多机器人同步定位与地图构建的地图融合算法的改进

本文主要研究了多机器人同步定位与地图构建(SLAM)的地图实时融合问题.在本文中提出一种混合的SLAM算法(HybridSLAM)算法,可以同时观测和更新多个路标,并根据FastSLAM2.0思想利用选取的最准确的路标观测值来修正机器人位姿.然后,在改进HybridSLAM算法基础上,进一步提出一种改进的多机器人HybridSLAM算法(MR–IHybridSLAM).每个机器人在不同初始位置运行IHybridSLAM算法构建子地图,并将子地图信息实时发送到同一工作站中.根据卡尔曼滤波(KF)原理将每个机器人构建的子地图融合成全局地图.最后,通过仿真实验构建多机器人融合的特征地图并与单一机器人快速的SLAM算法(FastSLAM)和HybridSLAM算法构建的地图进行误差对比,进一步来验证该算法的准确性、快速性和可行性.     

Fast and accurate map merging for multi-robot systems

We present a new algorithm for merging occupancy grid maps produced by multiple robots exploring the same environment. The algorithm produces a set of possible transformations needed to merge two maps, i.e translations and rotations. Each transformation is weighted, thus allowing to distinguish uncertain situations, and enabling to track multiple cases when ambiguities arise. Transformations are produced extracting some spectral information from the maps. The approach is deterministic, non-iterative, and fast. The algorithm has been tested on public available datasets, as well as on maps produced by two robots concurrently exploring both indoor and outdoor environments. Throughout the experimental validation stage the technique we propose consistently merged maps exhibiting very different characteristics.

多机器人系统的一种快速会话建立方法

H.324M作为多媒体移动终端的标准,使得多机器人系统的实时多媒体通讯成为了可能。在H.324M的基础上,针对数据传输的多路技术,开发了一种高效的移动多媒体传输协议栈,一个机器人能够同时处理两个或者更多的信息,使得多机器人系统能够加速会话建立、进行多媒体通讯。理论与实验结果表明,该方法相对于H.324M中推荐的方法,会话建立时间缩短了约50%。     

多机器人系统任务分配的研究进展

多机器人系统任务分配是机器人研究领域一个关键的研究课题。从多机器人任务分配分类及问题描述、多机器人任务分配的研究动态等方面对多机器人任务分配进行了综述,并根据近期文献探讨了多机器人系统任务分配需要解决的若干重要问题。     

连通性保持下的多机器人系统分布式群集控制

多机器人系统群集控制中的连通性保持对于系统的稳定性和状态收敛的快速性具有重要影响.本文在初始通信网络拓扑为强连通非平衡图的条件下,研究具有非完整约束运动学模型的多轮式移动机器人系统群集运动中的连通性保持控制问题.首先,构造了一类新颖的光滑有界的人工势场函数,该类函数可以同时满足连通性保持、碰撞规避和相对距离镇定等任务需求.进一步,将基于势函数梯度的控制策略与一致性控制机制有机结合,在系统中存在和不存在领航者的条件下,分别设计出一类具有连通性保持功能的光滑有界的分布式群集控制协议,不仅可有效避免不连续/非光滑控制器所固有的抖振现象以及执行器饱和问题,而且实现了将传统群集控制中的连通性保持算法从个体运动模型和系统通信拓扑类型两个方面同时加以拓展.最后,仿真结果和实验结果验证了本文所提出的光滑有界群集控制算法的有效性.     

The order of merging operations for queries in inverted file systems

In inverted file systems, queries can be written as Boolean expressions of inverted attributes. In response to a query, the system accesses address lists associated with the attributes in the query, merges them, and selects those records that satisfy the search logic. In this paper we consider the minimization of the CPU time needed for the merging operation. The time can possibly be reduced by taking address lists that occur in several product terms as a common factor of these products. This means that the union operation must be performed before the intersection operation. We present formulas which can be used to decide whether the above method is advantageous. The time can also be reduced by choosing the order of intersection operations so that it takes into consideration the occurrences of the address lists in the products and the lengths of the address lists. For choosing the order of intersection operations we give a heuristic algorithm that minimizes the total time needed for intersections.     

Formation control for multi-robot systems with collision avoidance

In this paper, distributed formation is studied for a team of mobile robots including leaders and followers. Followers are able to sense the relative displacements to neighbouring followers and all of the leaders, and the leaders can be sensed by the followers. Based on such assumption of sensing, distributed formation control scheme is designed, under which both followers team and leaders team are fully independent. The followers and leaders have exchangeable roles within their own group. The leaders can have an arbitrary formation, and around the leaders, the followers need to reach a regular polygon formation with a suitable orientation. Distributed control laws and localised collision avoidance algorithms are designed for each follower, and they use only local displacements. Speed and acceleration sensors are avoided. As the leaders and the followers are independent and exchangeable, both robot teams are scalable and robust against member failures and system delays.     

异构多机器人协作系统研究进展

异构多机器人系统可以发挥单一结构机器人在某个领域的优点而达到整体的最优配置,机器人的功能和接口协议对协作系统影响很大.IGRS协议是我国在信息设备协作领域中惟一的国际标准,为异构多机器人协作提供了有效的支持.对国内外相关研究进行系统地归纳和总结,找出需要解决的问题,并在课题组研制的多种机器人平台上,从3个方面阐述了基于IGRS协议的异构多机器人协作系统:异构机器人的定位、通信以及感知方案;异构机器人协商策略和分组方案;机器人的功能分类和规划,提出了细粒度可控的任务委托分配方案.     

栅格地图中多机器人协作搜索目标

目标搜索是多机器人领域的一个挑战.本文针对栅格地图中多机器人目标搜索算法进行研究.首先,利用Dempster-Shafer证据理论将声纳传感器获取的环境信息进行融合,构建搜索环境的栅格地图.然后,基于栅格地图建立生物启发神经网络用于表示动态的环境.在生物启发神经网络中,目标通过神经元的活性值全局的吸引机器人.同时,障碍物通过神经元活性值局部的排斥机器人,避免与其相撞.最后,机器人根据梯度递减原则自动的规划出搜索路径.仿真和实验结果显示本文提及的算法能够实现栅格地图中静态目标和动态目标的搜索.与其他搜索算法比较,本文所提及的目标搜索算法有更高的效率和适用性.     

基于排斥信息素的多机器人协作研究

提出了两种信息素应用的分类方法,一种是将信息素分为点信息素和线信息素,另一种是将信息素分为吸引信息素和排斥信息素。提出了三种排斥信息素应用的多机器人协作任务场合,并进行了仿真研究。仿真结果表明排斥信息素的应用是一种有效的多机器人协作方法。     

面向多机器人系统的增强学习研究进展综述

基于增强学习的多机器人系统优化控制是近年来机器人学与分布式人工智能的前沿研究领域.多机器人系统具有分布、异构和高维连续空间等特性,使得面向多机器人系统的增强学习的研究面临着一系列挑战,为此,对其相关理论和算法的研究进展进行了系统综述.首先,阐述了多机器人增强学习的基本理论模型和优化目标;然后,在对已有学习算法进行对比分析的基础上,重点探讨了多机器人增强学习理论与应用研究中的困难和求解思路,给出了若干典型问题和应用实例;最后,对相关研究进行了总结和展望.     

一种基于效益的多机器人避碰协调策略

多移动机器人系统在完成同时定位和地图构建SLAM任务时,机器人之间常常存在相互碰撞的问题,而这种碰撞的避免又不同于一般的避障,因为避障问题中的障碍物一般是不动的。为了解决机器人之间的避碰问题,提出了一种基于效益的多机器人避碰协调策略。该策略以提高多机器人系统探索效率为主,确定机器人通过交叉路口的顺序。同时考虑了动态协调避碰的情况,给出了确定机器人通过交叉路口顺序的算法。通过机器人在交叉路口实现避碰协调算法的仿真示例,对该方法的避碰协调过程进行了说明,并对仿真结果进行了分析,同时对仿真中机器人和目标位置的空间关系给出了合理的假设。     

The effect of learning by imitation on a multi-robot system based on the coupling of low-level imitation strategy and online learning for cognitive map building

It is assumed that future robots must coexist with human beings and behave as their companions. Consequently, the complexities of their tasks would increase. To cope with these complexities, scientists are inclined to adopt the anatomical functions of the brain for the mapping and the navigation in the field of robotics. While admitting the continuous works in improving the brain models and the cognitive mapping for robots’ navigation, we show, in this paper, that learning by imitation leads to a positive effect not only in human behavior but also in the behavior of a multi-robot system. We present the interest of low-level imitation strategy at individual and social levels in the case of robots. Particularly, we show that adding a simple imitation capability to the brain model for building a cognitive map improves the ability of individual cognitive map building and boosts sharing information in an unknown environment. Taking into account the notion of imitative behavior, we also show that the individual discoveries (i.e. goals) could have an effect at the social level and therefore inducing the learning of new behaviors at the individual level. To analyze and validate our hypothesis, a series of experiments has been performed with and without a low-level imitation strategy in the multi-robot system.     

改进萤火虫算法求解多机器人路径规划

为解决多机器人在静态环境中的路径规划问题,以路径长度为优化目标模型,并针对此模型设计了多机器人萤火虫算法(MR-FA)。首先,考虑到路径安全性对环境中的障碍物采取扩张操作,设计初始化规则以提高生成初始种群的效率;其次,根据算法的连续性原理及特点,设计个体等长策略将维度不一致的个体转变为等维度个体以便于萤火虫的移动更新,并对移动更新后的不可行解采取路径修正策略;然后对规划出的每个机器人的移动路径进行碰撞检测,同时针对机器人不同的碰撞情况设计相应的避碰策略,即暂停—回退策略(PFS)、局部路径重规划策略(LPRS);最后,为验证MR-FA的有效性,在三组环境中进行仿真实验并与其他三种算法进行对比,综合得出MR-FA在解决多机器人路径规划时更有优势。     

基于免疫自适应遗传算法的机器人栅格地图融合

融合各机器人独自创建的环境地图,实现信息共享,是提高分布式多移动机器人系统环境探索效率的关键.研究了在没有公共参考坐标系及机器人相对位置信息未知情况下的栅格地图融合问题,提出了一种基十免疫自适应遗传算法的栅格地图融合方法,该算法把反映两个栅格地图重叠区域相异程度的优化函数作为抗原,每个可能的平移、旋转平面转换对应一个抗体.仿真结果表明了该算法可以较快的收敛速度和较强的全局搜索能力,搜索到两个栅格地图的最佳重叠区域,实现地图融合.     

多机器人系统任务分配效用评价算法

为了提高复杂环境中多机器人系统任务分配的决策质量,获取准确、客观的效用评价,提出了一种基于自适应神经-模糊推理系统(adaptive neuro-fuzzy inference system,ANFIS)的效用评价算法ANFIS-UE.设计了基于ANFIS的效用评价网络结构,并采用Q学习对效用评价网络的参数进行学习.利用ANFIS优越的函数逼近能力和泛化能力,提高了效用函数的学习效率,能够对连续的状态输入产生连续的效用评价值.实验结果表明,该算法获得的效用评价相对更准确,从而提高了任务分配方案的质量.     

一种基于离散时间一致性理论的多机器人分布式巡逻算法

在多机器人巡逻任务中,由于通信距离的限制,单个机器人很难获得全局信息。然而,现有的大多数多机器人分布式巡逻算法都要求每个机器人获得其巡逻区域的全局信息进行决策。因此,考虑到通信半径约束和局部信息约束,为了通过相邻机器人之间的交互完成巡逻任务,基于离散时间一致性理论提出了两种巡逻算法。算法1使用全局信息进行决策,算法2基于离散时间一致性理论实现局部信息对全局信息的预测进行决策。通过模拟器Stage对所提算法与对比算法在不同机器人数量、通信半径、地图环境下进行了对比。实验验证了所提出的基于局部信息的分布式多机器人巡逻算法具有与原算法类似的特性和性能,能够使机器人在没有全局信息的情况下判断全局状态,并基于邻居之间的协商完成巡逻任务。     

基于强化学习的未知环境多机器人协作搜集

针对多机器人协作复杂搜集任务中学习空间大,学习速度慢的问题,提出了带共享区的双层强化学习算法。该强化学习算法不仅能够实现低层状态-动作对的学习,而且能够实现高层条件-行为对的学习。高层条件-行为对的学习避免了学习空间的组合爆炸,共享区的应用强化了机器人间协作学习的能力。仿真实验结果说明所提方法加快了学习速度,满足了未知环境下多机器人复杂搜集任务的要求。     

A framework for multi-robot motion planning from temporal logic specifications

We propose a framework for the coordination of a network of robots with respect to formal requirement specifications expressed in temporal logics.A regular tessellation is used to partition the space of interest into a union of disjoint regular and equal cells with finite facets,and each cell can only be occupied by a robot or an obstacle.Each robot is assumed to be equipped with a finite collection of continuous-time nonlinear closed-loop dynamics to be operated in.The robot is then modeled as a hybrid automaton for capturing the finitely many modes of operation for either staying within the current cell or reaching an adjacent cell through the corresponding facet.By taking the motion capabilities into account,a bisimilar discrete abstraction of the hybrid automaton can be constructed.Having the two systems bisimilar,all properties that are expressible in temporal logics such as Linear-time Temporal Logic,Computation Tree Logic,and μ -calculus can be preserved.Motion planning can then be performed at a discrete level by considering the parallel composition of discrete abstractions of the robots with a requirement specification given in a suitable temporal logic.The bisimilarity ensures that the discrete planning solutions are executable by the robots.For demonstration purpose,a finite automaton is used as the abstraction and the requirement specification is expressed in Computation Tree Logic.The model checker Cadence SMV is used to generate coordinated verified motion planning solutions.Two autonomous aerial robots are used to demonstrate how the proposed framework may be applied to solve coordinated motion planning problems.