Efficient spectral ultraspherical-dual-Petrov–Galerkin algorithms for the direct solution of (2n + 1)th-order linear differential equations

Some efficient and accurate algorithms based on ultraspherical-dual-Petrov–Galerkin method are developed and implemented for solving (2n + 1)th-order linear elliptic differential equations in one variable subject to homogeneous and nonhomogeneous boundary conditions using a spectral discretization. The key idea to the efficiency of our algorithms is to use trial functions satisfying the underlying boundary conditions of the differential equations and the test functions satisfying the dual boundary conditions. The method leads to linear systems with specially structured matrices that can be efficiently inverted. Numerical results are presented to demonstrate the efficiency of our proposed algorithms.     

Distributed orientation agreement in a group of robots

In this article, a method for the agreement of a set of robots on a common reference orientation based on a distributed consensus algorithm is described. It only needs that robots detect the relative positions of their neighbors and communicate with them. Two different consensus algorithms based on the exchange of information are proposed, tested and analyzed. Systematic experiments were carried out in simulation and with real robots in order to test the method. Experimental results show that the robots are able to agree on the reference orientation under certain conditions. Scalability with an increasing number of robots was tested successfully in simulation with up to 49 robots. Experiments with real robots succeeded proving that the proposed method works in reality.     

Deployment of wireless mesh network using RSSI-based swarm robots

This paper proposes a novel method for deploying a wireless mesh network (WMN) using a group of swarm robots equipped with wireless transceivers. The proposed method uses the rough relative positions of the robots estimated by their Radio Signal Strength Indicators (RSSIs) to deploy the WMN. The employed algorithm consists of three parts, namely, (1) a fully distributed and dynamic role decision method among the robots, (2) an adaptive direction control using the time difference of the RSSIs, and (3) a narrow corridor for the robots to pass by movement function along walls. In our study, we evaluated the performances of the proposed deployment method and a conventional method in a real environment using 12 real robots for simple deployment, and 10 real robots for passing the narrow corridor. The results of the performed experiments showed that (1) the proposed method outperformed the conventional method with regard to the deployment time, power consumption, and the distances traveled by the robots, and (2) the movement function along the walls is effective while passing a narrow corridor unlike any other function.     

Bounded controllers for global path tracking control of unicycle-type mobile robots

This paper presents a design of bounded controllers with a predetermined bound for global path tracking control of unicycle-type mobile robots at the torque level. A new one-step ahead backstepping method is first introduced. The heading angle and linear velocity of the robots are then considered as immediate controls to force the position of the robots to globally and asymptotically track its reference path. These immediate controls are designed based on the one-step ahead backstepping method to yield bounded control laws. Next, the one-step ahead backstepping method is applied again to design bounded control torques of the robots with a pre-specified bound. The proposed control design ensures global asymptotical and local exponential convergence of the position and orientation tracking errors to zero, and bounded torques driving the robots. Experimental results on a Khepera mobile robot verify the proposed control controller.     

Control of leader–follower formation and path planning of mobile robots using Asexual Reproduction Optimization (ARO)

This paper presents the optimal path of nonholonomic multi robots with coherent formation in a leader–follower structure in the presence of obstacles using Asexual Reproduction Optimization (ARO). The robots path planning based on potential field method are accomplished and a novel formation controller for mobile robots based on potential field method is proposed. The efficiency of the proposed method is verified through simulation and experimental studies by applying them to control the formation of four e-Pucks robots (low-cost mobile robot platform). Also the proposed method is compared with Simulated Annealing, Improved Harmony Search and Cuckoo Optimization Algorithm methods and the experimental results, higher performance and fast convergence time to the best solution of the ARO demonstrated that this optimization method is appropriate for real time control application.     

Deployment of mobile robots with energy and timing constraints

Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, and exploration. Many studies have been devoted to the control, sensing, and communication of robots. However, the deployment of robots has not been fully addressed. The deployment problem is to determine the number of groups unloaded by a carrier, the number of robots in each group, and the initial locations of those robots. This paper investigates robot deployment for coverage tasks. Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines. We build power models for mobile robots and calculate the robots' power consumption at different speeds. A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints. Our method uses rectangle scanlines as the coverage routes, and solves the deployment problem using fewer robots. Finally, we provide an approach to consider areas with random obstacles. Compared with two simple heuristics, our solution uses 36% fewer robots for open areas and 32% fewer robots for areas with obstacles.     

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。     

Robot Navigation Based on Discrimination of Artificial Fields: Application to Single Robots

The paper introduces a method for local navigation of mobile robots based on the discrimination of multiple artificial fields, which correspond to targets, obstacles, robots and, if this is the case, robot collectives. Instead of just adding up all potentials, the robot discerns the pertinent potentials at its location and applies a set of motion decisions at each moment. Satisfactory results are obtained. This is the first paper of a more extensive work dealing with individual robots, unorganized groups of robot and robot formations. Here, the method is introduced, with examples for a single robot and for several independent robots.     

Region-based shape control for a swarm of robots

This paper presents a region-based shape controller for a swarm of robots. In this control method, the robots move as a group inside a desired region while maintaining a minimum distance among themselves. Various shapes of the desired region can be formed by choosing the appropriate objective functions. The robots in the group only need to communicate with their neighbors and not the entire community. The robots do not have specific identities or roles within the group. Therefore, the proposed method does not require specific orders or positions of the robots inside the region and yet different formations can be formed for a swarm of robots. A Lyapunov-like function is presented for convergence analysis of the multi-robot systems. Simulation results illustrate the performance of the proposed controller.     

Distributed algorithms for formation of geometric patterns with many mobile robots

We discuss a method for controlling a group of mobile robots in a distributed manner. The method is distributed in the sense that all robots, or most of the robots in some cases, plan their motion individually based upon the given goal of the group and the observed positions of other robots. We illustrate the method by showing how a large number of robots can form an approximation of a circle, a simple polygon, or a line segment in the plane. We also show how the robots can distribute themselves nearly uniformly within a circle or a convex polygon in the plane. Finally, we show how the robots can be divided into two or more groups. It turns out that in many cases most robots execute an identical, simple algorithm. The performance of the method is demonstrated by simulation. © 1996 John Wiley & Sons, Inc.     

DNA机器人在AFM图像中的分割和识别

DNA机器人是一种由DNA大分子通过折纸技术制作的纳米级别的机器人,可以用于癌症的诊断和治疗。为了对DNA机器人形态进行研究,研究人员利用原子力显微镜（Atomic Force Microscope,AFM）拍摄出机器人的AFM图像。针对AFM图像多噪声及被观察的DNA机器人大规模重叠的特点,从除噪、分割、识别三个过程展开,提出了一种基于多尺度改进的分水岭AFM图像切割算法,以傅里叶描述子、曲率尺度空间描述符和Hu矩作为特征,实现DNA纳米机器人的分类和识别。     

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.     

Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.     

Pareto-optimal coordination of multiple robots with safety guarantees

This paper investigates the coordination of multiple robots with pre-specified paths, considering motion safety and minimizing the traveling time. A method to estimate possible collision point along the local paths of the robots is proposed. The repulsive potential energy is computed based on the distances between the robots and the potential collision points. This repulsive potential energy is used as the cost map of the probabilistic roadmap (PRM), which is constructed in the coordination space for multiple robots taking into account both motion time cost and safety cost. We propose a search method on the PRM to obtain the Pareto-optimal coordination solution for multiple robots. Both simulation and experimental results are presented to demonstrate the effectiveness of the algorithms.     

Fast self-localization method for mobile robots using multiple omnidirectional vision sensors

This paper presents a method for estimating position and orientation of multiple robots from a set of azimuth angles of landmarks and other robots which are observed by multiple omnidirectional vision sensors. Our method simultaneously performs self-localization by each robot and reconstruction of a relative configuration between robots. Even if it is impossible to identify correspondence between each index of the observed azimuth angles and those of the robots, our method can reconstruct not only a relative configuration between robots using `triangle and enumeration constraints' but also an absolute one using the knowledge of landmarks in the environment. In order to show the validity of our method, this method is applied to multiple mobile robots each of which has an omnidirectional vision sensor in simulation and the real environment. The experimental results show that the result of our method is more precise and stabler than that of self-localization by each robot and our method can handle the combinatorial explosion problem. Correspondence to:T. Nakamura (e-mail: ntakayuk@sys.wakayama-u.ac.jp)     

Particle Filter for Collaborative Multi-Robot Localization Tolerant to Recognition Error

Statistical algorithms using particle filters have been proposed previously for collaborative multi-robot localization. In these algorithms, by synchronizing each robot's belief or exchanging the particles of the robots, fast and accurate localization is attained. However, there algorithms assume correct recognition of other robots and the effects of recognition error are not considered. If the recognition of other robots is incorrect, a large amount of error in localization can occur. This paper describes this problem. Furthermore, in order to cope with the problem, an algorithm for collaborative multi-robot localization is proposed. In the proposed algorithm, the particles of a robot are exchanged with those of other robots according to measurement results obtained by the sending robot. At the same time, some particles remain in the sending robot. Received particles from other robots are evaluated using measurement results obtained by the receiving robot. The proposed method copes with recognition error by using the remaining particles, and increases the accuracy of estimation by twice evaluating the exchanged particles of the sending and receiving robots. 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.     

生物交哺行为启发的群体机器人搜集行为研究

为了提高群体机器人系统的整体性能,受生物系统中普遍存在的交哺现象的启发,在原来多机器人系统的基本行为的基础上,提出了一种引入交哺行为的多机器人协作机制。机器人依靠有限的感知能力和局部交互功能,以自组织方式执行目标搜集任务。机器人的内部状态变量反映其执行任务的情况以及对环境和其他机器人的评价。比较机器人的内部状态变量,可以判断是否需要交哺和交哺的方向性。主要目的是减少机器人之间的冲突,降低系统能量消耗的同时,提高机器人搜集目标的效率。最后通过计算机仿真实验以及与其他多机器人协作方法比较,分析该方法对提高系统性能的有效性。     

一种面向移动机器人的光流场计算方法

光流场的计算是移动机器人领域一个很重要的研究课题,但是现有的光流场计算方法并未充分利用移动机器人的特点。文章假设移动机器人配备了双目视觉系统和码盘,在此基础上提出了一种基于立体匹配技术的光流场计算方法。通过对实际采集的图片的对比试验,表明该方法较之传统方法,具有速度更快,准确性更高的优点。     

基于强化学习的多机器人协作

提出了一种动态环境下多个机器人获取合作行为的强化学习方法,该方法采用基于瞬时奖励的Q-学习完成单个机器人的学习,并利用人工势场法的思想确定不同机器人的学习顺序,在此基础上采用交替学习来完成多机器人的学习过程。试验结果表明所提方法的可行性和有效性。     

通信距离约束下异构水下爬游机器人任务分配

为了保证执行任务的水下爬游机器人之间时刻保持信息交互,提出了一种带通信距离约束的异构水下爬游机器人集群任务分配方法;首先,建立了异构水下爬游机器人集群的任务分配数学模型;其次,分析了多水下爬游机器人通信距离、航程等约束条件;最后,采用蚁群优化算法对异构水下爬游机器人集群的任务分配问题进行求解,在满足约束条件情况下实现了多爬游机器人总航行距离最短;仿真验证了该方法在通信距离约束下实现多水下爬游机器人任务分配的有效性.