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。     

多目标遗传算法在混流装配线排序中的应用

混流装配线实现在一条流水线上装配多种不同类型的产品。该文在总结混流装配线排序问题的基础上建立了二种排序的目标函数：最小化工作站的闲置与超载时间和保持均匀的零部件消耗速率。引入了基于Pareto理论和小生镜单元技术的适应度函数及选择算子构建了多目标遗传算法用于混流装配线的排序优化问题。通过一个混流装配线的多目标排序实验,验证了该方法的有效性。     

Deliberative On-Line Local Path Planning for Autonomous Mobile Robots

This paper describes a method for local path planning for mobile robots that combines reactive obstacle avoidance with on-line local path planning. Our approach is different to other model-based navigation approaches since it integrates both global and local planning processes in the same architecture while other methods only combine global path planning with a reactive method to avoid non-modelled obstacles. Our local planning is only triggered when an unexpected obstacle is found and reactive navigation is not able to regain the initial path. A new trajectory is then calculated on-line using only proximity sensor information. This trajectory can be improved during the available time using an anytime algorithm. The proposed method complements the reactive behaviour and allows the robot to navigate safely in a partially known environment during a long time period without human intervention.     

多机器人系统中的动态避碰规划

研究冲突区域中多机器人间的协调和避碰问题。采用集中-分布相结合的规划方法,根据系统的拓扑结构为每个机器人规划路径,在冲突区域内使用优先级策略对机器人的运动特征进行分布式规划。通过上下层智能的融合,提高整个系统的智能。     

基于再励学习的多移动机器人协调避障路径规划方法

随着多移动机器人协调系统的应用向未知环境发展,一些依赖于环境模型的路径规划方法不再适用。而利用再励学习与环境直接交互,不需要先验知识和样本数据的特点,该文将再励学习应用于多机器人协调系统中,提出了基于再励学习的避障路径规划方法,并将再励函数设计为基于行为分解的无模型非均匀结构。计算机仿真实验结果表明该方法有效,并有较好的鲁棒性,新的再励函数结构使得学习速度得以提高。     

基于量子粒子群算法的机器人路径规划

提出了一种基于量子粒子群优化算法的移动机器人全局路径规划方法。首先对环境地图进行建模,通过坐标变换在路径的起点与终点之间建立新地图,然后利用量子粒子群优化算法获得一条全局最优路径。该方法模型简单,算法复杂度低,收敛速度快,而且模型不依赖于障碍物的形状。仿真实验证实了该方法的可行性与有效性。     

Contact Friction Compensation for Robots Using Genetic Learning Algorithms

In this paper, the issues of contact friction compensation for constrained robots are presented. The proposed design consists of two loops. The inner loop is for the inverse dynamics control which linearizes the system by canceling nonlinear dynamics, while the outer loop is for friction compensation. Although various models of friction have been proposed in many engineering applications, frictional force can be modeled by the Coulomb friction plus the viscous force. Based on such a model, an on-line genetic algorithm is proposed to learn the friction coefficients for friction model. The friction compensation control input is also implemented in terms of the friction coefficients to cancel the effect of unknown friction. By the guidance of the fitness function, the genetic learning algorithm searches for the best-fit value in a way like the natural surviving laws. Simulation results demonstrate that the proposed on-line genetic algorithm can achieve good friction compensation even under the conditions of measurement noise and system uncertainty. Moreover, the proposed control scheme is also found to be feasible for friction compensation of friction model with Stribeck effect and position-dependent friction model.     

Online Learning and Adaptation of Autonomous Mobile Robots for Sustainable Agriculture

In this paper we will introduce the application of our newly patented double hierarchical Fuzzy-Genetic system (British patent 99-10539.7) to produce an intelligent autonomous outdoor agricultural mobile robot capable of learning and calibrating its controller online in a short time interval and implementing a life long learning strategy. The online and life long learning strategy allow the outdoor robots to increase their experience and adapt their controllers in the face of the changing and dynamic unstructured outdoor agricultural environments. Such characteristics permit prolonged periods of operation within dynamic agricultural environments, which is an essential feature for the realization of a platform vehicle for use in sustainable agriculture and organic farming.     

群体自主微小型移动机器人的合作

单个微小型机器人由于自身能力的限制，因此必须多个机器人联合起来才可以完 成指定的任务，所以机器人之间的协作在微操作领域就显得尤其重要。该文利用增强式的 学 习方法，使得微小型机器人具有一定的学习能力，增强了对不确定环境的适应性，并采 用了 一种基于行为的群体自主式微小移动机器人的协作结构，用于机器人的故障排除，仿 真结果 说明了这种体系结构的有效性。     

智能机器人路径规划及算法研究

路径规划技术是机器人控制技术研究中的一个重要问题,目前的研究主要分为全局规划方法和局部规划方法两大类。在对一些较有代表性的研究思想及其相关算法分析的基础上,比较各种方法的优缺点,提出了机器人路径规划今后的研究重点。     

Genetic operators for hierarchical graph clustering

In this paper we propose an encoding scheme and ad hoc operators for a genetic approach to hierarchical graph clustering. Given a connected graph whose vertices correspond to points within a Euclidean space and a fitness function, a hierarchy of graphs in which each vertex corresponds to a connected subgraph of the graph below is generated. Both the number of clustering levels and the number of clusters on each level are not fixed a priori and are subject to optimization.     

不同目标函数对用GA寻优Fuzzy控制规则的影响

本文首先简单介绍了遗传算法和模糊控制自寻优,然后给出了用遗传算法对模糊控制规则进行了自寻优的方法,并通过仿真比较不了同目标函数对用遗传算法寻优模糊控制规则的影响,得出了一些结论。     

基于遗传多目标优化算法的Pareto最优解研究

遗传算法可有效求解多目标优化问题中的Pareto最优解,并利用MATLAB进行了仿真验证。     

Parametric Planning for Multiple Cooperative Robots

A novel planning strategy, parametric planning, is proposed to negotiate the task-oriented object manipulation of multiple coordinated robots. The approach provides an advantage to improve flexibility of robotic cooperation, in which the desired trajectories in Cartesian space derived from task requirements are converted into the trajectories of robots in joint space for a fixed-coordinated multi-robot system. For this purpose, a parametric cooperative index matrix is introduced to handle the relationship of the input desired Cartesian trajectories and the position of robots. A case study of 2-dimension object-motion trajectory tracking using four robots is presented in the end. It proved that the proposed approach effectively delivers trajectory task requirements to the joint trajectories of robots.     

求解函数优化问题的遗传算法

从遗传算法的编码、遗传算子等方面剖析了遗传算法求解无约束函数优化问题的一般步骤,并以一个实例说明遗传算法能有效地解决函数优化问题。     

基于遗传算法的QoS多播路由优化算法

研究了带宽、延时、延时抖动和分组丢失率约束以及费用最小的QoS多播路由优化问题,提出了一种启发式遗传算法、该算法采用可变长度染色体(路由串)和它的基因(节点)应用于编码问题。交叉操作在交叉点进行部分染色体(部分路由)交换,变异操作维持种群的多样性。该算法采用简单维护操作维护好所有的不可行的染色体,交叉操作和变异操作相结合保证了最优解的搜索能力和解的全局收敛性。计算机仿真实验证明该算法快速有效,可靠性高。     

基于遗传算法的进化压力的研究

为了解影响进化压力的因素,在群体大小的选择、选择运算（Selection）、交叉运算（Crossover）三个方面进行了详细的分析。通过在遗传算法的三个阶段,选取不同的方案来解决复杂的函数优化问题的实验方法,得出了不同方案对进化压力的影响差异。时差异进行了比较分析,并对实际运用中方案的选择提出了一些建议。     

基于VR的自由飞行空间机器人自主运动规划仿真系统

该文研究了基于VR的自由飞行空间机器人（FFS）自主运动规划仿真系统技术。首先提出了一种增强型虚拟现实系统方案,并重点研究了有关FFSR的虚拟建模和自主运动规划控制技术,最后进行了基于VR的FFSR自主运动规划际统仿真。     

Evolving Receptive-Field Controllers for Mobile Robots

The use of evolutionary methods to generate controllers for real-world autonomous agents has attracted recent attention. Most of the pertinent research has employed genetic algorithms or variations thereof. Recent research has applied an alternative evolutionary method, evolution strategies, to the generation of simple Braitenberg vehicles. This application accelerates the development of such controllers by more than an order of magnitude (a few hours compared to more than two days). Motivated by this useful speedup, this paper investigates the evolution of more complex architectures, receptive-field controllers, that can employ nonlinear interactions and, therefore, can yield more complex behavior. It is interesting to note that the evolution strategy yields the same efficacy in terms of function evaluations, even though the second class of controllers requires up to 10 times more parameters than the simple Braitenberg architecture. In addition to the speedup, there is an important theoretical reason for preferring an evolution strategy over a genetic algorithm for this problem, namely the presence of epistasis.     

旅行商问题(TSP)的一种改进遗传算法

传统的序号编码遗传算法(GA)使用PMX、CX和OX等特殊的交叉算子,这些算子实施起来很麻烦。针对TSP问题的求解,提出了一种新的改进遗传算法：单亲进化遗传算法(PEGA),PEGA是利用父体所提供的有效边的信息,使用保留最小边的方法进行个体的进化。与传统的遗传算法相比,PEGA算法弥补了它们的不足之处,简化了遗传算法。给出了PEGA算法的数值算例,仿真实验表明了该算法对于对称的TSP和非对称的TSP问题,都具有收敛速度快的特点,证明了该算法的有效性。