多移动机器人系统研究发展近况

多移动机器人系统具有广泛的应用前景, 也是近年来机器人研究的热门课题之一. 本文对国内外近年来关于多移动机器人系统的研究工作进行了总结和分析, 重点介绍了多机器人系统的任务规划、运动规划、协调控制等问题的研究发展现状. 最后指出了多移动机器人系统研究急需解决的若干重要问题.     

多移动机器人系统运动协调研究综述

运动协调是多移动机器人系统领域的主要研究热点之一。在阐述多机器人合作与运动协调两者关系的基础上,给出了多机器人系统运动协调的问题描述及其分类;从主要研究方法的角度,归纳总结了多机器人系统运动协调的国内外研究动态。最后,对运动协调在多移动机器人系统领域的前景和研究方向作出了展望。     

多移动机器人避障编队控制

研究多移动机器人避障优化设计,针对多移动机器人在障碍物环境下的编队控制问题,为了保持整体合理避障和控制系统的稳定性和安全性,提出一种多机器人避障编队控制策略.首先获得多移动机器人编队的队形结构模型,结合多机器人完成避障编队任务的问题描述;在此基础上引入导航函数采用一种避障编队控制算法,使移动机器人能以设定的队形运动到目标点,可保证编队运动过程中未与障碍物发生碰撞.进行仿真的结果证明,所提算法解决了多机器人编队与避障问题,并保证了闭环系统的稳定性与安全性,验证了设计方法的有效性.     

智能移动机器人的超声避障研究

智能移动机器人是机器人研究领域的重要方向,是当前机器人领域中最活跃的研究主题之一.在分析了智能移动机器人避障常用传感器的基础上,提出了基于多超声传感器的移动机器人的超声避障系统.介绍了超声避障系统的模糊控制规则和非模糊化,并给出了实验结果.实验结果表明,模糊控制机理和策略易于接受和理解,便于应用开发,模糊避障算法对环境有很大的适应性,机器人在不同的环境条件下实现了避障.     

基于SBC智能移动机器人控制系统与路径规划的研究

本文论述了以SBC为核心的智能移动机器人控制系统以及基于圆弧路径的策略研究.移动机器人的系统组成结构提高了机器人的开放性,基于"NC"嵌入"PC"的运动控制方式,提高了机器人的控制效率,路径控制策略的圆弧算法有利于提高移动机器人运动的运动性能.     

基于混合结构的机器人Agent控制结构的研究

该文面向分布Agent多移动机器人系统,提出了一种适合于多移动机器人的机器人Agent分层式体系结构,包括状态监测层、决策规划层、协调控制层和行为控制层,其中状态监测层主要实现整个系统对外部环境的状态监测。决策规划层设定系统的全局目标和单个机器人的局部目标,合理快速地完成任务的分解和分配,实现机器人之间任务级之间的协作。协调控制层完成机器人之间的运动协调。行为控制器主要采用基于行为的方法实现具体的运动控制。该结构应用于RoboCup环境下的分布多机器人系统中,满足复杂的、动态的应用环境和系统要求。     

基于改进弹簧模型的移动机器人柔顺跟随行人方法

针对人机共融环境下的跟随型自动搬运机器人,为了解决被跟随目标突发性运动造成机器人失灵的难题,提出了一种基于改进弹簧模型的移动机器人柔顺跟随方法.给被跟随目标的腿部和障碍物添加虚拟弹簧,完成了对机器人和被跟随目标之间相对位姿的闭环控制,从而实现躲避障碍物和自然交互任务.特别地,通过给虚拟弹簧添加动态阻尼系数,实现了移动机器人跟随目标运动的实时性和柔顺性.通过Simulink仿真对比移动机器人对被跟随目标的柔顺跟随轨迹,实现对改进弹簧模型的参数优化.采用自主开发的两轮差速移动机器人和Vicon光学运动捕捉系统,在被跟随目标做无规律、长距离运动的条件下,验证了该移动机器人跟随轨迹的平滑性和柔顺性.     

运用异质传感器信息融合的移动机器人自定位

采用单类、单一传感器很难获得移动机器人的准确定位.为此,运用异质传感器信息融合来提高定位精度.首先,建立机器人运动方程和CCD摄像机观测模型.然后,利用扩展卡尔曼滤波器进行状态估计,选择Q,R矩阵抑制系统的模型噪声和量测噪声,并实现移动机器人的自定位.接着,建立超声波传感器的观测模型,获得机器人的自定位信息.最后,运用BP神经网络,将两种自定位信息进行融合,实现两类传感器的优缺点互补.仿真实验表明,运用异质传感器信息融合能明显地提高移动机器人的自定位精度.     

勇攀移动机器人技术研究新高峰

本刊记者:能介绍一下您在移动机器人方面的研究成果吗? 费尔南多·戈麦斯·布拉沃教授(以下简称布拉沃教授):我主要从事运动规划算法的研究,机器人实际执行能力取决于它的运动性能.运动规划算法能为其提供一系列的运动规划,使机器人更好地完成预期任务.在我的职业生涯中我还开发了一些科学的战略规划和控制机器人运动的项目,可以让机器人安全地避开障碍物和执行复杂的任务,如停车场停放汽车或者拖走停放的车.这些策略都运用了先进的计算机技术和人工智能技术. 本刊记者:移动机器人目前在西班牙处于什么研究阶段,何时能实现产业化?     

基于遗传算法的多移动机器人协调路径规划

采用链接图法建立了机器人工作空间模型;应用遗传算法规划多移动机器人运动路径; 引入适应值调整矩阵新概念,以达到对多移动机器人运动路径的全局优化;基于面向对象技术,研 制成功多移动机器人路径规划动态仿真系统.大量仿真实验结果表明,所提方法可行.     

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。     

一种基于行为效用理论的多移动机器人编队协调方法

本文提出了一种基于行为效用理论的多移动机器人编队协调方法，用于解决多移动机器人编队初始化问题，实现自治的多移动机器人按特定的空间结构进行编队以完成特定的任务．我们对编队问题协调控制策略和算法进行了研究，给出了行为效用方程及两个衡量值，并针对编队问题的协调控制过程中目标点重复决策问题及偏远目标的选择问题进行了探讨并给出了相应的改进协调方案，计算机仿真试验验证了本文所提方法的有效性和可行性．     

多移动机器人路径规划仿真系统的设计与实现

该文提出了一种多移动机器人路径规划仿真系统的设计方案,并在设计的基础上实现了仿真系统OpenSim。该仿真系统由GUI、业务处理层和存储层组成。GUI是图形用户界面,将仿真系统的各种功能提供给用户;业务处理层进行仿真数据的处理,由仿真平台和各个COM组件服务器组成,机器人传感器、控制器和协调器在COM组件中实现;存储层存储仿真过程中的数据,程序通过在存储层数据库中读写数据来获得和发布信息。该系统具有很好的通用性、分布式运行能力和强大的与用户的交互能力,极大地方便了对多移动机器人路径规划算法的研究。     

Maze exploration behaviors using an integrated evolutionary robotics environment

This paper presents results generated with a new evolutionary robotics (ER) simulation environment and its complementary real mobile robot colony research test-bed. Neural controllers producing mobile robot maze searching and exploration behaviors using binary tactile sensors as inputs were evolved in a simulated environment and subsequently transferred to and tested on real robots in a physical environment. There has been a considerable amount of proof-of-concept and demonstration research done in the field of ER control in recent years, most of which has focused on elementary behaviors such as object avoidance and homing. Artificial neural networks (ANN) are the most commonly used evolvable controller paradigm found in current ER literature. Much of the research reported to date has been restricted to the implementation of very simple behaviors using small ANN controllers. In order to move beyond the proof-of-concept stage our ER research was designed to train larger more complicated ANN controllers, and to implement those controllers on real robots quickly and efficiently. To achieve this a physical robot test-bed that includes a colony of eight real robots with advanced computing and communication abilities was designed and built. The real robot platform has been coupled to a simulation environment that facilitates the direct wireless transfer of evolved neural controllers from simulation to real robots (and vice versa). We believe that it is the simultaneous development of ER computing systems in both the simulated and the physical worlds that will produce advances in mobile robot colony research. Our simulation and training environment development focuses on the definition and training of our new class of ANNs, networks that include multiple hidden layers, and time-delayed and recurrent connections. Our physical mobile robot design focuses on maximizing computing and communications power while minimizing robot size, weight, and energy usage. The simulation and ANN-evolution environment was developed using MATLAB. To allow for efficient control software portability our physical evolutionary robots (EvBots) are equipped with a PC-104-based computer running a custom distribution of Linux and connected to the Internet via a wireless network connection. In addition to other high-level computing applications, the mobile robots run a condensed version of MATLAB, enabling ANN controllers evolved in simulation to be transferred directly onto physical robots without any alteration to the code. This is the first paper in a series to be published cataloging our results in this field.     

Model Continuity to Support Software Development for Distributed Robotic Systems: A Team Formation Example

Modeling, design and testing of the software underlying distributed robotic systems is a challenging task, especially when a large number of mobile robots and task coordination are involved. Model continuity, the ability to use the same model of a system throughout its design phases, provides an effective way to manage this development complexity and maintain consistency of the software. In this paper, we describe the design and implementation of a team-formation multi-robot system. This is used as an example to demonstrate how a modeling and simulation environment, based on the DEVS formalism, can support model continuity in the design of distributed robotic systems. This example shows how the intelligent control models of the robots are first designed and tested via simulation and, when verified mapped to physical robots with DEVS-on-a-chip brains for execution.     

异质多移动机器人协同技术研究的进展

随着移动机器人应用的领域和范围的不断扩展，多移动机器人由于其单个机器人无法比拟的优越性已经越来越受到重视．从体系结构、协作与协调、协作环境感知与定位、重构及机器学习几个重要课题对多移动机器人协同技术进行了综述，尤其侧重于各种技术如何处理和包容团队中的异质性，并分析了本领域中的研究难点问题，最后展望了异质多移动机器人研究的前景与发展趋势．     

Towards principled experimental study of autonomous mobile robots

We review the current state of research in autonomous mobile robots and conclude that there is an inadequate basis for predicting the reliability and behavior of robots operating in unengineered environments. We present a new approach to the study of autonomous mobile robot performance based on formal statistical analysis of independently reproducible experiments conducted on real robots. Simulators serve as models rather than experimental surrogates. We demonstrate three new results: 1) Two commonly used performance metrics (time and distance) are not as well correlated as is often tacitly assumed. 2) The probability distributions of these performance metrics are exponential rather than normal, and 3) a modular, object-oriented simulation accurately predicts the behavior of the real robot in a statistically significant manner.     

Distributed Constraint Force Approach for Coordination of Multiple Mobile Robots

A new approach to coordination of multiple mobile robots is presented in this paper. The approach relies on the notion of constraint forces which are used in the development of the dynamics of a system of constrained particles with inertia. A familiar class of dynamic, nonholonomic robots are considered. The goal is to design a distributed coordination control algorithm for each robot in the group to achieve, and maintain, a particular formation while ensuring navigation of the group. The theory of constraint forces is used to generate a stable control algorithm for each mobile robot that will achieve, and maintain, a given formation. The advantage of the proposed method is that the formation keeping forces (constraint forces) cancel only those applied forces which act against the constraints. Another feature of the proposed distributed control algorithm is that it allows to add/remove other mobile robots into/from the formation gracefully with simple modifications of the control input. Further, the algorithm is scalable. To corroborate the theoretical approach, simulation results on a group of six robots are shown and discussed.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.