Hybrid adaptive control for robot manipulators

A hybrid adaptive control scheme is proposed for robot manipulators. Unmodelled dynamics have been considered in the robot model. The standard RLS algorithm has been modified to take into account these unmodelled dynamics. Global stability of the system is ensured.     

混合粒子群和差分进化的定位算法

针对由测量误差造成的无线传感器网络定位精度不高的问题,提出一种混合粒子群和差分进化的节点定位算法（HPSO-DE）。首先,对粒子群算法的惯性权重进行自适应更新,使得每个个体随着迭代次数的增加而增大,进而提高其全局探索能力,然后改进差分进化算法的变异策略,从而提高该算法的局部寻优能力,之后将个体先经过改进的粒子群算法优化,低于平均适应度值的个体继续通过改进的差分进化算法优化,从而得到HPSO-DE算法。HPSO-DE算法继承了二者的优点,提高了该算法的最优解精度和收敛速度。最后在无线传感器网络节点定位模型中应用HPSO-DE算法,仿真结果表明,所提HPSO-DE算法在测距误差为30%时,定位误差比PSO和DFOA分别少2.1m和1.1m,具有更高的定位精度和更强的抗误差性能。     

Evolutionary filter for robust mobile robot global localization

Mobile robot global localization aims to determine the robot’s pose in a known environment in the absence of the robot’s initial pose information. This article presents an evolutive localization algorithm known as Evolutive Localization Filter (ELF). Based on evolutionary computation concepts, the proposed algorithm searches stochastically along the state space for the best robot’s pose estimate. The set of pose solutions (the population) represents the most likely areas according to the perception and motion information received. The population evolves by using the observation and motion error derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model. The resulting global localization module has been integrated successfully in a mobile robot equipped with a laser range finder. Experiments demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.     

A novel efficient algorithm for mobile robot localization

Being autonomous is one of the most important goals in mobile robots. One of the fundamental works to achieve this goal is giving the ability to a robot for finding its own correct position and orientation. Different methods have been introduced to solve this problem. In this paper, a novel method based on the harmony search (HS) algorithm for robot localization through scan matching is proposed. Simulation results show that the proposed method in comparison with a genetic algorithm-based approach has better accuracy and higher performance. Furthermore a new hybrid algorithm based on harmony search and differential evolution (DE) algorithms is proposed and evaluated on different benchmark functions. Finally the hybrid algorithm has been applied for mobile robot localization and it outperformed the HS-based approach.     

移动节点的环境自适应定位模型

针对现有基于对数正态信道模型的定位方法在各向异性环境下定位精度不理想的问题,提出一种具有环境自适应能力的移动节点定位模型,在传统信道模型中引入障碍物建模参数,通过新的模型表达解决无线网络各向异性环境中的节点定位问题。实验结果表明,该模型有良好的定位能力,对环境中的障碍物和其它干扰具有较低的敏感度。     

Evolutionary computing based mobile robot localization

Evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localization are that the system is partially dynamic and information for fitness evaluation is incomplete and corrupted by noise. In this research, variations to the above three evolutionary computing techniques are proposed to tackle the specific dynamic and noisy system. Their performances are compared based on simulation and experiment results and the feasibility of the proposed approach to mobile robot localization is demonstrated.     

A bacterial colony growth algorithm for mobile robot localization

Achieving robot autonomy is a fundamental objective in Mobile Robotics. However in order to realize this goal, a robot must be aware of its location within an environment. Therefore, the localization problem (i.e.,the problem of determining robot pose relative to a map of its environment) must be addressed. This paper proposes a new biology-inspired approach to this problem. It takes advantage of models of species reproduction to provide a suitable framework for maintaining the multi-hypothesis. In addition, various strategies to track robot pose are proposed and investigated through statistical comparisons. The Bacterial Colony Growth Algorithm (BCGA) provides two different levels of modeling: a background level that carries on the multi-hypothesis and a foreground level that identifies the best hypotheses according to an exchangeable strategy. Experiments, carried out on the robot ATRV-Jr manufactured by iRobot, show the effectiveness of the proposed BCGA.

Absolute localization for a mobile robot using place cells

This paper describes a method for absolute localization and environment recognition for an autonomous, sonar-equipped robot. The addition of an auto-associative memory to previously developed non-neural map making software results in a system that is capable of recognizing its environment and its position within the environment using remembered features and room geometry. In the prior system the robot used sonar to construct a metric map of an environment, but the map information had to be reconstructed each time the robot returned to an environment. We evaluated the system with a task that requires memory of the position of a goal that is not directly detectable by sonar.     

一种基于图像匹配的移动机器人定位方法

为增强移动机器人在非结构化动态环境下的定位能力,提出了一种基于图像相似度匹配的单目视觉粒子定位方法。在提取具有平移、旋转、缩放不变性的视觉特征基础上,引入相关核函数来提高特征对环境噪声和光照变化的适应性。利用以上局部特征,计算当前图像和参考图像的相似度作为粒子的权重,通过参考图像的可视区域更新粒子的后验概率分布。实验结果表明,该方法在不易提取几何特征的非结构化动态环境中能够实现可靠、高效的定位。     

Simultaneous localization and odometry self calibration for mobile robot

This paper presents both the theory and the experimental results of a method allowing simultaneous robot localization and odometry error estimation (both systematic and non-systematic) during the navigation. The estimation of the systematic components is carried out through an augmented Kalman filter, which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. In this first filter, the non-systematic error is defined as constant and it is overestimated. Then, the estimation of the real non-systematic component is carried out through another Kalman filter, where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter. There, the systematic parameters in the model are regularly updated with the values estimated by the first filter. The approach is theoretically developed for both the synchronous and the differential drive. A first validation is performed through very accurate simulations where both the drive systems are considered. Then, a series of experiments are carried out in an indoor environment by using a mobile platform with a differential drive.     

基于环境势场的移动机器人主动定位

提出一种以环境势场提供辅助线索的主动定位方法.首先建立以环境中各标志物为场源的势场模型;然后为机器人构造以窗口势函数极大值位置和环境势函数极大值位置为主体的运动目标候选集合,并以行为效用和行为代价构成评价函数,从中选出目标位置, 进而确定运动方向;最后根据机器人的运动能力和评价函数分别确定下一步的位移和观测方向.仿真结果验证了该方法的有效性.     

Lattice independent component analysis for appearance-based mobile robot localization

This paper introduces an approach to appearance-based mobile robot localization using a new approach to dimensional reduction based on the notion of Lattice Independence called Lattice Independent Component Analysis (LICA). Any algorithm that can select a set of Strong Lattice Independent (SLI) vectors from the data can be applied inside LICA, this paper applies a specific Endmember Induction Algorithm (EIA) developed by our research group. The fact that SLI vectors are Affine Independent allows the coupling of non-linear Lattice Associative Memories (LAM) and linear unmixing for data exploration and dimensionality reduction. To perform an appearance-based mobile robot visual localization, images from the on-board camera robot are transformed into low dimension feature vector representations for classification. For validation, we compare LICA against several Independent Component Analysis (ICA) approaches over a collection of recorded image sequences taken from the robot following some predefined paths. Results show that LICA improves most of the ICA approaches, and it is only slightly improved by the Molgedey and Schouster ICA in some data instances.     

High-speed laser localization for a restaurant service mobile robot

Nowadays, service robots have become very popular. The objective of this work was to develop a restaurant service mobile robot. This service mobile robot can transfer dishes around the restaurant. It can also show customers to an unoccupied table. The efficiency of the restaurant service can be increased with this robot. This service mobile robot is equipped with a “laser positioning system.” The laser positioning system is used for rapid and precise positioning and guidance of the mobile robot.     

混合差异演化算法在背包问题中的应用

提出了一种用于求解0-1背包问题的混合差异演化算法,详细阐述了该算法求解背包问题的具体操作过程。算法主要使用了两个思想策略,即启发式贪婪算法和基于二进制编码的差异演化算法。通过对其它文献中仿真实例的计算和结果对比,表明该算法对求解0-1背包问题的有效性,这对差异演化算法解决其它离散问题会有些帮助。     

混合差分进化算法

为了克服差分进化算法容易出现早熟和收敛速度慢的问题,提出了一种混合差分进化算法.该算法在趋药性差分进化算法(CDE)的基础上,通过对较优个体进行变异操作,维护了种群多样性、避免早熟;通过将较差的个体与较优个体进行杂交,提高了开采能力、加快了收敛速度.基于这两种策略,算法的开采能力与探索能力达到了平衡.用该算法解决标准函数优化问题,并将仿真结果与其他算法进行比较,数值结果表明该文算法具有较快的收敛速度和很强的跳出局部最优的能力.     

移动机器人自适应行为选择的混沌特性分析

针对移动机器人运行环境的复杂非线性特性,提出一种将重构相空间理论应用到移动机器人自适应行为选择中的混沌非线性分析方法。在所采集的一维自适应行为动作选择随机序列的基础上,重构时间序列的相空间,并计算相空间的嵌入维数、混沌吸引子和最大Lyapunov指数。计算结果表明,当运行环境中障碍物的分布密度较大时,移动机器人的自适应行为选择规律呈现混沌特性。这为结合混沌理论,给移动机器人的自适应行为选择提供更精确的控制,提供了理论基础。     

Consistent triangulation for mobile robot localization using discontinuous angular measurements

Localization is a fundamental operation for the navigation of mobile robots. The standard localization algorithms fuse external measurements of the environment with the odometric evolution of the robot pose to obtain its optimal estimation. In this work, we present a different approach to determine the pose using angular measurements discontinuously obtained in time. The presented method is based on an Extended Kalman Filter (EKF) with a state-vector composed of the external angular measurements. This algorithm keeps track of the angles between actual measurements from robot odometric information. This continuous angular estimation allows the consistent use of the triangulation methods to determine the robot pose at any time during its motion. The article reports experimental results that show the localization accuracy obtained by means of the presented approach. These results are compared to the ones obtained applying the EKF algorithm with the standard pose state-vector. For the experiments, an omnidirectional robotic platform with omnidirectional wheels is used.     

Sensor planning for mobile robot localization using Bayesian network inference

We propose a new method of sensor planning for mobile robot localization using Bayesian network inference. Since we can model causal relations between situations of the robot's behavior and sensing events as nodes of a Bayesian network, we can use the inference of the network for dealing with uncertainty in sensor planning and thus derive appropriate sensing actions. In this system we employ a multi-layered-behavior architecture for navigation and localization. This architecture effectively combines mapping of local sensor information and the inference via a Bayesian network for sensor planning. The mobile robot recognizes the local sensor patterns for localization and navigation using a learned regression function. Since the environment may change during the navigation and the sensor capability has limitations in the real world, the mobile robot actively gathers sensor information to construct and reconstruct a Bayesian network, and then derives an appropriate sensing action which maximizes a utility function based on inference of the reconstructed network. The utility function takes into account belief of the localization and the sensing cost. We have conducted some simulation and real robot experiments to validate the sensor planning system.     

Visual graph modeling for scene recognition and mobile robot localization

Image retrieval and categorization may need to consider several types of visual features and spatial information between them (e.g., different point of views of an image). This paper presents a novel approach that exploits an extension of the language modeling approach from information retrieval to the problem of graph-based image retrieval and categorization. Such versatile graph model is needed to represent the multiple points of views of images. A language model is defined on such graphs to handle a fast graph matching. We present the experiments achieved with several instances of the proposed model on two collections of images: one composed of 3,849 touristic images and another composed of 3,633 images captured by a mobile robot. Experimental results show that using visual graph model (VGM) improves the accuracies of the results of the standard language model (LM) and outperforms the Support Vector Machine (SVM) method.     

Optimal sensor scheduling for resource-constrained localization of mobile robot formations

This paper addresses the problem of resource allocation in formations of mobile robots localizing as a group. Each robot receives measurements from various sensors that provide relative (robot-to-robot) and absolute positioning information. Constraints on the sensors' bandwidth, as well as communication and processing requirements, limit the number of measurements that are available or can be processed at each time step. The localization uncertainty of the group, determined by the covariance matrix of the equivalent continuous-time system at steady state, is expressed as a function of the sensor measurements' frequencies. The trace of the weighted covariance matrix is selected as the optimization criterion, under linear constraints on the measuring frequency of each sensor and the cumulative rate of the extended Kalman filter updates. This formulation leads to a convex optimization problem (semidefinite program) whose solution provides the sensing frequencies, for each sensor on every robot, required in order to maximize the positioning accuracy of the group. Simulation and experimental results are presented that demonstrate the applicability of this method and provide insight into the properties of the resource-constrained cooperative localization problem.