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.     

Small robot agents with on-board vision and local intelligence

We designed a family of completely autonomous mobile robots with local intelligence. We developed a controller with a variety of digital and analog I/O facilities and the operating system RoBIOS, which allows maximum flexibility. The robots have a number of on-board sensors, including vision, and do not rely on global sensor systems. The on-board computing power is sufficient to analyze several color images per second. This enables the robots to perform several different task such as navigation, map generation or intelligent group behavior and does not limit them to the game of robot soccer.     

Tracking multiple moving targets with swarms of mobile robots

This paper presents a distributed approach to enable mobile robot swarms to track multiple targets moving unpredictably. The proposed approach consists of two constituent algorithms: local interaction and target tracking. When the robots are faster than the targets, Lyapunov theory can be applied to show that the robots converge asymptotically to each vertex of the desired equilateral triangular configurations while tracking the targets. Toward practical implementation of the algorithms, it is important to realize the observation capability of individual robots in an inexpensive and efficient way. A new proximity sensor that we call dual rotating infrared (DRIr) sensor is developed to meet these requirements. Both our simulation and experimental results employing the proposed algorithms and DRIr sensors confirm that the proposed distributed multi-target tracking method for a swarm of robots is effective and easy to implement.     

基于无线传感器网络的移动机器人智能导航算法

结合了无线传感器技术和群集智能技术两者的优势,提出一种新的基于无线传感器网络的移动机器人智能导航控制算法,并考虑了能量消耗的问题。算法利用基于多传感器信息融合的全局概率地图构建技术、使用群集仿生智能的基于微粒群算法的实时在线路径规划以及避障策略,提高了智能导航的整体性能,满足了在复杂环境和未知障碍物下导航的实时要求。最后设计并构造出了实际的无线传感器网络和实际的机器人系统,验证了算法成功实现机器人导航的有效性和准确性。     

移动机器人非视觉传感器及其信号处理方法

非视觉传感器是机器人认识和了解外部环境的重要途径，移动机器人常用的非视觉 传感器包括超声、红外、接近传感器等．这些传感器大多是以环或阵列的形式出现，因此其 信号处理往往要占用机器人大量的CPU时间．本文提出了一种采用多DSP控制和处理各类非视 觉传感器的方法，给出了传感器信号处理的原理和具体实现．同时我们引入了并行处理的机 制，各类传感器信号处理可同时进行，在很大程度上提高了机器人传感器信号处理的速度， 有利于机器人在实时动态环境中运行．并给出了非视觉传感器信号处理的实验结果，验证了 该方法的有效性．      

Path planning of fire-escaping system for intelligent building

We present the path-planning techniques of the fire-escaping system for intelligent building, and use multiple mobile robots to present the experimental scenario. The fire-escaping system contains a supervised computer, an experimental platform, some fire-detection robots and some navigation robots. The mobile robot has the shape of a cylinder, and its diameter, height and weight are 10?cm, 15?cm and 1.5?kg, respectively. The mobile robot contains a controller module, two DC servomotors (including drivers), three IR sensor modules, a voice module and a wireless RF module. The controller of the mobile robot acquires the detection signals from reflective IR sensors through I/O pins and receives the command from the supervised computer via wireless RF interface. The fire-detection robot carries the flame sensor to detect fire sources moving on the grid-based experiment platform, and calculates the more safety escaping path using piecewise cubic Bezier curve on all probability escaping motion paths. Then the user interface uses A* searching algorithm to program escaping motion path to approach the Bezier curve on the grid-based platform. The navigation robot guides people moving to the safety area or exit door using the programmed escaping motion path. In the experimental results, the supervised computer programs the escaping paths using the proposed algorithms and presents movement scenario using the multiple smart mobile robots on the experimental platform. In the experimental scenario, the user interface transmits the motion command to the mobile robots moving on the grid-based platform, and locates the positions of fire sources by the fire-detection robots. The navigation robot guides people leaving the fire sources using the low-risk escaping motion path and moves to the exit door.     

A new space and time sensor fusion method for mobile robot navigation

To fully utilize the information from the sensors of mobile robot, this paper proposes a new sensor‐fusion technique where the sample data set obtained at a previous instant is properly transformed and fused with the current data sets to produce a reliable estimate for navigation control. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar‐sensing system or the visual‐sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more sensors are required to measure a given physical parameter or to improve the reliability of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized for the purpose. The basic principle is illustrated by examples and the effectiveness is proved through simulations and experiments. The newly proposed STSF (space and time sensor fusion) scheme is applied to the navigation of a mobile robot in an environment using landmarks, and the experimental results demonstrate the effective performance of the system. © 2004 Wiley Periodicals, Inc.     

Motion-based identification of multiple mobile robots using trajectory analysis in a well-configured environment with distributed vision sensors

Networked mobile robots are able to determine their poses (i.e., position and orientation) with the help of a well-configured environment with distributed sensors. Before localizing the mobile robots using distributed sensors, the environment has to have information on each of the robots?? prior knowledge. Consequently, if the environment does not have information on the prior knowledge of a certain mobile robot then it will not determine its current pose. To solve this restriction, as a preprocessing step for indoor localization, we propose a motion-based identification of multiple mobile robots using trajectory analysis. The proposed system identifies the robots by establishing the relation between their identities and their positions, which are estimated from their trajectories related to each of the paths generated as designated signs. The primary feature of the proposed system is the fact that networked mobile robots are quickly and simultaneously able to determine their poses in well-configured environments. Experimental results show that our proposed system simultaneously identifies multiple mobile robots, and approximately estimates each of their poses as an initial state for autonomous localization.     

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 computation: complexity,algorithms, and logic

Natural Computing - We investigate algorithmic control of a large swarm of mobile particles (such as robots, sensors, or building material) that move in a 2D workspace using a global input signal...     

A geometric approach to deploying robot swarms

We discuss the fundamental problems and practical issues underlying the deployment of a swarm of autonomous mobile robots that can potentially be used to build mobile robotic sensor networks. For the purpose, a geometric approach is proposed that allows robots to configure themselves into a two-dimensional plane with uniform spatial density. Particular emphasis is paid to the hole repair capability for dynamic network reconfiguration. Specifically, each robot interacts selectively with two neighboring robots so that three robots can converge onto each vertex of the equilateral triangle configuration. Based on the local interaction, the self-configuration algorithm is presented to enable a swarm of robots to form a communication network arranged in equilateral triangular lattices by shuffling the neighbors. Convergence of the algorithms is mathematically proved using Lyapunov theory. Moreover, it is verified that the self-reparation algorithm enables robot swarms to reconfigure themselves when holes exist in the network or new robots are added to the network. Through extensive simulations, we validate the feasibility of applying the proposed algorithms to self-configuring a network of mobile robotic sensors. We describe in detail the features of the algorithm, including self-organization, self-stabilization, and robustness, with the results of the simulation.     

基于虚拟现实技术的移动机器人视觉伺服控制系统设计

为了有效确保移动机器人视觉伺服控制效果,提高移动机器人视觉伺服控制精度,设计了基于虚拟现实技术的移动机器人视觉伺服控制系统。通过三维视觉传感器和立体显示器等虚拟环境的I/O设备、位姿传感器、视觉图像处理器以及伺服控制器元件,完成系统硬件设计。从运动学和动力学两个方面,搭建移动机器人数学模型,利用标定的视觉相机,生成移动机器人实时视觉图像,通过图像滤波、畸变校正等步骤,完成图像的预处理。利用视觉图像,构建移动机器人虚拟移动环境。在虚拟现实技术下,通过目标定位、路线生成、碰撞检测、路线调整等步骤,规划移动机器人行动路线,通过控制量的计算,实现视觉伺服控制功能。系统测试结果表明,所设计控制系统的位置控制误差较小,姿态角和移动速度控制误差仅为0.05°和0.12m/s,移动机器人碰撞次数较少,具有较好的移动机器人视觉伺服控制效果,能够有效提高移动机器人视觉伺服控制精度。     

Navigation strategy of multiple mobile robot systems based on the null-space projection method

This paper deals with a navigation algorithm for swarm robot systems in which multiple mobile robots work together. The motion of each mobile robot is modeled in such a way to have more inputs than the number of outputs. The null-space projection method of this model is employed to resolve the motion of the swarm robot system while avoiding obstacles. The feasibility of the proposed navigation algorithm is verified through a simulation study using several swarm robot models.     

基于新型触须传感器的外形检测

啮齿类动物利用触须能够很好地识别外部对象的轮廓，为移动机器人识别被测物体提供了新的思路。借鉴啮齿动物触须的毛囊结构，设计了一种新型触须传感器，传感器采用二维PSD作为敏感元件。此传感器安装在移动机器人上，通过测量触须根部产生的微小位移量，来识别被测物体的外形轮廓。完成了圆柱体和棱柱体样件的对比实验，证明利用该触须传感器移动机器人能够识别出外部对象的轮廓。     

Tracking Targets Using Multiple Robots: The Effect of Environment Occlusion

This paper addresses the problem of tracking multiple targets using a network of communicating robots and stationary sensors. We introduce a Region-based Approach which controls robot deployment at two levels. A coarse deployment controller distributes robots across regions using a topological map which maintains urgency estimates for each region, and a target-following controller attempts to maximize the number of tracked targets within a region. A behavior-based system is presented implementing the Region-Based Approach, which is fully distributed and scalable. We compared the Region-based Approach to a 'naive' local-following strategy in three environments with varying degree of occlusion. The experimental results showed that the Region-based Approach performs better than the naive strategy when the environment has significant occlusion. Second, we performed experiments (the environment was held constant) in which two techniques for computing urgency estimates were compared. Last, different combinations of mobile sensors and stationary sensors were compared in a given environment.     

自主机器人非视觉传感器数据采集系统的研制

介绍了一种基于数字信息处理器(DSP)的非视觉传感器数据采集系统。它以DSPTMS320LF2407A为核心,集成了超声波传感器、红外测障传感器、红外测距传感器和方位传感器等模块,通过多传感器信息融合,可以弥补超声波传感器的盲区问题和多次反射问题,本系统通过串口或者通用系列总线(USB)进行通信。经实验验证:该系统结构简单、配置灵活、功耗低,具有较高的可靠性和实时性。     

Mobile robot navigation using grid line patterns via probabilistic measurement modeling

Mobile robots are generally equipped with proprioceptive motion sensors such as odometers and inertial sensors. These sensors are used for dead-reckoning navigation in an indoor environment where GPS is not available. However, this dead-reckoning scheme is susceptible to drift error in position and heading. This study proposes using grid line patterns which are often found on the surface of floors or ceilings in an indoor environment to obtain pose (i.e., position and orientation) fix information without additional external position information by artificial beacons or landmarks. The grid lines can provide relative pose information of a robot with respect to the grid structure and thus can be used to correct the pose estimation errors. However, grid line patterns are repetitive in nature, which leads to difficulties in estimating its configuration and structure using conventional Gaussian filtering that represent the system uncertainty using a unimodal function (e.g., Kalman filter). In this study, a probabilistic sensor model to deal with multiple hypotheses is employed and an online navigation filter is designed in the framework of particle filtering. To demonstrate the performance of the proposed approach, an experiment was performed in an indoor environment using a wheeled mobile robot, and the results are presented.     

A Localization Method Based on Map-Matching and Particle Swarm Optimization

This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory, a new robotic competition which is started in Lisbon in 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that performs well. The sensor information is continuously updated in time and space according to the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, the Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high, meaning that the map-matching is unreliable and the robot gets lost. The experiments presented in this paper prove the ability and accuracy of the presented technique to locate small mobile robots for this competition. Extensive results show that the proposed method presents an interesting localization capability for robots equipped with a limited amount of sensors, but also less reliable sensors.     

Speech-based formation control of the multi-robot system

The article presents multiple pattern formation control of the multi-robot system using A* searching algorithm, and avoids the collision points moving on the motion platform. We use speech recognition algorithm to decide the various pattern formations, and program mobile robots to present the movement scenario on the grid-based motion platform. We have been developed some pattern formations to be applied in game applications, such as long snake pattern formation, phalanx pattern formation, crane wing pattern formation, sword pattern formation, cone pattern formation and so on. The mobile robot contains a controller module, three IR sensor modules, a voice module, a wireless RF module, a compass module, and two DC servomotors. The controller of the mobile robot can acquire the detection signals from reflect IR sensor modules and the compass module, and decide the cross points of the aisle. The mobile robot receives the command from the supervised computer, and transmits the status of environment to the supervised computer via wireless RF interface. We develop the user interface of the multi-robot system to program motion paths for various pattern formation exchanges using the minimum displacement. Users can use speech to control the multiple mobile robots to execut pattern formation exchange. In the experimental results, users can speak the pattern formation. The speech recognition system receives the signal to decide the pattern formation. The multiple mobile robots can receive the pattern formation command from the supervised computer, and arrange the assigned pattern formation on the motion platform, and avoid other mobile robots.