Cooperative formation control of autonomous underwater vehicles: An overview

Formation control is a cooperative control concept in which multiple autonomous underwater mobile robots are deployed for a group motion and/or control mission. This paper presents a brief review on various cooperative search and formation control strategies for multiple autonomous underwater vehicles (AUV) based on literature reported till date. Various cooperative and formation control schemes for collecting huge amount of data based on formation regulation control and formation tracking control are discussed. To address the challenge of detecting AUV failure in the fleet, communication issues, collision and obstacle avoidance are also taken into attention. Stability analysis of the feasible formation is also presented. This paper may be intended to serve as a convenient reference for the further research on formation control of multiple underwater mobile robots.     

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。     

感知范围受限的群机器人自主围捕算法

针对在有障碍物场地中感知范围受限的群机器人协同围捕问题,本文首先给出了机器人个体、障碍物、目标的模型,并用数学形式对围捕任务进行描述,在此基础上提出了机器人个体基于简化虚拟速度和基于航向避障的自主围捕控制律.基于简化虚拟速度模型的控制律使得机器人能自主地围捕目标同时保持与同伴的距离避免互撞;基于航向的避障方法提升了个体的避障效率,避免斥力避障方法导致的死锁问题.其次本文证明了在该控制律下系统的稳定性.仿真结果表明,该算法在有效围捕目标的同时能够高效地避开障碍物,具有对复杂环境的适应性.最后本文分析了与其他方法相比该算法的优点.     

基于观测优化的双机协同控制与避障

为实现不确定环境下无人机对远程超视距目标的精确指示,考虑空中移动和静止障碍物,提出基于观测优化的双机协同控制与避障算法,从而增加测量信息,减小目标状态估计的不确定度.以费舍尔信息矩阵(FIM)表征所获取的目标信息,理论推导出三维空间中双机最优观测的指标函数,并设计无人机协同控制律,得到优化的无人机观测航迹,增强无人机协...     

Least squares tracking on the Euclidean group

A large class of problems in robotics, e.g., trajectory tracking with obstacle avoidance, compliant motion control, and complex assembly, can be formulated as a least-squares tracking problem on the Euclidean group subject to constraints on the state and/or control. We develop an optimal control framework for this general class of problems, and derive analytic solutions for the local and global versions of the general optimal control problem. Our formalism can be viewed in some sense as an extension to the Euclidean group of the linear quadratic regulator subject to state equality constraints. Examples from force-guided complex assembly and tracking with obstacle avoidance are given     

Multi-agent consensus algorithm with obstacle avoidance via optimal control approach

Multi-agent consensus problem in an obstacle-laden environment is addressed in this study. A novel optimal control approach is proposed for the multi-agent system to reach consensus as well as avoid obstacles with a reasonable control effort. An innovative nonquadratic penalty function is constructed to achieve obstacle avoidance capability from an inverse optimal control perspective. The asymptotic stability and optimality of the consensus algorithm are proven. In addition, the optimal control law only requires local information from the communication topology to guarantee the proposed behaviour, rather than all agents’ information. The consensus and obstacle avoidance are validated through various simulations.     

基于凸面体圆弧航路的无人机自主避障算法

为解决无人机飞行过程中障碍物规避问题,提出一种新的三维自主避障算法.首先,根据障碍物的若干信息利用标准凸面体对不规则障碍物进行数学建模,用一个或多个标准凸面体覆盖障碍物整体或关键部分;然后,根据障碍物模型设计圆弧规避航路算法,将避障问题转化为跟踪规避航路控制问题,并定义避障判定、避障方向判断和成功避障规则;最后,结合非线性制导律和高度通道控制,实现无人机实时三维航路跟踪与自主避障.非线性数值仿真结果表明,避障算法能够有效地规避障碍物且三维航路跟踪精度好,能够应用于无人机的避障飞行任务.     

群机器人任务分工与协同搜索方法

群机器人执行搜索任务时,为平衡任务分工和提高协同搜索效率,提出预分工与反馈调节的双层分工方法和模拟鸟群觅食的协同搜索方法。在任务分工方面,以探测的目标强度为依据建立目标选择概率函数,使用轮盘赌确定机器人意向目标,将意向目标相同的机器人预分配为同一子群,使用反馈调节法实现分工平衡。在协调搜索方面,模拟鸟群觅食过程,提出粒子群-人工势场的搜索和避障协同控制方法。仿真结果表明,协同搜索方法能够锁定区域内所有目标,与文献[10]方法相比,任务完成平均耗时减少了17.14%,最优耗时减少了18.61%,验证了该方法在任务分工和目标搜索上的优越性。     

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．     

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。     

Direct visual tracking control of remote cellular robots

This paper presents the design of a stable non-linear control system for the remote visual tracking of cellular robots. The robots are controlled through visual feedback based on the processing of the image captured by a fixed video camera observing the workspace. The control algorithm is based only on measurements on the image plane of the visual camera–direct visual control–thus avoiding the problems related to camera calibration. In addition, the camera plane may have any (unknown) orientation with respect to the robot workspace. The controller uses an on-line estimation of the image Jacobians. Considering the Jacobians’ estimation errors, the control system is capable of tracking a reference point moving on the image plane–defining the reference trajectory–with an ultimately bounded error. An obstacle avoidance strategy is also developed in the same context, based on the visual impedance concept. Experimental results show the performance of the overall control system.     

Formation control of mobile robots with obstacle avoidance based on GOACM using onboard sensors

This paper deals with the problem of formation control for nonholonomic mobile robots under a cluttered environment. When the obstacles are not detected, the follower robot calculates its waypoint to track, based on the leader robot’s state. The proposed geometric obstacle avoidance control method (GOACM) guarantees that the robot avoids the static and dynamic obstacles using onboard sensors. Due to the difficulty for the robot to simultaneously get overall safe boundary of an obstacle in practice, a safe line, which is perpendicular to the obstacle surface, is used instead of the safe boundary. Since GOACM is executed to find a safe waypoint for the robot, GOACM can effectively cooperate with the formation control method. Moreover, the adaptive controllers guarantee that the trajectory and velocity tracking errors converge to zero with the consideration of the parametric uncertainties of both kinematic and dynamic models. Simulation and experiment results present that the robots effectively form and maintain formation avoiding the obstacles.     

基于虚拟力引导的人机协同目标抓取方法

针对力反馈遥操作中传统人工势场法无法适应于机械臂整体的避障以及在作业过程中操作者难以控制机械臂到达所需位姿的问题,提出了一种基于虚拟力引导的人机协同目标抓取方法。力反馈设备向操作者提供力觉交互。通过结合人工势场法和虚拟夹具,构建管道形虚拟力场,生成实时虚拟力引导,实现协助操作者完成从端机器人的整体避障任务并在完成避障后引导机器人返回预定义路径并趋近目标点。当进行抓取任务时,构建锥形虚拟力场,实现协助操作者操作机械臂到达目标位置和姿态。此外,提出了一种机器人运动限制方法以降低操作者的操作失误对抓取任务的影响。实验证明,该方法能有效提高目标抓取操作的成功率和操作效率。     

小车远程控制及自主寻路系统的设计与实现

智能机器人可通过传感器获取周围的地形、温度等信息,并加以分析处理而采取对应策略。然而,机器人可能由于本身不具备定位功能而只能进行简单控制,也可能因为障碍物信息采样不足而导致避障算法失败。面对这一类问题,设计并实现了一套能对智能车进行远程控制并令其自主寻路的系统。该系统通过高效的图像处理与识别对智能车自动定位;对现有避障算法进行了改进,并建立了避让策略来保证智能车总能找到一条抵达目标的安全路径。实验结果表明,该系统定位精度较高,控制与寻路功能具有一定的可靠性与实用性。     

基于流函数的多移动机器人Swarming控制模型

利用流函数解决单个移动机器人的避障问题，并提出了基于流函数和单一连接规则的、采用虚拟leader和二叉树结构的多移动机器人swarming控制模型．基于单一连接规则的二叉树结构使得整个机器人群的控制更简单，更灵活；而虚拟leader的引入，使机器人群在避障过程中不会发生分离，并能够很好地解决类似于机器人掉队的问题，提高了系统的稳定性，增强了系统的应变能力．仿真结果验证了该模型的有效性．     

Obstacle avoidance for redundant robots using configuration control

The article presents a new and simple solution to the obstacle avoidance problem for redundant robots. In the proposed approach, called configuration control, the redundancy is utilized to configure the robot so as to satisfy a set of kinematic inequality constraints representing obstacle avoidance, while the end-effector is tracking a desired trajectory. The robot control scheme is very simple, and uses on-line adaptation to eliminate the need for the complex dynamic model and parameter values of the robot. Several simulation results for a four-link planar robot are presented to illustrate the versatility of the approach. These include reaching around a stationary obstacle, simultaneous avoidance of two obstacles, robot reconfiguration to avoid a moving obstacle, and avoidance of rectangular obstacles. The simplicity and computational efficiency of the proposed scheme allows on-line implementation with a high sampling rate for real-time obstacle avoidance in a dynamically varying environment.     

Temporal logic motion planning for dynamic robots

In this paper, we address the temporal logic motion planning problem for mobile robots that are modeled by second order dynamics. Temporal logic specifications can capture the usual control specifications such as reachability and invariance as well as more complex specifications like sequencing and obstacle avoidance. Our approach consists of three basic steps. First, we design a control law that enables the dynamic model to track a simpler kinematic model with a globally bounded error. Second, we built a robust temporal logic specification that takes into account the tracking errors of the first step. Finally, we solve the new robust temporal logic path planning problem for the kinematic model using automata theory and simple local vector fields. The resulting continuous time trajectory is provably guaranteed to satisfy the initial user specification.     

Navigation of multiple mobile robots in a highly clutter terrains using adaptive neuro-fuzzy inference system

In recent years, the interest in research on robots has increased extensively; mainly due to avoid human to involve in hazardous task, automation of Industries, Defence, Medical and other household applications. Different kinds of robots and different techniques are used for different applications. In the current research proposes the Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for navigation of single as well as multiple mobile robots in highly cluttered environment. In this research it has tried to design a control system which will be able decide its own path in all environmental conditions to reach the target efficiently. Some other requirement for the mobile robot is to perform behaviours like obstacle avoidance, target seeking, speed controlling, knowing the map of the unknown environments, sensing different objects and sensor-based navigation in robot’s environment.     

双轮移动机器人安全目标追踪与自动避障算法

设计了双轮移动机器人安全目标追踪算法和双回路的追踪与避障控制方案.内层控制回路是目标追踪的控制律,用来指导机器人追踪到指定目标并保持一定的安全距离,而且兼顾了机器人在运行速度上的限制和追踪的时间效率,其控制的渐近稳定性用Lyapunov函数法进行了证明.当遇到障碍物时,外层控制回路根据超声传感器的信息和阻抗控制的概念产生阻抗虚拟力,将期望目标调整到虚拟位置,使机器人能够自动转向以避开障碍物.仿真研究和实验结果证明了追踪算法的有效性和避障方法的可行性.     

基于速度空间的移动机器人同时避障和轨迹跟踪方法

针对有障碍物环境下非完整轮式 移动机器人的轨迹跟踪问题,提出一种基于速度空间的同时避障和轨迹跟踪方法(VSTTM).首先,根据机器人 的动力学特性构建速度空间,得到由速度元组构成的控制集;然后,构造目标函数并对各控制量进行 评价,其中跟踪误差评价函数评估跟踪效果,碰撞检测函数检测是否发生碰撞,终端状态惩罚项保证 算法的稳定性;最后,通过优化过程找到最优的无碰控制量.仿真结果表明了所提出方法的有效性.