【作者】 王聪颖；

【导师】 何衍；

【作者基本信息】 浙江大学 ， 控制理论与控制工程， 2010， 硕士

【摘要】 多机器人编队指一组机器人保持一定的队形共同运动的过程,是多机器人系统研究的重要内容,可应用于航空、航天、交通、军事等多个领域。目前学者已提出了许多典型的编队方法,包括领队-跟踪法、虚拟结构法、行为主义方法等。这些方法均能保证编队控制的收敛性,但主要解决的是系统内部的协调问题,对外部环境带来的影响,没有充分考虑,因此无法直接应用于普遍存在动、静态障碍物的生活环境,大大限制了多机器人编队在服务机器人领域的应用。为了解决上述问题,我们将编队分解为轨迹和队形两个部分,通过路径规划和行为切换来实现障碍环境的安全运动和队形保持,具体做了以下工作:1.讨论了编队控制的三层结构,自上而下包括任务层、规划层和控制层三个部分,并对基于领队跟踪法的队形保持和基于人工势场法的运动控制进行了较为详细的分析。2.针对编队运动过程中队形保持和避障要求之间的矛盾,提出了一种静态障碍物环境下的刚性编队方法。该方法通过各个机器人传感器信息的融合、以及角速度的设计,使编队中的机器人可作为一个整体进行避障。实验表明,该方法不仅可以使系统在避障中保持指定的队形,还可以保证避障的轨迹足够光滑。3.针对障碍环境中的动态障碍物一般具有自主性的特点,提出了一种基于有限状态机FSM(Finite State Machine)的刚性编队方法。该方法在动、静态障碍物的识别的基础上,通过编队速度的切换来实现障碍物的规避。实验表明,该方法不仅可以保证避障过程中的队形完整,还具有实现简单的特点。4.为了消除机器人里程计定位的累积误差,针对生活环境中一般存在摄像头监控的情况,研究了基于单摄像头的视觉定位问题。通过合适的标记设计,提出了一种基于RGB分析和形态学处理的机器人中心定位方法。实验表明,该算法不仅可以达到满意的定位精度,还可应用于不同的光照场合,消除地面反光的干扰。上述工作的有效性均得到了仿真和实验的验证,具有向更复杂环境推广的价值和潜力。更多还原

【Abstract】 Multi-robot formation system refers to a group of robots which move together with a certain formation. It is important in research of multi-robot system, and can be applied in many fields like aviation, aerospace, transportation, military, and so on. So far, many typical formation methods have already been proposed, including leader-follower, virtual structure, behaviorism, etc. These methods can guarantee the convergence of the whole formation. However, what they mainly focus on is the inner problem, but not enough for the outer ones. Thus, they can’t be used in living environment in which there exist lots of obstacles both static and dynamic. As a result, it narrows the application of multi-robot formation system in the field of service robots. So, here we divide the whole formation system into two parts - trajectory and formation shape, and realize its safety and formation maintenance in obstacle-environment through path-planning and behavior-switching. Details are as follows:1. A 3-level scheme of the formation control is discussed, including Task Level, Planning Level and Control Level. The formation maintenance based on leader-follower method and movement-control based on artificial potential method is also analysised in details.2. To deal with the contradiction between formation maintenance and obstacle avoidance, a method of rigid formation control in environment with static obstacles in it is propose. It regards the robots in formation as an entirety by fusing information of sensors of every individual robot and designing a particular angular velocity. And this has already been tested and verified by experiments.3. As common dynamic obstacles exist in living environment are usually autonomous, a method of rigid formation control based on FSM (Finite State Machine) is proposed. On the basis of recognition of dynamic and static obstacles, it avoids the obstacles successfully by changing the velocity of formation. It can guarantee the integrity of the formation while avoiding the obstacles, and is easy to realize as well. This has already been tested and verified by experiments. 4. In order to clear the accumulated error taken by the robot odometer, considering cameras usually exist in obstacle environments, we study how to locate obstacles by a single camera. This thesis proposed a method of locating the robot center based on the RGB-analysis and morphology with a kind of appropriate marks. It has been tested and verified by experiments to have enough accuracy and can be used in different backgrounds, especially where there is reflective.The validity of all mentioned above has been tested and verified by simulations and experiments, and they have the potentials and are valuable for more complicated environments.更多还原