A self-adaptive communication strategy for flocking in stationary and non-stationary environments

We propose a self-adaptive communication strategy for controlling the heading direction of a swarm of mobile robots during flocking. We consider the problem where a small group of informed robots has to guide a large swarm along a desired direction. We consider three versions of this problem: one where the desired direction is fixed; one where the desired direction changes over time; one where a second group of informed robots has information about a second desired direction that conflicts with the first one, but has higher priority. The goal of the swarm is to follow, at all times, the desired direction that has the highest priority and, at the same time, to keep cohesion. The proposed strategy allows the informed robots to guide the swarm when only one desired direction is present. Additionally, a self-adaptation mechanism allows the robots to indirectly sense the second desired direction, and makes the swarm follow it. In experiments with both simulated and real robots, we evaluate how well the swarm tracks the desired direction and how well it maintains cohesion. We show that, using self-adaptive communication, the swarm is able to follow the desired direction with the highest priority at all times without splitting.     

Robust Adaptive Control of Robots Using Neural Network: Global Stability

A desired compensation adaptive law‐based neural network (DCAL‐NN) controller is proposed for the robust position control of rigid‐link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee the global asymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on‐line, with no offline learning phase required. When compared with standard adaptive robot controllers, we do not require linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of rigid robots without any modifications. A comparative simulation study with different robust and adaptive controllers is included.     

Formation Control of Swarm Robots with Multiple Proximity Distance Sensors

This study deals with the formation control problem of swarm robots using position sensitive detector (PSD) proximity distance sensors based on light-emitting diodes (LEDs). These proximity distance sensors are lightweight and quickly responsive, and are expected to enhance the mobility and flexibility of swarm robots. However, as each sensor has a narrow detection angle, the formation control problem becomes more difficult than when wide-directional distance sensors (such as cameras and laser rangefinders) are used. To overcome this difficulty, we design a two-part motion controller that controls both position and attitude. The attitude controller is necessary for continuous detection of other robots through the narrow detection angles. The designed controller is distributed in the sense that it requires only information on measured values of each robot’s own sensors. Next, we derive an appropriate sensor arrangement (positions and detection angles) that achieves the desired formation pattern. Finally, the effectiveness of the proposed method is demonstrated in an experiment performed by six omni-wheeled robots equipped with LED-based PSD proximity distance sensors.     

一种基于个体评估能力的Stigmergic群体搭建机制

群体搭建任务研究一群机器人如何以分散控制的方式并行搭建出具有几何形状的砖块结构.相对于群机器人学中的其他任务,群体搭建研究特别注重搭建过程的时空协调.作为一种以环境为媒介进行间接交互的时空协调机制,Stigmergy被认为更具一般性与通用性.传统方法通过同时感知多邻域信息或利用结构对称性来制定复杂规则,但其并不十分适用于非模块结构以及序列结构.本文针对此类结构提出了基于个体评估能力的Stigmergic群体搭建时空协调机制,该协调机制可通过赋予个体感知环境、评估系统当前状态的能力来简化对搭建规则的依赖,即个体利用简单搭建规则即可完成群体搭建任务,实现群体搭建过程时空的协调.仿真实验结果表明提出的机制是可行且有效的.     

Neural adaptive PID formation control of car-like mobile robots without velocity measurements

A virtual leader–follower formation control of a group of car-like mobile robots is addressed in this paper. First, the kinematic and dynamic models of car-like robots are transformed into a second-order leader–follower formation model which inherits all structural properties of the robot dynamic model. Then, a new observer-based proportional–integral-derivative formation controller is proposed to force that all robots construct a desired formation with respect to a predefined virtual leader. To improve the formation tracking and observation performance, the integral action is incorporated into the design of the observer–controller scheme. Adaptive robust and neural network techniques are also employed to compensate uncertain parameters, unmodeled dynamics, and external disturbances. Lyapunov’s direct method is utilized to show that the formation tracking and observation errors are semi-globally uniformly ultimately bounded. Then, the proposed controller is extended to the leader–follower formation of a team of tractor–trailer systems. Finally, simulation results illustrate the efficiency of the proposed controller.     

Multi-robot nonlinear model predictive formation control: Moving target and target absence

This paper describes a novel approach in formation control for mobile robots in the active target tracking problem. A nonlinear model predictive formation controller (NMPFC) for target perception was implemented to converge a group of mobile robots toward a desired target. The team must also maintain a desired formation following a target while it is moving, or follow a leader in the case of target’s absence. The structure details of the controller, as well as a mathematical analysis of the formation model used, are presented. Furthermore, results of simulations and experiments with real robots are presented and discussed.     

含驱动器动力学的移动机器人编队自适应控制

针对含有驱动器及编队动力学的多非完整移动机器人编队控制问题,基于领航者-跟随者[l-ψ]控制结构,通过反步法设计了一种将运动学控制器与驱动器输入电压控制器相结合的新型控制策略。采用径向基神经网络（RBFNN）对跟随者及领航者动力学非线性不确定部分进行在线估计,并通过自适应鲁棒控制器对神经网络建模误差进行补偿。该方法不但解决了移动机器人编队控制的参数与非参数不确定性问题,同时也确保了机器人编队在期望队形下对指定轨迹的跟踪;基于Lyapunov方法的设计过程,保证了控制系统的稳定与收敛;仿真结果表明了该方法的有效性。     

Synchronization Control for a Swarm of Unicycle Robots: Analysis of Different Controller Topologies

This paper proposes a nonlinear synchronization controller for a swarm of unicycle robots performing a cooperative task, i.e., following a desired trajectory per robot while maintaining a prescribed formation. The effect of communication between robots is analyzed and several network topologies are investigated, e.g., all‐to‐all, ring type, undirected, among others. The stability analysis of the closed loop system is provided using the Lyapunov method. Experiments with four unicycle robots are presented to validate the control law and communication analysis. Accumulated errors over the experiment time are presented in order to determine which topology is most efficient.     

轮式移动机器人预见预测运动控制

针对移动机器人的运动控制问题,该文采用预见预测控制方法加以解决。利用三阶Bezier曲线作为路径生成器生成目标轨迹,并据此设计了最优预见控制器作为系统的前馈补偿;使用扩展卡尔曼滤波器作为预测模型,基于广义预测控制(GPC)实现了PPC运动控制器的设计。仿真实验结果证明了该方法的有效性。     

Cooperative adaptive consensus tracking for multiple nonholonomic mobile robots

This paper addresses the cooperative adaptive consensus tracking for a group of multiple nonholonomic mobile robots, where the nonholonomic robot model is assumed to be a canonical vehicle having two actuated wheels and one passive wheel. By integrating a kinematic controller and a torque controller for the nonholonomic robotic system, a cooperative adaptive consensus tracking strategy is developed for the uncertain dynamic models using Lyapunov-like analysis in combination with backstepping approach and sliding mode technique. A key feature of the developed adaptive consensus tracking algorithm is the introduction of a directed network topology into the control constraints based on algebraic graph theory to characterise the communication interaction among robots, which plays an important role in realising the cooperative consensus tracking with respect to a specific common reference trajectory. Furthermore, a novel framework is proposed for developing a unified methodology for the convergence analysis of the closed-loop control systems, which can fully ensure the desired adaptive consensus tracking for multiple nonholonomic mobile robots. Subsequently, illustrative examples and numerical simulations are provided to demonstrate and visualise the theoretical results.     

未知动态环境下非完整移动群机器人围捕

针对未知动态障碍物环境下非完整移动群机器人围捕,提出了一种基于简化虚拟受力模型的自组织方法.首先给出了个体机器人的运动方程,然后给出了未知动态环境下目标和动态障碍物的运动模型.通过对复杂环境下围捕行为的分解,抽象出简化虚拟受力模型,基于此受力模型,设计了个体运动控制方法,接着证明了系统的稳定性并给出了参数设置范围.不同情况下的仿真结果表明,本文给出的围捕方法可以使群机器人在未知动态障碍物环境下保持较好的围捕队形,并具有良好的避障性能和灵活性.最后分析了本文与基于松散偏好规则的围捕方法相比的优势.     

非完整移动机器人编队的滑模控制

提出了一个非线性滑模控制器,协调一组非完整移动机器人以取得合乎要求的编队．考虑了两个机器人组成的领航者—跟随者机器人模型,通过滑模控制使它们沿预定的轨迹运动并保持预定的相对距离、方位角及运动方向．运用传统的李亚普诺夫理论研究了闭环系统的稳定性．在合理的假设下,从理论上证明了存在有界干扰情形下机器人编队的渐近稳定性,即所设计的滑模控制器使得相对距离误差、方位角误差及运动方向误差渐近稳定．最后,给出了两台机器人情形的数值仿真例子来验证该方法的有效性．     

Visual servoing tracking control of uncalibrated manipulators with a moving feature point

The paper is concerned with the problem of uncalibrated visual servoing robots tracking a dynamic feature point along with the desired trajectory. A nonlinear observer and a nonlinear controller are proposed, which allow the considered uncalibrated visual servoing robotic system to fulfil the desired tracking task. Based on this novel control method, a dynamic feature point with unknown motion parameters can be tracked effectively along with the desired trajectory, even with multiple uncertainties existing in the camera, the kinematics and the manipulator dynamics. By the Lyapunov theory, asymptotic convergence of the image errors to zero with the proposed control scheme is rigorously proven. Simulations have been conducted to verify the performance of the proposed control scheme. The results demonstrated good convergence of the image errors.     

Stigmergic cooperation of nanoparticles for swarm fuzzy control of low-density lipoprotein concentration in the arterial wall

This paper proposes the use of stigmergic cooperation between two swarms of Fuzzy Nanoparticles (FNPs) and Auxiliary Nanoparticles (ANPs) for intelligent control of Low-Density Lipoprotein (LDL) concentration in the arterial wall, as a novel non-invasive method for prevention of atherosclerosis. Given any desired fuzzy controller, a swarm of FNPs in the aqueous environment of a living tissue can collectively realize an accurate approximation of this controller, which is called swarm fuzzy controller. In this study, the task of the swarm fuzzy controller is to manipulate the pheromone level of the environment as output according to the sensed value of LDL concentration as input. Pheromone is a chemical substance that is used for stigmergic communication between two swarms of FNPs and ANPs. An ANP consists of a drug reservoir connected to a nanoscale valve which is controllable by pheromone concentration. The level of pheromone in the local environment of an ANP determines how much drug should be released by it. The hardware complexity of the proposed approach is lower than nanorobotics to facilitate its manufacturing. Simulation results on a well-known mathematical model demonstrate that this method can successfully reduce the LDL level to a desired value in the arterial wall of a patient with very high LDL level, while its performance is much better in contrast to the previous work of authors. Also, the mass of the released drug in a healthy wall is 16 times lesser than its corresponding value in an unhealthy wall.     

Artificial moment method for swarm robot formation control

The purpose of this paper is to develop a general control method for swarm robot formation control. Firstly, an attraction-segment leader-follower formation graph is presented for formation representations. The model of swarm robot systems is described. According to the results and two kinds of artificial moments defined as leader-attraction moment and follower-attraction moment, a novel artificial moment method is proposed for swarm robot formation control. The principle of the method is introduced and the motion controller of robots is designed. Finally, the stability of the formation control system is proved. The simulations show that both the formation representation graph and the formation control method are valid and feasible.     

Practical consensus tracking control of multiple nonholonomic wheeled mobile robots in polar coordinates

This paper proposes a sliding‐mode control (SMC) method to achieve practical cooperative consensus tracking for a network of multiple nonholonomic wheeled mobile robots (MNWMRs) with input disturbances. A novel SMC surface under the nonholonomic constraints is first formulated to characterize the network communication interactions among the networked robots under the framework of polar coordinates. A unified distributed consensus tracking strategy is then proposed by systematically combining a position controller and a direction controller. Furthermore, a simple yet general criterion is derived to achieve the desired practical consensus of trajectory tracking and posture stabilization for MNWMRs. In particular, for a specific common consensus trajectory, the complete asymptotic tracking in heading direction can be fully guaranteed when the perfect asymptotic position‐tracking errors are realized. Accordingly, the developed consensus tracking strategy for MNWMRs demonstrates some advantages of control performance including stability, robustness, and effectiveness over the existing control method proposed for their single‐robot counterparts. Some comparative simulation results are given to confirm the effectiveness of the proposed cooperative consensus control method.     

一种多非完整移动机器人分布式编队控制方法

本文研究了多非完整移动机器人编队控制算法。在该算法中,参考轨迹被视为虚拟领导者,只有部分机器人可以接收到领导者信息,机器人之间只能进行局部信息交互。利用坐标变换将机器人系统的编队问题转化为变换后系统的一致性问题,在持续激励的条件下,设计了一种分布式控制算法,通过图论与Lyapunov 理论证明了该分布式控制算法可以使移动机器人队伍指数收敛于期望队形,并使队形的几何中心指数收敛到参考轨迹。最后,数值仿真验证了该控制算法的有效性。     

Finite‐time formation control of multiple nonholonomic mobile robots

This paper considers finite‐time formation control problem for a group of nonholonomic mobile robots. The desired formation trajectory is represented by a virtual dynamic leader whose states are available to only a subset of the followers and the followers have only local interaction. First of all, a continuous distributed finite‐time observer is proposed for each follower to estimate the leader's states in a finite time. Then, a continuous distributed cooperative finite‐time tracking control law is designed for each mobile robot. Rigorous proof shows that the group of mobile robots converge to the desired geometric formation pattern in finite time. At the same time, all the robots can track the desired formation trajectory in finite time. Simulation example illustrates the effectiveness of our method. Copyright © 2012 John Wiley & Sons, Ltd.     

Impedance Control with On-Line Neural Network Compensator for Dual-Arm Robots

A controller design strategy of dual-arm robots is proposed in this paper. The controller consists of a central controller and three force controllers. The central controller is used to calculate each arms force command according to the desired object motion. A force controller is used in each arm to track the commanding force. Another force controller is used to track the desired contact force between the manipulated object and its environment. The force controller can be partitioned into three parts. The computed torque method is used to linearize and decouple the dynamics of a manipulator. An impedance controller is then added to regulate the mechanical impedance between the manipulator and its environment. In order to track a reference force signal, an on-line neural network is used to compensate the effect of unknown parameters of the manipulator and environment. The simulation results are reported to show the performance of the neural network compensator.     

A bounded controller for multirobot navigation while maintaining network connectivity in the presence of obstacles

Maintaining the connectivity of networked robots is a challenge in multirobot applications. In this paper, this challenging problem is addressed through the development of a novel controller that can guarantee that robots will approach their individual desired positions while maintaining existing network topology and avoiding obstacles. A new concept of connectivity constraint, along with a continuous modeling approach to obstacle avoidance, is utilized in building the navigation function. The designed potential field integrates the navigation requirement, connectivity constraint, and obstacle avoidance simultaneously, based on which a bounded control input is generated for multirobot control. It is shown that if the initial configurations of the robots are connected and the desired configuration is reachable, the proposed controller is capable of driving multirobots to their individual goal positions conditionally while keeping the underlying network connected. Simulations and experiments are finally performed using a group of mobile robots to demonstrate the effectiveness of the proposed controller.