稳定性基础上的优化则鲜有研究。
再次,我们在实验室中以一个多机器人小组为平台,做了很多踏实的实验性研究。实验的结果显示出了我们新提出的Leader-Follower Receding-Horizon阵型控制器的优越性,不仅用于常规的阵型控制任务,而且在一些特殊的阵型控制任务中,RH-LF也体现出了其优越性,例如阵型后退实验等等。通过将Receding-Horizon和Leader-Follower控制器想结合,我们提出了一种新的覆盖面更广,效率更高的多机器人阵型控制器。
引文
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