人工情感的进化控制系统实现

人工情感在机器人的研究中至关重要,简要概括了当前人工情感的应用.在借鉴情感学习控制的理论的基础上,融入了进化控制的思想,设计出了一种基于人工情感的控制体系结构,在此结构中包含有基于遗传算法的进化控制系统、神经和人工情感控制系统.机器人通过神经系统接受环境信息并进行行为决策,行为决策的效果通过情感学习模型进行反馈.情感学习模型根据机器人的内、外环境状态,产生情感因子（即生物激素）,再由情感因子来调节神经系统的记忆和行为决策,最后神经系统的记忆与行为模块又由进化系统得以继承.该控制结构加强了机器人在动态环境中的学习和自适应能力.仿真实验验证了该控制结构的有效性,仿真结果也表明机器人具有很强的学习和自适应能力.     

基于人工情感控制系统的设计与实现

人工情感在机器人的研究中至关重要,文中简要概括了当前人工情感的应用;我们在借鉴生物系统控制理论的基础上,融入了进化控制的思想,设计了一种基于人工情感的控制体系结构,在此结构中包含有基于蚁群算法的进化控制系统、神经和人工情感控制系统;机器人通过神经系统接受环境信息并进行行为决策,行为决策的效果通过情感学习模型进行反馈;情感学习模型根据机器人的内、外环境状态,产生情感因子(即生物激素),再由情感因子来调节神经系统的记忆和行为决策,最后神经系统的记忆与行为模块又由进化系统得以继承;该控制结构加强了机器人在动态环境中的学习和自适应能力;为了验证该控制结构的有效性,文章做了仿真实验;仿真结果也表明机器人具有很强的学习和自适应能力.     

基于注意度评价的人工意识模型

情感机器人的研究已经成为普遍关注的焦点.但是,建立一个普适的情感模型,使机器人具有情感逻辑系统,并能真正进行情感思维,还面临着许多理论和技术上的困难.基于现有的情感模型,对意识心理学理论进行了深入的研究,并根据Maslow的需求层次理论,构建了一个具有外在刺激的、基于注意度评价的人工意识模型.实验表明,该模型为机器人情感模型的建立提供了一种新的研究方法.     

情感建模研究进展

情感建模在和谐人机交互及情感机器人方向有着广泛应用,是情感计算及人工心理理论研究的主要内容．本文对情感建模研究现状及其相关应用作了较为详细的分析与综述,指出了情感建模目前所面临的问题及难点．     

基于内分泌调节机制的机器人行为规划算法及其应用研究

借鉴内分泌系统对神经系统与遗传系统的高层调节机制，提出了一种新的基于内分泌调节机制的机器人行为规划算法．此算法中机器人通过神经系统接受环境信息并进行行为决策，行为决策的效果通过一种情感学习模型进行反馈．情感学习模型根据机器人的内、外环境状态,产生情感因子(即生物激素)，再由情感因子来调节神经系统的记忆和行为决策，最后神经系统的记忆与行为模式又由遗传系统得以继承．该算法有效避免了神经系统复杂的自学习过程。同时也保证机器人有较强的自适应能力．为了验证算法的有效性，本文做了机器人足球队守门员训练的仿真实验，结果也表明该算法具有很强的自适应学习能力．     

基于人工心理的机器人平台的研究与实现

本文提出并实现了一种基于人工心理的机器人平台。平台采用上下位机结构,上位机采用嵌入式PC,利用语音合成与识别技术、图像识别与处理技术实现多通道人机交互,利用人工智能和人工心理理论建立数学模型,实现机器人的智能化和情感化交互;下位机采用PIC单片机系统实现机器人的行为控制。实验结果表明,机器人可以与人实现和谐的情感智能交互。     

基于个性和OCC的机器人情感建模研究

机器人不仅要具有简单的机械作业和逻辑推理能力，还应当具有类似人类的情感能力．本文将个性与情绪、情感、理解、表达相结合，采用OCC模型作为评价标准，建立了符合人类情感规律的、可用于情感机器人的情感模型。通过一个应用上述模型的虚拟人情感交互系统．验证了此模型可以很好的对人类的情感进行仿真．可以应用于情感机器人和人性化计算机、游戏等许多领域。     

一种基于任务的机器人情感决策模型的构建

为了使人机交流更自然,提出一种基于任务的机器人情感决策模型,建立从多种感知输入到多种行为 输出的映射．机器人的情感状态采用PAD（pleasure-arousal-dominance）情感空间描述,并考虑了个性因素．结合正 态分布原型,采用概率与情感结合的行为映射方式,使决策模型的决策变量受随机因素和情感因素影响,保证其输 出行为更符合人类的情感变化．以“福娃”机器人为平台验证了该方法的正确性和实用性．     

人机交互中的个性化情感模型

人与机器人的交互过程中,情感因素的引入能够使人机交流更加自然和谐.因此,完整的人工情感模型的建立是首要解决的问题.基于情感能量理论基础,首先,提出了心境自发转移和刺激转移模型.其次,结合情绪自发转移的马尔可夫链模型和刺激转移的HMM模型,将心境和情绪的自发和刺激转移过程统一在一个框架下.最后,将完整的人工情感模型软件化并应用于儿童玩伴机器人上,在接受非结构化环境与用户的信息输入后,个性化的情感软件模块产生输出,实现针对儿童用户的玩伴机器人个性化交互,通过应用验证了该模型的有效性.     

机器人情感交互模型研究

为了实现机器人与人的和谐交互,该文给出了基于Multi-agent的情感机器人结构模型,提出了机器人情感交互模型构建方法,介绍了基于灰色系统的情感模型和情感关联模型,构造了机器人学习模型,实现了情感机器人交互系统,结果表明机器人能够和人进行有智能和情感的交互。     

基于情感与环境认知的移动机器人自主导航控制

将基于情感和认知的学习与决策模型引入到基于行为的移动机器人控制体系中, 设计了一种新的自主导航控制系统. 将动力学系统方法用于基本行为设计, 并利用ART2神经网络实现对连续的环境感知状态的分类, 将分类结果作为学习与决策算法中的环境认知状态. 通过在线情感和环境认知学习, 形成合理的行为协调机制. 仿真表明, 情感和环境认知能明显地改善学习和决策过程效率, 提高基于行为的移动机器人在未知环境中的自主导航能力     

Emotions and temperament of robots: Behavioral aspects

An approach to the control of robots behavior based on the emotion and temperament mechanism is proposed. It is shown that these psychological features can be simulated fairly simply. The proposed emotion-based architecture of the robot control system leans upon the Simonov informational theory of emotions, while the specific features of temperament are reduced to a two-parameter model of the excitation-inhibition type. Experiments performed with mobile robots are described. These experiments demonstrate a set of various types of robots’ behavior: melancholic, choleric, sanguine, and phlegmatic. All these types were implemented using the so-called temperament controller, which determines a balance between the excitation and inhibition parameters of the robot control system. An FSM-based model of temperament is also proposed that makes it possible to describe the behavior of an individual. Using this model, it is shown that, for performing certain collective behavior tasks, it is useful to have in the group individuals with different behavior so that this behavior also depends on the individual emotions and temperament of robots.     

Behavioral task processing for cognitive robots using artificial emotions

This paper presents an artificial emotional-cognitive system-based autonomous robot control architecture for a four-wheel driven and four-wheel steered mobile robot. Discrete stochastic state-space mathematical model is considered for behavioral and emotional transition processes of the autonomous mobile robot in the dynamic realistic environment. The term of cognitive mechanism system which is composed from rule base and reinforcement self-learning algorithm explain all of the deliberative events such as learning, reasoning and memory (rule spaces) of the autonomous mobile robot. The artificial cognitive model of autonomous robot control architecture has a dynamic associative memory including behavioral transition rules which are able to be learned for achieving multi-objective robot tasks. Motivation module of architecture has been considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors for long-term action planning. Also reinforcement self-learning and reasoning ability of artificial cognitive model and motivational gain effects of proposed architecture can be observed on the executing behavioral sequences during simulation. The posture and speed of the robot and the configurations, speeds and torques of the wheels and all deliberative and cognitive events can be observed from the simulation plant and virtual reality viewer. This study constitutes basis for the multi-goal robot tasks and artificial emotions and cognitive mechanism-based behavior generation experiments on a real mobile robot.     

Improvement of group performance of job distributed mobile robots by an emotionally biased control system

This paper deals with the implementation of emotions in mobile robots performing a specified task in a group in order to develop intelligent behavior and easier forms of communication. The overall group performance depends on the individual performance, group communication, and the synchronization of cooperation. With their emotional capability, each robot can distinguish the changed environment, can understand a colleague robot’s state, and can adapt and react with a changed world. The adaptive behavior of a robot is derived from the dominating emotion in an intelligent manner. In our control architecture, emotion plays a role to select the control precedence among alternatives such as behavior modes, cooperation plans, and goals. Emotional interaction happens among the robots, and a robot is biased by the emotional state of a colleague robot in performing a task. Here, emotional control is used for a better understanding of the colleague’s internal state, for faster communication, and for better performance eliminating dead time. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

高精度激光测距下的机器人自主避障控制

在机器人自主避障过程中,由于传感器数据的误差会降低机器人感知和决策的准确性,从而影响机器人自主避障能力。为此,提出高精度激光测距下的机器人自主避障控制方法。通过设计机器人体系结构,建立机器人运动学模型,为机器人避障控制提供依据。采用高精度激光测距技术,构建机器人移动场地地形。通过自适应阈值方法,完成机器人的自主避障控制。实验结果表明,所提方法的机器人自主避障控制效果好,且障碍物位置测试值与实际位置值的误差保持在0.5m以内,具有较高的避障控制精确度。     

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。     

Control circuits simplification and computer programs design on bipedal robot

This study is expected to provide the best design of bipedal robot, which will help widen the application in the future. A new design system is presented through simplification of the control circuit, and the design of computer programs not only lowers the research barriers of the robots but also decreases the development costs. This design system can be used for various operations of manufacturing processes to make up the shortages of the flexibility for the robots. In recent years, the progress in electronics and the control technology has made the robots useful not only for dangerous and automatic tasks, but also for advanced and friendly people service. Thus, the robots have been used in the factories for automation and towards the general use in regular life. Of all robots, the bipedal robot attracts the most attention for its humanoid outlook, user-friendly design, and artificial intelligence for the human society. Many bipedal robots are developed to satisfy consumers’ needs. The control circuits and the program design are the key issues to make the bipedal robots. In this study, a synchronous robot controller for 31°-axis freedom is developed. The authors also equip a memory in the hardware architecture to store all moving commands of the bipedal robot. In terms of internal programs, the authors develop a human interface that synchronizes the movement of the robot and collects the pace data of the robot. The authors use the statistical method to analyze the data and establish a database of the robot's movement. With the database, one can finally drive the robot to walk and generate the pace design of the bipedal robots.     

养老服务机器人的技术发展趋势

随着人工智能,无线网络,云计算的发展,服务机器人已经进入快速发展阶段。我国已经进入老龄化社会,服务机器人成为缓解社会压力,推动科技发展的关键技术和社会热点。它给出了基于云计算的服务机器人基本概念、体系结构及关键技术,重点阐述了人机交互技术在机器人拟“人”化的实现过程,分析了养老服务机器人未来发展社会、政策及技术方面的支持与保障,总结了制约云机器人发展的障碍和改善方法。     

Biomimetic control architecture for robotic cooperative tasks

This article proposes some control algorithms to be applied to the MIROSOT robot league architecture. The MIROSOT league soccer game concept is fairly simple: two teams of robots, with 3–5 robots per side, play football autonomously. The ball that the teams play with is an orange golf ball. Above the pitch is a machine vision camera running at 60 frames per second. This camera is linked to a server, which calculates the positions and velocities of each of the robots and the ball, and then determines what each robot should be doing. These instructions are then communicated to the robots over wireless links. In order to develop an efficient control strategy and architecture, the robots have to use strategies from the real human soccer game. Using the software Simi Scout, a suitable analysis of tactics can be extracted from the games. After analyzing the soccer game, a number of attributes are specified and then embedded at different levels. The specified attributes are interconnected, and the analysis of the game is processed for optimization. Using this information, the robot program is adapted and experimental tests/games are played. We comment on the results, and propose an improved control architecture based on practical results.