一种新的基于RFID的室内移动机器人自定位方法研究

针对室内移动机器人自定位算法定位精度不高、定位误差存在波动的问题,提出了一种RTFL(RFID tag floor based localization)定位算法与RSSI定位算法相结合的室内移动机器人自定位方法。由RTFL定位算法给定机器人位置估算初值和机器人所在的范围,然后通过基于RSSI的机器人自定位系统进行机器人位置的进一步精确定位。求解过程中,通过遗传算法求解极大似然方程组,并且提出了染色体的筛选和剔除策略。仿真实验结果表明：该方法在有效的时间内完成定位,平均定位误差为0.1572m,与传统的改进方法0.33214m的定位误差相比,降低了近一倍。并且新方法受环境影响较小,鲁棒性较好,能够很好的满足室内移动机器人的定位要求。     

基于运动视的移动机器人定位方法

提出了一种新颖的基于运动视的定位方法．该方法从机器人接近的目标上选取两个特征点并根据它们的图像坐标确定机器人运动前后相对于目标的位姿．本文还提出了一种能够更准确地找出特征点图像坐标的搜索算法．试验结果表明该方法有较高的定位精度和较强的鲁棒性．     

基于粒子滤波器的移动机器人自定位方法研究

首先,对粒子滤波器的原理进行了简要阐述。然后详细描述了基于粒子滤波器的移动机器人自定位算法——蒙特卡洛定位算法。在ROS(Robot Operating System)平台上对该算法进行了仿真实验并分析了其性能。最后,对蒙特卡洛粒子滤波定位方法用于移动机器人定位进行了总结。结果表明,MCL(蒙特卡洛)算法是一种精确鲁棒的移动机器人概率定位方法,可对解决移动机器人的定位问题提供有意义的参考。提出的机器人自定位方法为机器人在Robocup竞赛中自主执行各种作业提供定位支持,已在2013年中国机器人大赛获奖。     

一种有效的移动机器人里程计误差建模方法

移动机器人里程计误差建模是研究移动机器人定位问题的基础. 现有的移动机器人里程计误差建模方法多数针对某一种驱动类型移动机器人设计, 运动过程中缺乏对里程计累计误差的实时反馈补偿, 经过长距离运动过程定位精度大幅度降低. 因此本文针对同步驱动和差动驱动轮式移动机器人平台提出了一种通用的里程计误差建模方法. 在假设机器人运动路径近似弧线基础上, 依据里程计误差传播规律推导了非系统误差、系统误差与里程计过程输入之间的近似函数关系, 进而提出一种具有闭环误差实时反馈补偿功能的移动机器人定位算法, 对定位过程中产生的里程计累计误差给予实时反馈补偿. 实验表明新算法有效地减少了里程计累计误差, 提高了定位精度.     

Mobile robot localization based on PSO estimator

Localization is fundamental to autonomous operation of the mobile robot. A particle filter (PF) is widely used in mobile robot localization. However, the robot localization based PF has several limitations, such as sample impoverishment and a degeneracy problem, which reduce significantly its performance. Evolutionary algorithms, and more specifically their optimization capabilities, can be used in order to overcome PF based on localization weaknesses. In this paper, mobile robot localization based on a particle swarm optimization (PSO) estimator is proposed. In the proposed method, the robot localization converts dynamic optimization to find the best robot pose estimate, recursively. Unlike the localization based on PF, the resampling step is not required in the proposed method. Moreover, it does not require noise distribution. It searches stochastically along the state space for the best robot pose estimate. The results show that the proposed method is effective in terms of accuracy, consistency, and computational cost compared with localization based on PF and EKF.     

A hybrid harmony search algorithm for the flexible job shop scheduling problem

In this paper, a novel hybrid harmony search (HHS) algorithm based on the integrated approach, is proposed for solving the flexible job shop scheduling problem (FJSP) with the criterion to minimize makespan. First of all, to make the harmony search (HS) algorithm adaptive to the FJSP, the converting techniques are developed to convert the continuous harmony vector to a kind of discrete two-vector code for the FJSP. Secondly, the harmony vector is mapped into a feasible active schedule through effectively decoding the transformed two-vector code, which could largely reduce the search space. Thirdly, a resultful initialization scheme combining heuristic and random strategies is introduced to make the initial harmony memory (HM) occur with certain quality and diversity. Furthermore, a local search procedure is embedded in the HS algorithm to enhance the local exploitation ability, whereas HS is employed to perform exploration by evolving harmony vectors in the HM. To speed up the local search process, the improved neighborhood structure based on common critical operations is presented in detail. Empirical results on various benchmark instances validate the effectiveness and efficiency of our proposed algorithm. Our work also indicates that a well designed HS-based method is a competitive alternative for addressing the FJSP.     

基于混合位姿估计模型的移动机器人三维地图创建方法

针对室内复杂环境下的稠密三维建模问题, 提出一种基于RGB-D 相机的移动机器人同时定位与三维地图创建方法. 该方法利用架设在移动机器人上的RGB-D 相机获取环境信息, 根据点云和纹理加权模型建立结合局部纹理约束的混合位姿估计方法, 确保定位精度的同时减小失败率. 在关键帧选取机制下, 结合视觉闭环检测方法, 运用树结构网络优化(TORO) 算法最小化闭环误差, 实现三维地图的全局一致性优化. 在室内环境下的实验结果验证了所提出算法的有效性和可行性.

     

基于改进UKF算法的移动机器人定位方法研究

针对移动机器人在多传感器融合定位过程中因噪声统计特性未知或不准确引起的定位精度不高问题,提出了一种基于Sage-Husa滤波改进的无损卡尔曼滤波（UKF）移动机器人定位算法。首先建立了移动机器人定位相关模型;然后根据噪声统计特性时变特点利用Sage-Husa中的噪声估计器,对状态噪声和量测噪声进行自适应地估计,减小扰动噪声给定位解算带来的误差;接着在状态更新时引入收敛因子,加快算法收敛速度;最后将UKF算法和改进的UKF算法应用到实验室移动机器人中进行仿真实验。实验结果表明,所提出的算法对状态扰动具有较强的抵制能力,对机器人定位的准确性与稳定性的提升具有显著效果。     

A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing

The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem.A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.     

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.     

基于改进PSO算法的移动机器人路径规划

针对粒子群优化(PSO)算法收敛速度快但容易陷入局部极值和细菌觅食优化(BFO)算法全局搜索能力强但效率低的问题,提出了一种将BFO算法的趋化、迁徙和复制操作引入到粒子群搜索过程的具有全局搜索能力和快速收敛的混合算法.在BFO算法和PSO算法的原理、操作步骤基础上,分别使用了PSO算法、BFO法和混合算法对移动机器人进行全局路径规划仿真试验,并分别给出了各算法的迭代次数、适应值曲线.仿真结果表明:与PSO算法和BFO算法相比,所提出的混合算法具有搜索时间短、迭代次数少的优点,较好验证了混合算法在移动机器人路径规划方面的可行性和有效性.     

可移动机器人的马尔可夫自定位算法研究

马尔可夫定位算法是利用机器人运动环境中的概率密度分布进行定位的方法.使用该 方法机器人可在完全不知道自己位置的情况下通过传感器数据和运动模型来估计自己的位置. 但是,在研究中发现它还存在一些问题,如概率减小到零后就无法恢复.对只有距离传感器的机 器人在对称的环境中仅仅采用该算法就无法确定位置.为了解决这些问题,文中给出了修正算 法,并建议在机器人上装上方向仪(如指南针或陀螺仪等),然后利用定义的一个角度高斯分布 函数来构造新的机器人感知模型.在此基础上详细地阐述了一种新的自定位技术.最后,采用仿 真程序验证了机器人在对称环境中运动时这一新算法的可行性.     

模糊PID控制下移动机器人定位方法研究

机器人定位研究一直是机器人学研究的重点,但目前机器人定位方法都存在缺点,抗干扰能力差,不能做到准确定位,主要是由于环境等多方面因素的干扰,定位误差会逐渐加大;由于上述原因,提出了一种基于设定值加权模糊PID控制的移动机器人自定位方法;给出了定位过程的参数,为机器人移动建立模型,设计一种模糊 PID 控制器,根据误差及变化率大小,选择模糊定位或PID定位,实现移动机器人的智能定位,提高机器人定位准确的准确性;通过仿真实验结果证明:模糊PID控制的机器人自定位方法对移动机器人的定位过程有较好的改善作用,实用效果较好。     

Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.     

Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations

Hybridizing of the optimization algorithms provides a scope to improve the searching abilities of the resulting method. The purpose of this paper is to develop a novel hybrid optimization algorithm entitled hybrid robust differential evolution (HRDE) by adding positive properties of the Taguchi's method to the differential evolution algorithm for minimizing the production cost associated with multi-pass turning problems. The proposed optimization approach is applied to two case studies for multi-pass turning operations to illustrate the effectiveness and robustness of the proposed algorithm in machining operations. The results reveal that the proposed hybrid algorithm is more effective than particle swarm optimization algorithm, immune algorithm, hybrid harmony search algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing and Hooke-Jeevespatter search.     

基于多传感器融合的移动机器人定位

针对移动机器人定位系统中单一传感器定位精度低与环境地图的重要性问题, 提出了一种基于多传感器融合的移动机器人定位方法. 首先, 在未知环境下, 分别利用单一里程计, 扩展卡尔曼滤波(extended Kalman filter,EKF)算法融合里程计、惯性测量单元(inertial measurement unit, ...     

基于主动环形闭合约束的移动机器人分层同时定位和地图创建

基于Rao-Blackwellized粒子滤波器提出了一种基于主动闭环策略的移动机器人分层同时定位和地图创建(simultaneous localization and mapping,SLAM)方法,基于信息熵的主动闭环策略同时考虑机器人位姿和地图的不确定性;局部几何特征地图之间的相对关系通过一致性算法估计,并通过环形闭合约束的最小化过程回溯修正.在仅有单目视觉和里程计的基础上,建立了鲁棒的感知模型;通过有效的尺度不变特征变换(scale invariant feature transform,SIFT)方法提取环境特征,基于KD-Tree的最近邻搜索算法实现特征匹配.实际实验表明该方法为实现SLAM提供了一种有效可靠的途径.     

A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem

This paper proposes a hybrid modified global-best harmony search (hmgHS) algorithm for solving the blocking permutation flow shop scheduling problem with the makespan criterion. First of all, the largest position value (LPV) rule is proposed to convert continuous harmony vectors into job permutations. Second, an efficient initialization scheme based on the Nawaz-Enscore-Ham (NEH) heuristic is presented to construct the initial harmony memory with a certain level of quality and diversity. Third, harmony search is employed to evolve harmony vectors in the harmony memory to perform exploration, whereas a local search algorithm based on the insert neighborhood is embedded to enhance the local exploitation ability. Moreover, a new pitch adjustment rule is developed to well inherit good structures from the global-best harmony vector. Computational simulations and comparisons demonstrated the superiority of the proposed hybrid harmony search algorithm in terms of solution quality.