基于粒子滤波的未知环境下机器人同时定位、地图构建与目标跟踪

为了解决机器人在未知环境下的目标跟踪问题,提出了一种基于粒子滤波的机器人同时定位、地图构建与目标跟踪方法．该方法采用Rao-Blackwellized粒子滤波器对机器人位姿状态、标志柱分布和目标位置同时进行估计．该方法中,粒子群的总体分布情况表征机器人位姿状态,而每个粒子均包含2类EKF滤波器,其中一类用来完成对标志柱分布的估计,另一类用来完成对目标状态的估计,粒子的权值则由粒子状态相对于标志柱和目标状态2类相似度共同产生．通过仿真和实体机器人实验验证了该方法的有效性．     

对抗样本三元组约束的度量学习算法

针对已有三元组约束的度量学习算法大多利用先验知识构建约束,一定程度上制约了度量学习算法性能的问题,本文借鉴对抗训练中样本扰动的思想,在原始样本附近学习对抗样本以构造对抗三元组约束,基于对抗三元组和原始三元组约束构建了度量学习模型,提出了对抗样本三元组约束的度量学习算法(metric learning algorithm with adversarial sample triples constraints,ASTCML)。实验结果表明,提出的算法既克服了已有固定约束方法受先验知识影响大的问题,也提高了分类精度,说明区分更加难以区分的三元组约束能够提升算法的性能。     

The Matching Fund Project for Practical Use of Robotic Travel Aid for the Visually Impaired

Destined for the visually impaired, Robotic Travel Aid (RoTA) acts as an intelligent cart, guiding people across the streets. The 60 kg, 1-m tall mobile robot is equipped with a computer vision system, stereo camera sensor and voice interface. When moving, it is aware of its environment: it recognizes landmarks such as zebra-crossing marks or traffic signals, stopping when the light is red, and 'sees' cars or other pedestrians. In case of trouble, the robots communicates wirelessly with a service center, allowing it to give extra information on the trajectory and adapt the navigational information system. Now, a wheelchair robot is developed as the successor of RoTA, not only for the visually impaired, but also the disabled.     

基于实体相似性的知识表示学习方法

知识表示学习旨在将知识图谱中的实体和关系表示成低维稠密实值向量,能有效缓解知识图谱的数据稀疏性和显著提升计算效率。然而,现有大多数知识表示学习方法仅将实体视为三元组的一个组成部分,没有考虑实体自身具有的特质,如实体相似性。为了加强嵌入向量的语义表达,提出基于实体相似性的表示学习方法SimE。该方法首先利用实体的结构邻域度量实体的相似性,再将实体的相似性和拉普拉斯特征映射结合作为基于三元组事实的表示学习方法的约束,形成联合表示。实验结果表明,该方法在链接预测和三元组分类等任务上与目前最好的方法性能接近。     

Dynamic Modeling of a Class of Continuum Manipulators in Fixed Orientation

A novel simultaneous localization and mapping (SLAM) technique based on independent particle filters for landmark mapping and localization for a mobile robot based on a high-frequency (HF)-band radio-frequency identification (RFID) system is proposed in this paper. SLAM is a technique for performing self-localization and map building simultaneously. FastSLAM is a standard landmark-based SLAM method. RFID is a robust identification system with ID tags and readers over wireless communication; further, it is rarely affected by obstacles in the robot area or by lighting conditions. Therefore, RFID is useful for self-localization and mapping for a mobile robot with a reasonable accuracy and sufficient robustness. In this study, multiple HF-band RFID readers are embedded in the bottom of an omnidirectional vehicle, and a large number of tags are installed on the floor. The HF-band RFID tags are used as the landmarks of the environment. We found that FastSLAM is not appropriate for this condition for two reasons. First, the tag detection of the HF-band RFID system does not follow the standard Gaussian distribution, which FastSLAM is supposed to have. Second, FastSLAM does not have a sufficient scalability, which causes its failure to handle a large number of landmarks. Therefore, we propose a novel SLAM method with two independent particle filters to solve these problems. The first particle filter is for self-localization based on Monte Carlo localization. The second particle filter is for landmark mapping. The particle filters are nonparametric so that it can handle the non-Gaussian distribution of the landmark detection. The separation of localization and landmark mapping reduces the computational cost significantly. The proposed method is evaluated in simulated and real environments. The experimental results show that the proposed method has more precise localization and mapping and a lower computational cost than FastSLAM.     

融合实体描述信息和邻居节点特征的知识表示学习方法

知识图谱表示学习旨在将实体和关系映射到一个低维稠密的向量空间中。现有的大多数相关模型更注重于学习三元组的结构特征,忽略了三元组内的实体关系的语义信息特征和三元组外的实体描述信息特征,因此知识表达能力较差。针对以上问题,提出了一种融合多源信息的知识表示学习模型BAGAT。首先,结合知识图谱特征来构造三元组实体目标节点和邻居节点,并使用图注意力网络（GAT）聚合三元组结构的语义信息表示;然后,使用BERT词向量模型对实体描述信息进行嵌入表示;最后,将两种表示方法映射到同一个向量空间中进行联合知识表示学习。实验结果表明,BAGAT性能较其他模型有较大提升,在公共数据集FB15K-237链接预测任务的Hits@1与Hits@10指标上,与翻译模型TransE相比分别提升了25.9个百分点和22.0个百分点,与图神经网络模型KBGAT相比分别提升了1.8个百分点和3.5个百分点。可见,融合实体描述信息和三元组结构语义信息的多源信息表示方法可以获得更强的表示学习能力。     

Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory

We describe a general framework for learning perception-based navigational behaviors in autonomous mobile robots. A hierarchical behavior-based decomposition of the control architecture is used to facilitate efficient modular learning. Lower level reactive behaviors such as collision detection and obstacle avoidance are learned using a stochastic hill-climbing method while higher level goal-directed navigation is achieved using a self-organizing sparse distributed memory. The memory is initially trained by teleoperating the robot on a small number of paths within a given domain of interest. During training, the vectors in the sensory space as well as the motor space are continually adapted using a form of competitive learning to yield basis vectors that efficiently span the sensorimotor space. After training, the robot navigates from arbitrary locations to a desired goal location using motor output vectors computed by a saliency-based weighted averaging scheme. The pervasive problem of perceptual aliasing in finite-order Markovian environments is handled by allowing both current as well as the set of immediately preceding perceptual inputs to predict the motor output vector for the current time instant. We describe experimental and simulation results obtained using a mobile robot equipped with bump sensors, photosensors and infrared receivers, navigating within an enclosed obstacle-ridden arena. The results indicate that the method performs successfully in a number of navigational tasks exhibiting varying degrees of perceptual aliasing.     

Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory

We describe a general framework for learning perception-based navigational behaviors in autonomous mobile robots. A hierarchical behavior-based decomposition of the control architecture is used to facilitate efficient modular learning. Lower level reactive behaviors such as collision detection and obstacle avoidance are learned using a stochastic hill-climbing method while higher level goal-directed navigation is achieved using a self-organizing sparse distributed memory. The memory is initially trained by teleoperating the robot on a small number of paths within a given domain of interest. During training, the vectors in the sensory space as well as the motor space are continually adapted using a form of competitive learning to yield basis vectors that efficiently span the sensorimotor space. After training, the robot navigates from arbitrary locations to a desired goal location using motor output vectors computed by a saliency-based weighted averaging scheme. The pervasive problem of perceptual aliasing in finite-order Markovian environments is handled by allowing both current as well as the set of immediately preceding perceptual inputs to predict the motor output vector for the current time instant. We describe experimental and simulation results obtained using a mobile robot equipped with bump sensors, photosensors and infrared receivers, navigating within an enclosed obstacle-ridden arena. The results indicate that the method performs successfully in a number of navigational tasks exhibiting varying degrees of perceptual aliasing.     

一种无线传感器信号衰减自适应测距模型

使用无线传感器作为路标实现机器人定位具有许多优势,但无线传感器与机器人之间的距离测量存在易受环境干扰的缺点.为了解决这一难题,在对无线传感器射频信号衰减原理分析的基础上,基于在线学习的方法为无线传感器路标建立自适应的信号衰减测距模型.由于模型学习过程是在线进行的,环境因素对无线信号传播衰减的影响被包含在模型中,故此测距模型提高了对无线信号传播环境的适应能力.此外,把路标的身份作为测距模型的输入,从而区分了传感器个体的差异,实验结果证明了这种建模方法在提高无线传感器测距精度方面的有效性.     

基于相似性负采样的知识图谱嵌入

针对现有知识图谱嵌入模型通过从实体集中随机抽取一个实体来生成负例三元组,导致负例三元组质量较低,影响了实体与关系的特征学习能力。研究了影响负例三元组质量的相关因素,提出了基于实体相似性负采样的方法来生成高质量的负例三元组。在相似性负采样方法中,首先使用K-Means聚类算法将所有实体划分为多个组,然后从正例三元组中头实体所在的簇中选择一个实体替换头实体,并以类似的方法替换尾实体。通过将相似性负采样方法与TransE相结合得到TransE-SNS。研究结果表明:TransE-SNS在链路预测和三元组分类任务上取得了显著的进步。     

A new approach to solve SLAM challenges by relative map filter

In this paper we propose a new approach to solve some challenges in the simultaneous localization and mapping (SLAM) problem based on the relative map filter (RMF). This method assumes that the relative distances between the landmarks of relative map are estimated fully independently. This considerably reduces the computational complexity to average number of landmarks observed in each scan. To solve the ambiguity that may happen in finding the absolute locations of robot and landmarks, we have proposed two separate methods, the lowest position error (LPE) and minimum variance position estimator (MVPE). Another challenge in RMF is data association problem where we also propose an algorithm which works by using motion sensors without engaging in their cumulative error. To apply these methods, we switch successively between the absolute and relative positions of landmarks. Having a sufficient number of landmarks in the environment, our algorithm estimates the positions of robot and landmarks without using motion sensors and kinematics of robot. Motion sensors are only used for data association. The empirical studies on the proposed RMF-SLAM algorithm with the LPE or MVPE methods show a better accuracy in localization of robot and landmarks in comparison with the absolute map filter SLAM.     

Hierarchical refinement of skills and skill application for autonomous robots

One of the major goals in designing learning robots is to let these robots develop useful skills over time. These skills are not only related to physical actions of the robot, but also to the coordination of activities, communication with humans, and active sensing. Throughout this paper, the interdependency between these different kinds of skills is analyzed. For the case of elementary action skills and coordination skills, methods for inegration of skill application and refinement are developed. It is shown that this integration has the potential to support long-term learning and autonomous experimentation.     

Intelligent control for the vision-based indoor navigation of an A-life mobile robot

Realizing steady and reliable navigation is a prerequisite for a mobile robot, but this facility is often weakened by an unavoidable slip or some irreparable drift errors of sensors in long-distance navigation. Although perceptual landmarks were solutions to such problems, it is impossible not to miss landmarks occasionally at some specific spots when the robot moves at different speeds, especially at higher speeds. If the landmarks are put at random intervals, or if the illumination conditions are not good, the landmarks will be easier to miss. In order to detect and extract artificial landmarks robustly under multiple illumination conditions, some low-level but robust image processing techniques were implemented. The moving speed and self-location were controlled by the visual servo control method. In cases where a robot suddenly misses some specific landmarks when it is moving, it will find them again in a short time based on its intelligence and the inertia of the previous search motion. These methods were verified by the reliable vision-based indoor navigation of an A-life mobile robot.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

基于接触状态感知发育的机器人柔性装配方法

为了提高工业机器人装配的实时性、自适应性和鲁棒性,借鉴人类后天感知学习方式,提出一种基于接触状态感知发育的柔性装配方法.采用机器人末端的位姿和力/力矩来描述装配接触状态,结合支持向量数据描述和改进极限学习机对接触状态感知发育,形成可自我更新成长的经验知识库,预测机器人的装配动作,完成柔性装配任务.为验证所提出方法的有效性,以小型断路器卡合装配为例进行实验,实验结果表明,采用接触状态感知发育可实现装配经验知识库的自我更新,完成机器人的柔性装配,验证了所提出方法的可行性和有效性.     

Learning Dextrous Manipulation Skills for Multifingered Robot Hands Using the Evolution Strategy

We present a method for autonomous learning of dextrous manipulation skills with multifingered robot hands. We use heuristics derived from observations made on human hands to reduce the degrees of freedom of the task and make learning tractable. Our approach consists of learning and storing a few basic manipulation primitives for a few prototypical objects and then using an associative memory to obtain the required parameters for new objects and/or manipulations. The parameter space of the robot is searched using a modified version of the evolution strategy, which is robust to the noise normally present in real-world complex robotic tasks. Given the difficulty of modeling and simulating accurately the interactions of multiple fingers and an object, and to ensure that the learned skills are applicable in the real world, our system does not rely on simulation; all the experimentation is performed by a physical robot, in this case the 16-degree-of-freedom Utah/MIT hand. Experimental results show that accurate dextrous manipulation skills can be learned by the robot in a short period of time. We also show the application of the learned primitives to perform an assembly task and how the primitives generalize to objects that are different from those used during the learning phase.     

Learning Dextrous Manipulation Skills for Multifingered Robot Hands Using the Evolution Strategy

We present a method for autonomous learning of dextrous manipulation skills with multifingered robot hands. We use heuristics derived from observations made on human hands to reduce the degrees of freedom of the task and make learning tractable. Our approach consists of learning and storing a few basic manipulation primitives for a few prototypical objects and then using an associative memory to obtain the required parameters for new objects and/or manipulations. The parameter space of the robot is searched using a modified version of the evolution strategy, which is robust to the noise normally present in real-world complex robotic tasks. Given the difficulty of modeling and simulating accurately the interactions of multiple fingers and an object, and to ensure that the learned skills are applicable in the real world, our system does not rely on simulation; all the experimentation is performed by a physical robot, in this case the 16-degree-of-freedom Utah/MIT hand. E xperimental results show that accurate dextrous manipulation skills can be learned by the robot in a short period of time. We also show the application of the learned primitives to perform an assembly task and how the primitives generalize to objects that are different from those used during the learning phase.     

Self-calibrating smooth pursuit through active efficient coding

This paper presents a model for the autonomous learning of smooth pursuit eye movements based on an efficient coding criterion for active perception. This model accounts for the joint development of visual encoding and eye control. Sparse coding models encode the incoming data at two different spatial resolutions and capture the statistics of the input in spatio-temporal basis functions. A reinforcement learner controls eye velocity so as to maximize a reward signal based on the efficiency of the encoding. We consider the embodiment of the approach in the iCub simulator and real robot. Motion perception and smooth pursuit control are not explicitly expressed as tasks for the robot to achieve but emerge as the result of the system’s active attempt to efficiently encode its sensory inputs. Experiments demonstrate that the proposed approach is self-calibrating and robust to strong perturbations of the perception–action link.     

Robot navigation in the real world:: Experiments with Manchester’s FortyTwo in unmodified, large environments

Mobile robot navigation under controlled laboratory conditions is, by now, state of the art and reliably achievable. To transfer navigation mechanisms used in such small-scale environments to applications in untreated, large environments, however, is not trivial, and typically requires modifications to the original navigation mechanism: scaling up is hard.In this paper, we discuss the difficulties of mobile robot navigation in general, the various options to achieve navigation in large environments, and experiments with Manchester’s FortyTwo, which investigate how scaling up of navigational competencies can be achieved. We were particularly interested in autonomous mobile robot navigation in unmodified, large and varied environments, without the aid of pre-installed maps or supplied CAD models of the environment. This paper presents a general approach to achieve this.FortyTwo regularly travels the corridors of the Department of Computer Science at Manchester University, using topological maps, landmarks, low level “enabling behaviours” and active exploitation of features of the environment. Experimental results obtained in these environments are given in this paper.     

Model-based learning for mobile robot navigation from the dynamicalsystems perspective

This paper discusses how a behavior-based robot can construct a "symbolic process" that accounts for its deliberative thinking processes using models of the environment. The paper focuses on two essential problems; one is the symbol grounding problem and the other is how the internal symbolic processes can be situated with respect to the behavioral contexts. We investigate these problems by applying a dynamical system's approach to the robot navigation learning problem. Our formulation, based on a forward modeling scheme using recurrent neural learning, shows that the robot is capable of learning grammatical structure hidden in the geometry of the workspace from the local sensory inputs through its navigational experiences. Furthermore, the robot is capable of generating diverse action plans to reach an arbitrary goal using the acquired forward model which incorporates chaotic dynamics. The essential claim is that the internal symbolic process, being embedded in the attractor, is grounded since it is self-organized solely through interaction with the physical world. It is also shown that structural stability arises in the interaction between the neural dynamics and the environmental dynamics, which accounts for the situatedness of the internal symbolic process, The experimental results using a mobile robot, equipped with a local sensor consisting of a laser range finder, verify our claims.     

基于陆标动态配置的移动机器人主动探索

针对无预置陆标的环境, 研究移动机器人动态在线配置陆标问题及基于此的主动探索. 首先, 提出陆标动态在线配置准则, 并分析陆标配置对机器人定位与建图的影响; 然后基于扩展的卡尔曼滤波器, 将机器人的主动探索转化为多目标最优控制问题, 优化目标包含3个部分, 分别对应定位与建图的准确性、机器人预期探索的新区域大小和陆标配置对定位与建图的影响, 机器人选取最优化目标函数的控制输入以实现准确的定位、建图和对环境的充分探索; 最后对陆标进行有效的增补和去冗余. 仿真结果表明该方法的有效性.