基于支持向量规则的运动控制器自然语言构造方法

本文介绍了一种基于支持向量规则的运动控制器自然语言构造方法，提出利用 支持向量机理论，对通过自然语言构造的模糊控制规则进行支持向量规则抽取，从而获得一 个在指定控制精度下的支持向量规则运动控制器．这种方法可以在给定任务精度下抽取真正 有效的控制规则完成控制任务，使控制规则数及控制器形式得到简化，为未来将基于语言构 造的控制器推向实用奠定了基础．所提控制方法在一个轮式移动机器人系统上进行了语言训 练实验．     

采用自然语言的移动机器人任务编程

本文在机器人具备基本运动技能的基础上［1］，采用基于指令教导的学习方法．通 过自然语言教会机器人完成抽象化任务，并以程序体方式保存所学知识，也即通过自然语言 对话自动生成程序流．通过让机器人完成导航等任务，验证所提自然语言编程方法的可行性 ．     

移动机器人路径跟踪的智能预瞄控制方法研究

本文首先介绍了移动机器人的基本硬件组成，然后模仿人工预瞄驾驶行为，提出了一 种移动机器人路径跟踪的智能预瞄控制方法，并介绍了智能预瞄控制器的原理、结构及其设 计过程．试验表明：本文提出的控制方法可保证机器人准确地沿各种参考路径行走，且具有 良好的鲁棒性．具有运动避障功能的移动机器人控制系统正在研究过程中．     

一种新的双环结构机器人无标定自抗扰视觉伺服控制方法

在研究基于自抗扰控制器的机器人无标定视觉伺服方法的基础上，提出了一种新的双环结构机器人无标定自抗扰视觉伺服控制方法．内环采用Kalman滤波算法进行图像雅可比矩阵的在线辨识，可较好地逼近真实模型；外环采用自抗扰控制器，利用非线性观测器实时估计系统相对于当前估计模型的总扰动，并在控制中加以动态补偿．针对六自由度工业机器人进行了二维运动目标的跟踪实验，实验结果表明了该方法的可行性和有效性．     

基于FBFN 的鲁棒仿生学习系统设计及在运动平衡控制中的应用

针对两轮直立式机器人的运动平衡控制问题,结合OCPA 仿生学习系统,基于模糊基函数,设计了一 种鲁棒仿生学习控制方案．它不需要动力学系统的先验知识,也不需要离线的学习阶段．鲁棒仿生学习控制器主要 包括仿生学习单元、增益控制单元和鲁棒自适应单元3 部分．仿生学习单元由模糊基函数网络（FBFN）实现,FBFN 不仅执行操作行为产生功能,逼近动力学系统的非线性部分,同时也执行操作行为评价功能,并利用性能测量机制 提供的误差测量信号,产生取向值信息,对操作行为产生网络进行调整．增益控制单元的作用是确保系统的稳定性 和性能,鲁棒自适应单元的作用是消除FBFN 的逼近误差及外部干扰．此外,由于FBFN 的参数是基于李亚普诺夫 稳定性理论在线调整的,因此进一步确保了系统的稳定性和学习的快速性．理论上证明了鲁棒仿生学习控制器的稳 定性,仿真实验结果验证了其可行性和有效性．     

基于分层学习的四足机器人运动自适应控制模型

针对四足机器人面对腿部损伤无法继续有效自主运作的问题,提出一种基于分层学习的自适应控制模型。该模型结构由上层状态策略控制器(SDC)和下层基础运动控制器(BDC)组成。SDC对机器人腿部及姿态进行决策并选择运动子策略,BDC子运动策略表达该状态下机器人的运动行为。在Unity3D中构建反关节多自由度的四足机器人,训练多种腿部受损状况的BDC子运动策略,BDC成熟后20s周期随机腿部受损并训练SDC。该模型控制流程为SDC监测机器人状态,激活BDC策略,BDC输出期望关节角度,最后由PD控制器进行速度控制。其实现机器人在腿部受损后自我适应继续保持运作。仿真与实验结果表明,该控制模型能在机器人损伤后能自我快速、稳定调整运动策略,并保证运动的连贯性及柔和性。     

基于DSP的工业机器人控制器的设计与实现

提出了一种基于DSP技术的工业机器人控制器的设计，该控制器采用一台工业PC机以 及一块DSP多轴运动控制卡，较好地实现了机器人的实时控制，提高了机器人控制器的运动 控制性能，最后给出了相关的实验和结论．     

基于场景匹配的移动机器人避障

研究环境未知情况下移动机器人的避障问题，提出一种基于模糊场景匹配的移动机器人避障方法．该方法对多种传感器的信息进行融合，生成当前环境的场景并与场景库中的场景进行匹配，利用匹配结果并通过模糊控制器得到机器人的运动参数，对机器人的避障进行控制,实验结果表明了该方法的正确性和有效性。     

基于行为的机器人编队控制研究

建立了一种基于行为的机器人编队控制结构模型，该结构采用分层控制策略，全局控制器根据当前所有机器人的状态从一个有限状态机中选择下一步机器人的行为，将协调控制量发送给各机器人，各机器人再通过局部控制器对自身进行控制。该结构模型简单、易行，并且适用于机器人不同任务的需要，具有很高的灵活性，而且易于仿真实现。在此基础上，将一类机器人模型进行反馈线性化，再根据编队控制的要求，利用后推方法设计控制律。仿真结果表明这种结构模型和控制算法是有效的。     

DYNAX系统机器人在工业生产中能仿真与应用

采用DYNAX运动控制器对奇瑞QH-165工业机器人进行运动控制,可以在对机器人进行正逆运动学分析的基础上,借助DYNAX提供的CAM模式,利用VC＋＋6．0语言开发运动控制软件实现机器人的I／O控制、轨迹示教等。     

Extraction of Robot Primitive Control Rules from Natural Language Instructions

A support vector rule based method is investigated for the construction of motion controllers via natural language training. It is a two-phase process including motion control information collection from natural language instructions, and motion information condensation with the aid of support vector machine (SVM) theory. Self-organizing fuzzy neural networks are utilized for the collection of control rules, from which support vector rules are extracted to form a final controller to achieve any given control accuracy. In this way, the number of control rules is reduced, and the structure of the controller tidied, making a controller constructed using natural language training more appropriate in practice, and providing a fundamental rule base for high-level robot behavior control. Simulations and experiments on a wheeled robot are carried out to illustrate the effectiveness of the method.     

Incremental statistical learning for integrating motion primitives and language in humanoid robots

We describe a novel approach that allows humanoid robots to incrementally integrate motion primitives and language expressions, when there are underlying natural language and motion language modules. The natural language module represents sentence structure using word bigrams. The motion language module extracts the relations between motion primitives and the relevant words. Both the natural language module and the motion language module are expressed as probabilistic models and, therefore, they can be integrated so that the robots can both interpret observed motion in the form of sentences and generate the motion corresponding to a sentence command. Incremental learning is needed for a robot that develops these linguistic skills autonomously . The algorithm is derived from optimization of the natural language and motion language modules under constraints on their probabilistic variables such that the association between motion primitive and sentence in incrementally added training pairs is strengthened. A test based on interpreting observed motion in the forms of sentence demonstrates the validity of the incremental statistical learning algorithm.     

Mobile robot programming using natural language

How will naive users program domestic robots? This paper describes the design of a practical system that uses natural language to teach a vision-based robot how to navigate in a miniature town. To enable unconstrained speech the robot is provided with a set of primitive procedures derived from a corpus of route instructions. When the user refers to a route that is not known to the robot, the system will learn it by combining primitives as instructed by the user. This paper describes the components of the Instruction-Based Learning architecture and discusses issues of knowledge representation, the selection of primitives and the conversion of natural language into robot-understandable procedures.     

An Informal Semantic Analysis of Motion Verbs Based on Physical Primitives

A representation scheme for verbs and prepositions specifying path and locative information is developed. The representation emphasizes the implementability of the underlying semantic primitives. The primitives pertain to mechanical characteristics such as geometric relationships among objects, force or motion characteristics implied by verbs, and their prepositional modifiers. This representation has been used to animate the performance of tasks underlying natural language imperatives.     

Incremental kinesthetic teaching of motion primitives using the motion refinement tube

We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method starts with observational learning and applies iterative kinesthetic motion refinement using a forgetting factor. Kinesthetic teaching is supported by introducing the motion refinement tube, which represents an area of allowed motion refinement around the nominal trajectory. On the realtime control level, the kinesthetic teaching is handled by a customized impedance controller, which combines tracking performance with compliant physical interaction and allows to implement soft boundaries for the motion refinement. A novel method for continuous generation of motions from a hidden Markov model (HMM) representation of motion primitives is proposed, which incorporates time information for each state. The proposed methods were implemented and tested using DLR??s humanoid upper-body robot Justin.     

Adaptive Robot Biped Locomotion with Dynamic Motion Primitives and Coupled Phase Oscillators

In order to properly function in real-world environments, the gait of a humanoid robot must be able to adapt to new situations as well as to deal with unexpected perturbations. A promising research direction is the modular generation of movements that results from the combination of a set of basic primitives. In this paper, we present a robot control framework that provides adaptive biped locomotion by combining the modulation of dynamic movement primitives (DMPs) with rhythm and phase coordination. The first objective is to explore the use of rhythmic movement primitives for generating biped locomotion from human demonstrations. The second objective is to evaluate how the proposed framework can be used to generalize and adapt the human demonstrations by adjusting a few open control parameters of the learned model. This paper contributes with a particular view into the problem of adaptive locomotion by addressing three aspects that, in the specific context of biped robots, have not received much attention. First, the demonstrations examples are extracted from human gaits in which the human stance foot will be constrained to remain in flat contact with the ground, forcing the “bent-knee” at all times in contrast with the typical straight-legged style. Second, this paper addresses the important concept of generalization from a single demonstration. Third, a clear departure is assumed from the classical control that forces the robot’s motion to follow a predefined fixed timing into a more event-based controller. The applicability of the proposed control architecture is demonstrated by numerical simulations, focusing on the adaptation of the robot’s gait pattern to irregularities on the ground surface, stepping over obstacles and, at the same time, on the tolerance to external disturbances.     

Component-based robot system design for grasping tasks

The paper presents a robot system design with highly reusable components for a component-based robot system for manipulation tasks. The robot system is designed based on the analysis of manipulation tasks using a unified modeling language use case diagram. For a service robot with locomotion and manipulation mechanisms, reusability of robot system components is improved by adopting the proposed design. Our structure consists of scenario, task, robot information management server, data analyzer, sensor hardware controller, skill, and motion hardware controller on a component-based robot system. Based on the proposed robot system, we implemented a component-based robot system and subsequently realized a grasping motion by a service robot.