知识引导遗传算法实现机器人路径规划

针对传统遗传算法求解机器人路径规划问题存在的收敛速度较慢的缺陷,设计一种知识引导遗传算法,在染色体的编码,初始种群的产生,各种遗传算子和优化算子中加入相关的领域知识.综合考虑机器人路径的长度,安全度和平滑度等性能指标,在对机器人进行路径规划的同时,利用删除,简化,修正和平滑,种优化算子进行路径优化操作.仿真结果表明.所提方法能够有效提高遗传算法求解实际路径规划问题的能力和效率.

     

量子进化算法研究进展

在介绍量子进化算法(QEA)的原理、特点和基本流程的基础上,重点综述QEA的改进,包括改进基本算子、引入新算子、改变种群规模、扩展为并行算法和构造新型算法框架等.介绍了QEA的应用研究,进而提出了QEA在理论、算法、组合优化、多目标优化与约束优化、不确定优化及应用方面的若干进一步的研究内容.     

基于焦虑概念和拍卖方法的多机器人协作搜集

针对目前应用于多机器人搜集任务的拍卖方法很少考虑机器人参与拍卖的时机是否适当这一问题,在拍卖方法中引入了心理学的焦虑概念,提出了基于焦虑/拍卖的多机器人协作搜集方法.通过对机器人焦虑程度的量化反映其对环境、队友及自身状况的评价,进而反映其对自己独立完成任务的不自信程度和对队友提出的拍卖邀请的主观接受程度.实验结果表明,与单纯的拍卖方法相比,该方法能提高多机器人协作搜集任务的执行效率.     

基于移动机器人路径规划的鼠群算法

研究静态环境下机器人路径规划问题,并根据老鼠觅食行为提出一种鼠群算法.该算法引入环境因子和经验因子,每次搜索后对路径进行经验因子更新,通过迭代的方式寻找静态环境下机器人最佳路径.同时提出一种禁忌策略,有效地避免了路径死锁问题.理论分析和实验结果表明,该算法能使机器人在有较多障碍的环境下迅速找到一条优化路径,而且安全避碰,与同类算法相比具有一定的优越性.

     

深海采矿机器人的行走鲁棒控制

针对深海采矿机器人模型和外界环境不确定性而导致其控制问题异常复杂这一难题,首先用RBF神经网络逼近系统不确定性的上界,在状态反馈的基础上,构造了基于虚拟输入的航向控制鲁棒自适应律;然后给出了左右履带速度输入与虚拟输入之间的模糊控制推理规则,提出了采矿机器人的行走控制方法.仿真结果验证了所提出方法的可行性.

     

一种辨识Wiener-Hammerstein模型的新方法

针对非线性Wiener-Hammerstein模型,提出利用粒子群优化算法对非线性模型进行辨识的新方法.该方法的基本思想是将非线性系统的辨识问题转化为参数空间上的优化问题;然后采用粒子群优化算法获得该优化问题的解.为了进一步增强粒子群优化算法的辨识性能,提出利用一种混合粒子群优化算法.最后,仿真结果验证了该方法的有效性和可行性.

     

多目标混沌差分进化算法

将差分进化算法用于多目标优化问题,提出了多目标混沌差分进化算法(CDEMO).该算法利用混沌序列初始化种群,并用混沌备用种群进行替换操作.该操作不仅起到了维持非劣最优解集均匀性的作用,而且增强了算法的搜索功能.对CDEMO的性能进行研究,数值实验结果表明了CDEMO的有效性.

     

多车场多车型装卸混合车辆路径问题研究

为满足电子商务客户多样化和个性化的需求,建立了多车场、多车型的装卸混合车辆调度模型,并使用混合遗传启发式算法求解．首先采用混合编码,使问题变得更简洁;利用个体数量控制选择策略,以保证群体的多样性;引入２-交换变异策略,并结合爬山算法,加强染色体的局部搜索能力．然后,对混合遗传算法求得的精英种群进行禁忌搜索,提高了搜索效率．最后,通过实例计算表明了上述模型和算法的有效性．

     

可重构模块机器人分散容错控制

针对可重构模块机器人的执行器故障,提出一种基于自适应模糊系统的分散被动容错控制方法．该方法不需要机器人动力学模型与模块之间的信息交换,模块控制器分别采用间接和直接自适应方法设计,自适应参数的更新律基于Ｌｙａｐｕｎｏｖ稳定性理论设计,保证了系统的稳定性和H_∞ 跟踪性能．数值仿真结果表明了所提出方法的有效性．

     

A survey on coverage path planning for robotics

Coverage Path Planning (CPP) is the task of determining a path that passes over all points of an area or volume of interest while avoiding obstacles. This task is integral to many robotic applications, such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image mosaics, demining robots, lawn mowers, automated harvesters, window cleaners and inspection of complex structures, just to name a few. A considerable body of research has addressed the CPP problem. However, no updated surveys on CPP reflecting recent advances in the field have been presented in the past ten years. In this paper, we present a review of the most successful CPP methods, focusing on the achievements made in the past decade. Furthermore, we discuss reported field applications of the described CPP methods. This work aims to become a starting point for researchers who are initiating their endeavors in CPP. Likewise, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works.     

Multiobjective coverage path planning: Enabling automated inspection of complex,real-world structures

An important open problem in robotic planning is the autonomous generation of 3D inspection paths – that is, planning the best path to move a robot along in order to inspect a target structure. We recently suggested a new method for planning paths allowing the inspection of complex 3D structures, given a triangular mesh model of the structure. The method differs from previous approaches in its emphasis on generating and considering also plans that result in imperfect coverage of the inspection target. In many practical tasks, one would accept imperfections in coverage if this results in a substantially more energy efficient inspection path. The key idea is using a multiobjective evolutionary algorithm to optimize the energy usage and coverage of inspection plans simultaneously – and the result is a set of plans exploring the different ways to balance the two objectives. We here test our method on a set of inspection targets with large variation in size and complexity, and compare its performance with two state-of-the-art methods for complete coverage path planning. The results strengthen our confidence in the ability of our method to generate good inspection plans for different types of targets. The method's advantage is most clearly seen for real-world inspection targets, since traditional complete coverage methods have no good way of generating plans for structures with hidden parts. Multiobjective evolution, by optimizing energy usage and coverage together, ensures a good balance between the two – both when 100% coverage is feasible, and when large parts of the object are hidden.     

面向平均区域覆盖的多机器人分布式控制EI北大核心CSCD

随着通信技术、传感技术和控制技术的发展,多机器人系统因其良好的鲁棒性,灵活性和可扩展性,在理论研究和工程应用中展现出广阔的前景.区域覆盖是多机器人系统典型应用之一,目前多采用维诺图分割覆盖区域并使用Lloyd算法控制机器人前往维诺图细胞中心.然而传统Lloyd算法存在不平衡问题,即机器人覆盖区域面积大小不一,这降低了多机器人协作效率.针对平均区域覆盖问题,本文提出了一种改进的Lloyd算法,将维诺图中各细胞面积方差引入Lloyd算法,相应地设计了基于梯度下降法的分布式控制器.本文方法降低了维诺图中各细胞面积的方差,改善了Lloyd算法的平衡性,能够实现整个区域面积更为平均的划分与机器人对该区域的覆盖.数值仿真与无人机实物实验均验证了改进算法的有效性.     

移动机器人路径规划算法综述

路径规划算法是实现移动机器人自主导航的关键技术。针对移动机器人路径规划技术进行研究,分析各算法的实现机制与原理,并系统性的总结了主流路径规划算法研究现状。根据移动机器人路径规划算法的特点,将路径规划算法分为：传统规划算法、智能规划算法、基于采样的规划算法。基于以上分类,分述近年来的主要研究成果,重点分析各类算法的优缺点。针对移动机器人路径规划算法研究现状,对其未来研究方向进行展望,为移动机器人路径规划大发展提供一定的思路。     

基于鱼群算法的智能机器人全覆盖路径规划

为保证机器人的行驶轨迹可以全方位地的覆盖地图的全部坐标点,并降低路径重复率,基于鱼群算法设计智能机器人全覆盖路径规划方法。建立智能机器人死区脱困模型,计算栅格地图模型中的目标活性值,获取整体栅格数量,描述地图中栅格状态,得到脱困时的行驶角度差。基于鱼群算法设计全路径覆盖判定方法,描述不同目标鱼个体之间的距离,在三重移动目标坐标系下,获取元素坐标向量,建立每个目标点的求解代价和,计算下一个目标点行驶的最小距离。设计机器人全覆盖路径规划算法,判断当前位置是否为死区,获取路径规划的全局最优解,实现智能机器人的全覆盖路径规划。利用Matlab仿真软件完成智能机器人全覆盖路径规划实验。结果表明,在简单环境下,该路径规划方法覆盖率为100%,重复率为5.23%,路径长度为15.36m；在复杂环境下,该路径规划方法的覆盖率为100%,重复率则为10.24%,路径长度为20.34m。由此证明,该方法具有较好地规划效果较好。     

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。     

Connectivity preserving localized coverage algorithm for area monitoring using wireless sensor networks

Efficient network coverage and connectivity are the requisites for most Wireless Sensor Network (WSN) deployments, particularly those concerned with area monitoring. Due to the resource constraints of the sensor nodes, redundancy of coverage area must be reduced for effective utilization of the available resources. If two nodes have the same coverage area in their active state, and if both the nodes are activated simultaneously, it leads to redundancy in network and wastage of precious sensor resources. In this paper, we address the problem of network coverage and connectivity and propose an efficient solution to maintain coverage, while preserving the connectivity of the network. The proposed solution aims to cover the area of interest (AOI), while minimizing the count of the active sensor nodes. The overlap region of two sensor nodes varies with the distance between the nodes. If the distance between two sensor nodes is maximized, the overall coverage area of these nodes will also be maximized. Also, to preserve the connectivity of the network, each sensor node must be in the communication range of at least one other node. Results of simulation of the proposed solution indicate up to 95% coverage of the area, while consuming very less energy of 9.44 J per unit time in the network, simulated in an area of 2500 m².     

无线传感器网络中基于遗传算法的优化覆盖机制

覆盖作为无线传感器网络应用的一个基本问题．反映了网络监测和实现目标跟踪的质量效果．针对传感器节点的高密度部署情况,研究了工作节点集选取问题．提出两种基于加权遗传算法和基于约束遗传算法的优化覆盖机制．根据生成的适值函数进行遗传算法操作．并计算传感器网络充分覆盖区域所需的近似最优工作节点集．仿真结果表明该算法能快速收敛于最优解．完成工作节点集的优化选取,从而降低网络冗余,延长网络生存时间．     

基于遗传算法的飞行航路规划

飞行航路规划是一个大范围多目标多约束的三维规划问题。遗传算法是一种求解复杂问题的通用方法,该文在遗传算法中加入了飞行航路规划的相关知识来求解问题。首先,根据飞行航路规划中导航点属性复杂的特点,扩充导航点的模型,并在此基础上采用导航点链表形式的自由编码。第二,为加速规划的进程,同时保证充分的随机性和广泛性,初始群体构造采用端点启发初始化方法。第三,适应度函数由惩罚函数和代价函数组合计算,其中惩罚函数对应问题的约束条件,而代价函数对应问题的目标。第四,采用启发式交叉和启发式变异。最后,通过剖面优化操作实现高度维上的调整。仿真结果证明这是适于所研究问题的有效方法。     

Notes on averaging over acyclic digraphs and discrete coverage control

In this paper we study averaging algorithms and coverage control laws in a unified light. First, we characterize the convergence properties of averaging algorithms over acyclic digraphs with fixed and controlled-switching topology. Second, we introduce and study novel discrete coverage control laws, which are useful in practical implementations of coverage strategies. We characterize the close relationship of the novel discrete control laws with continuous coverage control laws and with averaging algorithms over a class of acyclic digraphs, that we term discrete Voronoi graphs. These results provide a unified framework to model a vast class of distributed optimization problems.