不确定环境下多机器人的动态编队控制

提出了一种不确定环境下多机器人的动态编队控制方法．通过队形参数矩阵确立多机器人之间的相对 位置关系,将全局队形控制问题转化为跟随机器人离轴点对虚机器人（与领航机器人运动方向一致,且对领航机器 人保持期望的相对距离和观测方位角）离轴点的跟踪．基于建立的跟随机器人和领航机器人之间的误差跟踪系统模 型设计相应控制律实现队形保持,并提出了防止机器人与障碍物及其它机器人碰撞的避障策略．仿真结果表明了所 提方法的可行性和有效性．     

基于遗传算法的BP神经网络优化策略研究

人工神经网络（ANN）可用作机器人控制器,完成多机器人协作搬运作业。针对这种方法收敛速度较慢,误差较大的不足,本文提出基于遗传算法优化的方法。该方法利用遗传算法优化人工神经网络,通过改变ANN结构和遗传算法操作参数,找到最优网络,提高网络收敛速度。仿真结果证明,该方法的可行性与有效性。     

基于并列选择遗传算法的多机器人协作探测

多机器人系统在执行探测任务时,存在多个机器人同时选择相同探测区域而造成碰撞几率增加和探测效率降低的问题.在原有并列选择遗传算法的基础上,提出了一种改进的遗传算法.仿真和实验结果表明,将该算法应用于多机器人协作区域探测,可以减小机器人之间的碰撞几率,提高多机器人系统的探测效率.     

From insect to Internet: Situated control for networked robot teams

Ant-like systems take advantage of agents' situatedness to reduce or eliminate the need for centralized control or global knowledge. This reduces the need for complexity of individuals and leads to robust, scalable systems. Such insect-inspired situated approaches have proven effective both for task performance and task allocation. The desire for general, principled techniques for situated interaction has led us to study the exploitation of abstract situatedness – situatedness in non-physical environments. The port-arbitrated behavior-based control approach provides a well-structured abstract behavior space in which agents can participate in situated interaction. We focus on the problem of role assumption, distributed task allocation in which each agent selects its own task-performing role. This paper details our general, principled Broadcast of Local Eligibility (BLE) technique for role-assumption in such behavior-space-situated systems, and provides experimental results from the CMOMMT target-tracking task. This revised version was published online in August 2006 with corrections to the Cover Date.     

Lifelong Adaptation in Heterogeneous Multi-Robot Teams: Response to Continual Variation in Individual Robot Performance

Generating teams of robots that are able to perform their tasks over long periods of time requires the robots to be responsive to continual changes in robot team member capabilities and to changes in the state of the environment and mission. In this article, we describe the L-ALLIANCE architecture, which enables teams of heterogeneous robots to dynamically adapt their actions over time. This architecture, which is an extension of our earlier work on ALLIANCE, is a distributed, behavior-based architecture aimed for use in applications consisting of a collection of independent tasks. The key issue addressed in L-ALLIANCE is the determination of which tasks robots should select to perform during their mission, even when multiple robots with heterogeneous, continually changing capabilities are present on the team. In this approach, robots monitor the performance of their teammates performing common tasks, and evaluate their performance based upon the time of task completion. Robots then use this information throughout the lifetime of their mission to automatically update their control parameters. After describing the L-ALLIANCE architecture, we discuss the results of implementing this approach on a physical team of heterogeneous robots performing proof-of-concept box pushing experiments. The results illustrate the ability of L-ALLIANCE to enable lifelong adaptation of heterogeneous robot teams to continuing changes in the robot team member capabilities and in the environment.     

Research on Multi-robot Coordination Searching Ignition Sources Approach

For the purpose of study on forecasting forest fire behavior, a probability approach was presented to search ignition sources by multi-robot coordination. Firstly, the environment map is built based on Bayes rules. Then, the probability searching strategy based on the environment map was designed. Every grid of the searching area was assigned searching expectation value, and robots selected the grid with the highest expectation value as its searching target. The simulation results show the search time reduces greatly, which proves the feasibility and validity of the given algorithm under unknown fire condition.     

基于粒子群遗传优化算法的多机器人任务分配研究

在粒子群优化算法中,引入遗传算法中的克隆算子和变异算子,提出了粒子群遗传优化算法,并将多机器人系统的任务分配问题转换为在多维解空间内寻找最优解的问题,利用粒子群遗传优化算法在此空间寻找最优解,以实现对多机器人任务的协调分配.算例仿真表明,粒子群遗传优化算法不但具有粒子群优化算法所具有的易于工程实现、计算效率高等优点,还克服了粒子群优化算法易早熟、粒子群整体收敛性差等缺点,能够解决多机器人任务分配问题.     

基于市场法及能力分类的多机器人任务分配方法

针对多机器人系统研究中如何有效地实现复杂任务的分布式动态分配这个基础性问题，提出了一种对这类问题进行形式化描述的一般方法．该方法从能力分类的角度出发，提出了机器人及任务能力向量的概念，并对多机器人任务分配问题进行了形式化描述，讨论了单个及多个机器人合作完成任务的能力条件．基于这种形式化描述方法，提出了一种采用市场机制的完全分布式的多机器人任务分配方法．仿真实验结果表明该方法能够有效地实现多机器人复杂任务的动态分布式分配．     

多机器人系统的研究现状

介绍了多机器人系统的研究进展及现状,对该领域内的主要内容(思想源泉、体系结构与控制、通信、学习、可重构机器人)的研究现状作了分析和介绍,还提出了一些未来值得研究的议题。     

多机器人系统传感器融合技术应用研究

当前应用于机器人的多传感器数据融合研究大都集中在机器人自主导航和定位问题上,很少涉及利用机器人进行目标跟踪的研究。现有的基于网络时延的融合算法大都直接套用传统的同步数据融合方法,因此会产生数据等待、资源浪费以及较差的实时性能等问题。本文提出结合预测估计以及递推加权融合技术,设计出一种新的能适应网络时延的多传感器预测加权融合算法。该算法不仅能很好的解决现有基于时延的数据融合算法存在的诸多弊端,而且拥有良好的实时预测功能。文中给出了新算法的推导过程,并通过计算机仿真算例来显示新算法的实用性和优越性。