基于简化虚拟受力模型的群机器人自组织协同围捕研究

发布时间：2018-05-05 22:32

  本文选题：群机器人 + 简化虚拟受力模型　； 参考：《湖南大学》2016年博士论文

【摘要】：群机器人系统研究是最近兴起的一个热点,它受启发于复杂的自然系统,比如社会性昆虫(蚂蚁、蜜蜂等)或者有协作的动物群体,群机器人系统的全局行为涌现自个体机器人层面上实施的局部规则。群机器人围捕系统是群机器人系统研究的一个非常典型的任务平台,具有重要研究价值,有利于实现包围、搜救、群体对抗、队形保持、协作搬运、目标保卫以及领导护卫等,可广泛用于反恐、军事、安全保卫与警戒等方面。然而目前的群机器人围捕系统理论尚不完备,所涉及的围捕环境相对简单,围捕系统的可扩展性不强,多目标围捕算法复杂。针对上述问题,本文研究基于人工物理方法的复杂环境下群机器人围捕系统,主要研究内容如下:(1)未知动态凸障碍环境中非完整移动群机器人围捕。不同于基于松散偏好规则的围捕算法,而是从人工物理角度出发,提出了基于简化虚拟受力模型的围捕理论和算法,该算法只需要两最近邻和目标的位置信息,算法简单高效;简化虚拟受力模型中简单的受力函数实现了参数较易设置的围捕系统;设计的仿生智能避障函数,实现了未知动态凸障碍物环境下的良好避障。在理论上分析了围捕系统的稳定性,研究了Leader涌现的规律。Leader涌现的判断条件是受力出现一个较大值后逐步近似单调衰减,这与基于松散偏好规则围捕时Leader涌现的判断条件正好相反,其原因是简化虚拟受力模型中机器人个体感知两最近邻的施力建模为斥力,Leader距离两最近邻越远斥力越小,这样会使得Leader涌现过程中Leader本身的振荡较小。(2)未知非凸障碍物环境中非完整移动群机器人围捕。提出了基于简化虚拟受力模型的非凸静态障碍物循障算法,该循障算法同样只需要两最近邻和目标的位置信息即可,所设定的与障碍物之间的运动角度加大了与障碍物平行方向上的运动速度,保证了避障的同时快速循障。在理论上分析了系统的稳定性,并给出了成功循障非凸静态障碍物的条件。(3)未知动态变形障碍物环境中群机器人围捕问题。基于简化虚拟受力模型设计了动态变形障碍物循障算法,该算法同样只需要两最近邻和目标的位置信息即可实现,所设定的与障碍物之间的运动角度兼顾了与障碍物垂直方向的速度大小和与障碍物平行方向上的速度大小,可以做到快速循障的同时与动态变形障碍物保持合适的距离,该循障算法还可适用于对机器人、非凸动态非变形障碍物、非凸静态障碍物以及动态或静态凸障碍物的循障。在理论上分析了系统的稳定性,并给出了成功循障动态变形障碍物的条件和机器人数量扩展时围捕队形的特点。(4)未知动态复杂非凸障碍物环境中群机器人协同多层围捕。将原来的简化虚拟受力模型中机器人个体只能计算单层围捕圆周上的受力扩展至可以计算任意层围捕圆周上的受力,机器人在层与层之间的移动由其内层或同层两最近邻的位置信息来确定,基于以上设计的多层围捕算法实现了复杂环境下群机器人围捕系统的高可扩展性、高可靠性和强避障能力。在理论上分析了多层围捕系统的稳定性。(5)未知动态凸障碍物环境中群机器人协同多目标围捕。将原来的简化虚拟受力模型中机器人个体对单个目标的受力分析扩展至对多目标中任意一个目标的受力分析,每个机器人的围捕目标由动态多目标任务分配算法来确定,该算法只需要面向多目标中心方向180o范围内的两最近邻任务信息来实现即需要的信息量少,算法本身是分布式的并且简单高效。在理论上分析了多目标围捕系统的稳定性,并给出了多目标任务分配时对机器人群体内数量的要求。(6)自行设计了物理围捕试验平台SRf H(Swarm Robots for Hunting,围捕群机器人),验证了所提基本围捕理论与算法的正确性和有效性。采用LINK UWB定位系统,开展了基于简化虚拟受力模型的围捕理论与算法在无障碍物环境下和凸障碍物环境下的围捕试验。不同环境和不同数目的SRf H围捕试验说明了所提出的围捕理论与算法具有较好的鲁棒性、可扩展性以及灵活性。Leader涌现的规律进一步在实践中得到检验,由于通信环境的复杂性,再加上定位系统本身的定位误差导致机器人个体关于两最近邻的受力分析难度进一步加大,除了有时候通过做滤波处理可以进行判断外,其它情况下则需要将Leader的判断条件更改为有一段受力序列是来自机器人个体左边的两最近邻或右边的两最近邻即可,不再需要单调衰减的限制,即实际情况中常出现单调但不一定衰减的情形,这是实践与理想仿真中的不同之处。
[Abstract]:The research of group robot system is a hot spot recently. It is inspired by complex natural systems, such as social insects (ants, bees, etc.) or cooperative animal groups. The global behavior of the group robot system emerges from the local rules implemented on the level of the individual robot. The swarm robot capture system is a group robot system research. A very typical task platform, which has important research value, is conducive to the realization of encirclement, search and rescue, group confrontation, formation maintenance, cooperation handling, target guard and leadership guard, etc., which can be widely used in anti-terrorism, military, security and vigilance. However, the theory of the swarm robot capture system is not complete and involved. The trap environment is relatively simple, the expansibility of the capture system is not strong, and the multi target hunting algorithm is complex. In this paper, the swarm robot hunting system based on the artificial physical method is studied in this paper. The main contents are as follows: (1) the incompleteness of the mobile swarm robot in the unknown dynamic convex obstacle environment is different from the looseness based on looseness. This algorithm is based on the simplified virtual force model. This algorithm only needs two nearest neighbor and target location information. The algorithm is simple and efficient, and the simple number of force functions in the virtual force model is simplified. The bionic intelligent obstacle avoidance function realizes the good obstacle avoidance in the unknown dynamic convex obstacle environment. The stability of the trap system is analyzed in theory. The judgment condition of the emergence of the rule.Leader of Leader is the gradual approximate monotonous attenuation after the emergence of a large value, which is emerging from the emergence of the Leader based on the loose preference rule. The reason is the opposite. The reason is that the force modeling of the two nearest neighbor of the robot's individual perception in the simplified virtual force model is the repulsion, the farther the Leader distance two the nearest neighbor is, the smaller the repulsion, which will make the oscillation of the Leader itself less in the process of the emergence of the Leader. (2) the uncomplete mobile swarm robot in the unknown non convex obstacle environment is trapped. Based on the simplified virtual force model, a non convex static obstacle avoidance algorithm is proposed. The algorithm only needs the location information of the two nearest neighbor and the target. The motion angle between the obstacles and the obstacles is increased in parallel direction to the obstacle, and the obstacle avoidance is guaranteed at the same time. The stability of the system is analyzed, and the conditions for the successful non convex static obstacles are given. (3) the swarm robot capture in the unknown dynamic deformable obstacle environment. Based on the simplified virtual force model, the dynamic deformation obstacle algorithm is designed. The algorithm only needs the location information of the two nearest neighbour and the target. The motion angle between the obstacles and the obstacle takes into account the velocity of the vertical direction of the obstacle and the velocity in the parallel direction to the obstacle. It can keep the barrier and keep the appropriate distance to the dynamic deformable obstacle. The algorithm can also be applied to the robot, non convex dynamic non deformable obstacle, non convex static barrier. The obstacle and the obstacle of dynamic or static convex obstacle. The stability of the system is analyzed in theory, and the conditions of the obstacle dynamic deformable obstacle and the characteristic of the round formation when the number of robots are expanded. (4) the group robot in the unknown dynamic complex non convex obstacle environment is combined with the multi-layer trap. In the model, the robot can only calculate the force on the circumference of a single layer to calculate the force on the circumference of an arbitrary layer. The movement of the robot between the layer and the layer is determined by the location information of the nearest neighbor of the inner layer or the same layer two. High scalability, high reliability and strong obstacle avoidance ability. The stability of multi-layer capture system is analyzed theoretically. (5) group robots in unknown dynamic convex obstacle environment are cooperative multi target hunting. The force analysis of a single target in the original simplified virtual force model is extended to any one of the multiple targets. The target's force analysis is determined by the dynamic multi target assignment algorithm for each robot. The algorithm only needs the two nearest neighbor task information in the 180o range of the multi target center direction to realize the required information. The algorithm itself is distributed and simple and efficient. In theory, the multi target hunting system is analyzed. The stability of the system, and the requirements for the number of robot groups in the assignment of multi-objective tasks. (6) the physical capture test platform SRf H (Swarm Robots for Hunting, the swarm robot) is designed, and the correctness and effectiveness of the proposed basic capture theory and algorithm are verified. The LINK UWB positioning system is adopted and the simplification is carried out. The trap theory of virtual force model and its algorithm under the environment of barrier free environment and convex obstacle environment. The different environment and different number of SRf H capture experiments show that the proposed trap theory and algorithm have good robustness, scalability and flexibility of the emergence of.Leader are further tested in practice. As a result of the complexity of the communication environment, and the positioning error of the positioning system itself, the difficulty of the force analysis of the two nearest neighbor of the robot is further increased, except sometimes by the filtering process. In other cases, the judgment condition of the Leader is changed to a section of the force sequence. The nearest neighbor of the two nearest neighbor or the two nearest neighbor on the left of a robot is no longer required for monotonous attenuation, that is, a case of monotonous but not constant attenuation in the actual situation, which is the difference between practice and ideal simulation.

【学位授予单位】：湖南大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TP242
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本文编号：1849564