考虑障碍环境下的多机器人编队控制研究

编队和避障控制是机器人路径规划设计中的典型问题,文中提出了将leader-following法和人工势场法相结合的方法,来更好地完成多机器人在未知环境下的编队和避障控制。之前的研究只将leader-following算法用于多机器人的编队控制,而文中提出此方法也可以用于多机器人系统的避障控制。基于leader-following法,多机器人能自动编队并保持队形;而结合人工势场法,多机器人可以保持队形行进,在遇到障碍物的情况下变换队形避障,在避障后恢复原队形,最终到达目标。通过仿真实验证明,该算法实现了多机器人在未知环境下的自动编队和避障,从而证明了leader-following算法可以用于机器人的避障控制。     

考虑障碍环境下的多机器人编队控制研究

编队和避障控制是机器人路径规划设计中的典型问题,文中提出了将leader—following法和人工势场法相结合的方法,来更好地完成多机器人在未知环境下的编队和避障控制。之前的研究只将leader—following算法用于多机器人的编队控制,而文中提出此方法也可以用于多机器人系统的避障控制。基于leader—following法,多机器人能自动编队并保持队形;而结合人工势场法,多机器人可以保持队形行进,在遇到障碍物的情况下变换队形避障,在避障后恢复原队形,最终到达目标。通过仿真实验证明,该算法实现了多机器人在未知环境下的自动编队和避障,从而证明了leader—following算法可以用于机器人的避障控制。     

非完整移动机器人的复合编队控制

主要研究了非完整自主机器人之间的队形保持和避障问题,提出了一种新的复合编队控制方法,该方法根据机器人的期望位置在其运动约束区域内外的不同,分别以一种灵活的反馈线性化算法和最优近似目标算法来建立控制规则,并提出了编队环境中存在静态障碍物时的队形控制策略,从而实现多机器人的稳定编队控制．该方法降低了传统线性反馈控制对编队初始误差范围的要求,并且解决了非完整机器人编队的避障问题．实验结果表明了该编队控制方法的可行性和有效性．     

一种新的用于足球机器人的全向视觉系统

全向视觉系统是RoboCup中型组足球机器人最重要的传感器之一。为了实现足球机器人的目标识别与自定位,提示了一种新的足球机器人全向视觉系统的设计与实现,其中在硬件上设计了一种由水平等比镜面和垂直等比镜面组合而成的新型全向反射镜面,其能够采集到较理想的全景图像;软件上则根据该镜面的成像特性实现了一种新颖的基于场地标志线信息的机器人自定位算法,该算法能够获得较准确的机器人自定位值。实验结果表明,该全向视觉系统能够有效地应用于机器人足球赛中。     

多移动机器人的领航-跟随编队避障控制

针对多移动机器人的编队控制问题,提出了一种结合Polar Histogram避障法的领航-跟随协调编队控制算法。该算法在领航-跟随l-φ编队控制结构的基础上引入虚拟跟随机器人,将编队控制转化为跟随机器人对虚拟跟随机器人的轨迹跟踪控制。结合移动机器人自身传感器技术,在简单甚至复杂的环境下为机器人提供相应的路径运动策略,实现实时导航的目的。以两轮差动Qbot移动机器人为研究对象,搭建半实物仿真平台,进行仿真实验。仿真结果表明:该方法可以有效地实现多移动机器人协调编队和避障控制。     

基于全向移动平台的多机器人编队控制研究

针对多机器人编队控制中的队形控制和协同避障问题,提出了基于麦克纳姆轮的全向移动多机器人编队的基于领航-跟随型编队控制算法.首先建立多机器人运动学模型,得到车体运动控制参数,并针对传统领航跟随法进行改进,设计一种虚拟结构领航-跟随法,并将改进的人工势场法引入领航机器人的在线局部路径规划中,通过添加虚拟斥力旋转势场,解决了局部极小值问题,实现了多机器人编队在静态障碍环境中无碰撞路径规划.最后通过Python仿真验证了该算法结合的有效性.     

一种鲁棒的基于全向视觉的足球机器人自定位方法

针对RoboCup 中型组机器人足球比赛环境高度动态的特点和现有定位方法的不足,提出了一种鲁棒 的基于全向视觉的自定位方法．该方法结合使用粒子滤波定位和匹配优化定位,同时还设计了基于图像熵的摄像机 参数自动调节算法以使全向视觉的输出能够适应光线条件的变化．实验结果表明,通过使用该方法,机器人能够在 实时获得高精度自定位的同时实现可靠的全局定位,并对遮挡、光线条件变化等环境动态因素具有很强的鲁棒性．     

通讯受限条件下的机器人编队算法

针对通讯受限条件下大规模移动机器人编队任务, 本文提出了基于行为的分布式多机器人线形编队控制 和避障算法. 机器人个体无需获得群体中所有机器人的信息, 而是根据传感器获取的环境信息和局部范围内的机器 人信息对其自身的调整方向进行预测, 并最终很好地完成了设定的编队及避障任务. 由于本文方法需求的通讯量不 大, 并且采用分布式控制, 因此该方法适用于大规模的机器人集群编队任务. 文中还给出了本系统的稳定性分析, 证 明了系统的稳定性. 实验结果表明该算法使得机器人能够仅通过局部信息形成线形编队, 在遇到障碍物后能够灵活 避开障碍物, 并且在避开障碍物进入安全区域后重新恢复线形编队.     

未知环境下多AUV协同避障方法研究

针对多AUV（autonomous underwater vehicle）系统在未知环境中进行路径规划时难以兼顾避障与编队的问题,提出了一种基于领航—跟随者与行为的多AUV协同避障方法。首先,通过构造碰撞危险度及偏离目标评价函数,设计了AUV局部路径规划方法;在此基础上,结合编队控制方法,分别为领航者和跟随者设计不同的行为以及行为选择模式。半物理仿真实验结果表明,该算法能够实现多AUV系统在未知环境中的协同避障,且队形偏离度与恢复队形时间优于传统多机器人避障算法。实验结果证明了该算法的可行性与有效性。     

移动机器人的队形控制及其主动避障

在这篇论文中, 我们利用一个统一的算法框架来解决移动机器人的队形控制和主动避障问题, 使得编队中的从机器人在避开障碍物的同时, 能够与被跟踪的主机器人保持期望的相对距离或相对方位. 在现有的关于主—从跟踪编队控制的文献中, 为了实现对主机器人快速准确的跟踪, 从机器人在跟踪控制时需要主机器人在惯性坐标系下的绝对运动速度作为队形跟踪控制器的输入. 然而, 在一些环境中, 主机器人的绝对运动状态很难获得. 这里, 我们将利用主—从机器人之间的相对速度来建立机器人编队系统的运动学模型. 基于这个模型的编队控制方法将不再需要测量主机器人的绝对运动速度. 进一步地, 上述的建模和控制方法被扩展为一个移动机器人的动态避障方法, 该方法利用机器人与障碍物之间相对运动状态作为避障控制器的信息输入. 利用由三个非完整移动机器人组成的多机器人系统, 验证了所提出编队控制方法的有效性.     

快速收敛的机器人部队任意队形分布式控制算法

针对多智能体协作完成特定任务时难以在全自主控制的前提下协作形成任意队形和队形向量不易确定的问题 ,通过由各智能体自主简单的确定自己的队形向量 ,从理论上扩展基于队形向量的队形控制原理以生成任意队形 ,改进机器人的运动方式以提高收敛速度 ,提出一种快速收敛的机器人部队任意队形分布式控制算法 .仿真结果表明 ,该算法可以形成任意队形 ,比现有控制算法的收敛速度快 ,队形收敛所需的时间仅为现有算法的 10 %左右     

A membrane computing framework for self-reconfigurable robots

Self-reconfigurable robots are built by modules which can move in relationship to each other, which allows the robot to change its physical form. Finding a sequence of module moves that reconfigures the robot from the initial configuration to the goal configuration is a hard task and many control algorithms have been proposed. In this paper, we present a novel method which combines a cluster-flow locomotion based on cellular automata together with a decentralized local representation of the spatial geometry based on membrane computing ideas. This new approach has been tested with computer simulations and real-world experiments performed with modular self-reconfigurable robots and represents a new point of view with respect other control methods found in the literature.

     

多移动机器人避障编队控制

研究了非完整移动机器人群的避障编队问题. 在次优化控制基础上, 通过对每个交互机器人求解指标函数存在耦合的优化问题提出了两种算法. 在终端惩罚项中加入了势场函数并且构造出相应的终端约束集. 关于系统稳定性及安全性进行了讨论. 仿真实例说明了所提算法的可行性.     

Decentralized behavior-based formation control of multiple robots considering obstacle avoidance

This paper proposes a decentralized behavior-based formation control algorithm for multiple robots considering obstacle avoidance. Using only the information of the relative position of a robot between neighboring robots and obstacles, the proposed algorithm achieves formation control based on a behavior-based algorithm. In addition, the robust formation is achieved by maintaining the distance and angle of each robot toward the leader robot without using information of the leader robot. To avoid the collisions with obstacles, the heading angles of all robots are determined by introducing the concept of an escape angle, which is related with three boundary layers between an obstacle and the robot. The layer on which the robot is located determines the start time of avoidance and escape angle; this, in turn, generates the escape path along which a robot can move toward the safe layer. In this way, the proposed method can significantly simplify the step of the information process. Finally, simulation results are provided to demonstrate the efficiency of the proposed algorithm.     

Kilobot: A low cost robot with scalable operations designed for collective behaviors

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few tens of robots. To address this issue, this paper presents Kilobot, an open-source, low cost robot designed to make testing collective algorithms on hundreds or thousands of robots accessible to robotics researchers. To enable the possibility of large Kilobot collectives where the number of robots is an order of magnitude larger than the largest that exist today, each robot is made with only $14 worth of parts and takes 5 min to assemble. Furthermore, the robot design allows a single user to easily operate a large Kilobot collective, such as programming, powering on, and charging all robots, which would be difficult or impossible to do with many existing robotic systems. We demonstrate the capabilities of the Kilobot as a collective robot, by using a small robot test collective to implement four popular swarm behaviors: foraging, formation control, phototaxis, and synchronization.     

A collaborative localization tolerant to recognition error by double-check particle exchange

Statistical algorithms using particle filters for collaborative multi-robot localization have been proposed. In these algorithms, by synchronizing every robot’s belief or exchanging particles of the robots with each other, fast and accurate localization is attained. These algorithms assume correct recognition of other robots, and the effects of recognition errors are not discussed. However, if the recognition of other robots is incorrect, a large amount of error in localization can occur. This article describes this problem. Furthermore, an algorithm for collaborative multi-robot localization is proposed in order to cope with this problem. In the proposed algorithm, the particles of a robot are sent to other robots according to measurement results obtained by the sending robot. At the same time, some particles remain in the sending robot. Particles received from other robots are evaluated using measurement results obtained by the receiving robot. The proposed method is tolerant to recognition error by the remaining particles and evaluating the exchanged particles in the sending and receiving robots twice, and if there is no recognition error, the proposed method increases the accuracy of the estimation by these two evaluations. These properties of the proposed method are argued mathematically. Simulation results show that incorrect recognition of other robots does not cause serious problems in the proposed method.     

Scalable formation control in stealth with limited sensing range

In this paper, a protocol and a control law are designed for a single robot so that a team of such robots can interact and cooperate to reach the displacements from an eligible reference formation. Each robot is equipped with displacement sensors of limited sensing ranges. Communication channels are assumed to be unavailable to the team, and each robot works in stealth mode. The team is scalable such that new robots can be recruited, and existing robots can be dismissed. In order for the team size to be scalable, the extended formation based on relative displacement is established as the reference formation. Thus, using the extended formation as a reference, the control law and the protocol could be flexible. As potential conflicts deflect the robot team from the desired formation, the control law is designed to expose the conflicts to the involved neighboring robots such that the protocol can resolve them. A numerical example is given to illustrate how an extended formation is designed, and a simulation example is conducted to demonstrate the performance and merits of the proposed techniques. Copyright © 2016 John Wiley & Sons, Ltd.     

仅考虑位置信息连通性保持的群体机器人集结控制

研究速度信息未知情形下的群体机器人集结控制问题.分别针对未设置虚拟领导者和设置虚拟领导者两种情形,提出了仅考虑位置信息的观测器和具有网络连通性保持的分布式控制方法.理论分析表明该算法具有网络连通性保持功能,并且保证所有机器人的速度信息收敛到一致,机器人最终也会聚集到一起.数值仿真实验验证了所提出分布式控制算法的有效性.     

Motion control algorithms and simulation of the dynamics of multilink robots moving based on the principles of a traveling wave

Multilink robots that simulate the motion of snakes and worms are considered. The motion is a result of the interaction with a supporting surface when the configuration of the robot is changed. Supervisor control algorithms are proposed for which a user (an operator) controls the motion velocity and direction, while the robot configuration that implements the desired motion is changed automatically. Controlled motions of the considered robots were simulated based on their dynamic models. The motions that simulated movements of snakes and worms are compared. Robots of this type can be used as mobile robots.     

双参考点切换的多移动机器人队形运动控制方法

为描述机器人队列的运动过程,从相对位姿的角度定义了多移动机器人的队形模型.在传统leader-following队形控制的基础上,引入切换控制思想,每对领路机器人与跟随机器人之间设计3个控制器,对应跟随机器人中轴线上两参考点分别设计两个运动子控制器,控制领路机器人与跟随机器人之间的相对位姿;切换控制器根据系统处于平衡状态时,跟随机器人线速度的符号切换运动控制器,从而保证队列收敛到目标队形.仿真实验结果表明,机器人队列表现出良好的整体一致性,队列运动更加平稳.