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.     

操作条件反射学习自动机及其在机器人平衡控制中的应用

针对两轮机器人的平衡控制问题,在学习自动机理论的框架中,提出一种基于操作条件反射学习自动机的仿生学习模型.该模型引入认知学习单元和取向单元,分别用来实现操作行为学习和指导系统进化的方向.模拟两轮自平衡机器人的平衡控制仿真实验表明,该学习模型具有可行性和有效性,能使机器人自主学会平衡控制技能,并使其具有高度的自适应能力.     

进化操作行为学习模型及在移动机器人避障上的应用

针对移动机器人避障上存在的自适应能力较差的问题,结合遗传算法(GA)的进化思想,以自适应启发评价(AHC)学习和操作条件反射(OC)理论为基础,提出了一种基于进化操作行为学习模型(EOBLM)的移动机器人学习避障行为的方法。该方法是一种改进的AHC学习模式,评价单元采用多层前向神经网络来实现,利用TD算法和梯度下降法进行权值更新,这一阶段学习用来生成取向性信息,作为内在动机决定进化的方向;动作选择单元主要用来优化操作行为以实现状态到动作的最佳映射。优化过程分两个阶段来完成,第一阶段通过操作条件反射学习算法得到的信息熵作为个体适应度,执行GA学习算法搜索最优个体;第二阶段由OC学习算法选择最优个体内的最优操作行为,并得到新的信息熵值。通过移动机器人避障仿真实验,结果表明所设计的EOBLM能使机器人通过不断与外界未知环境进行交互主动学会避障的能力,与传统的AHC方法相比其自学习自适应的能力得到加强。     

基于ANFIS的机器人系统建模的研究

针对机器人这种不确定性的复杂非线性系统很难建立其精确的数学模型这一问题,提出一种基于自适应神经模糊推理(ANFIS)的方法对机器人系统进行建模.此方法将模糊推理和神经网络的学习能力有机地结合起来,并利用神经网络的学习机制自动地从输入输出数据中提取规则.建模过程中为了给ANFIS赋予一个合适的初始状态,选用减法聚类对输入数据进行处理.ANFIS网络的所有参数采用混合算法进行调节,即前提参数采用误差反向传播法,结论参数采用最小二乘法.最后在Matlab中对二自由度机器人进行仿真研究,仿真结果表明该方法模型结构简单,建模速度快,辨识精度高,同时也验证了该方法的有效性,为进一步实现机器人鲁棒自适应控制打下基础.     

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．     

不确定混沌系统的差分进化小波神经网络控制

提出一种基于小生境自适应差分进化小波神经网络（NADE-WNN）的方法对不确定混沌系统进行控制。该方法利用小波神经网络学习未知模型混沌系统的动态特性并实施控制,为提高神经网络的学习精度和收敛速度,采用小生境自适应差分进化算法同时优化小波神经网络的结构和参数,简化网络结构,提高网络的学习精度和全局收敛性。仿真实验结果表明,在有外部干扰和参数摄动的情况下,NADE-WNN仍能对不确定混沌系统进行有效控制,且网络结构、控制精度和收敛速度都优于传统神经网络。     

具有环境自适应能力的多机器人编队系统研究

对多机器人的体系结构进行了研究.采用时空表和时间控制器相结合的方法，解决多机器人间的协调协作问题.针对编队问题的具体特性，提出了基于环境的记忆学习方法，使多机器人编队系统具有较强的环境自适应能力.最后,通过仿真实验实现了整个多机器人系统,进一步验证了各个算法的可行性和有效性.􀁱     

差分进化算法在游戏策略设计中的应用

基于Robocode机器人战斗仿真引擎,从设计原理和实际实现两方面阐述了运用差分进化算法来设计机器人行动策略。重点介绍差分进化算法理论如何与实际应用相结合:预先对机器人各种行为进行编程,行为选择逻辑应用差分进化算法实现,让机器人具有根据不同的场景和对手,自适应的选择最优的响应策略的能力。运用设计好的机器人与其它机器人战斗,对战斗结果进行分析。最后,提出可改进的若干方向。     

不确定性自由漂浮空间机器人神经自适应控制

针对具有模型不确定性以及外部干扰下的自由漂浮空间机器人,采用一种整体逼近的神经网络自适应控制方法。该方法采用RBF神经网络对不同重力环境下系统模型的不确定项进行整体逼近,对系统的不确定项进行在线自适应学习。神经网络的逼近误差以及外界干扰由鲁棒项进行消除。该方法不依赖于系统模型,简化了控制系统的结构,在考虑重力等不确定项的情况下不用改变控制器也能进行控制,并且根据李亚普诺夫理论证明了所设计控制器使系统渐进稳定。在不同重力环境下进行了仿真,验证了控制方案的有效性。     

Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network

Control system implementation is one of the major difficulties in rehabilitation robot design. A newly developed adaptive impedance controller based on evolutionary dynamic fuzzy neural network (EDRFNN) is presented, where the desired impedance between robot and impaired limb can be regulated in real time according to the impaired limb??s physical recovery condition. Firstly, the impaired limb??s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using a slide average least squares (SALS)identification algorithm. Then, hybrid learning algorithms for EDRFNN impedance controller are proposed, which comprise genetic algorithm (GA), hybrid evolutionary programming (HEP) and dynamic back-propagation (BP) learning algorithm. GA and HEP are used to off-line optimize DRFNN parameters so as to get suboptimal impedance control parameters. Dynamic BP learning algorithm is further online fine-tuned based on the error gradient descent method. Moreover, the convergence of a closed loop system is proven using the discrete-type Lyapunov function to guarantee the global convergence of tracking error. Finally, simulation results show that the proposed controller provides good dynamic control performance and robustness with regard to the change of the impaired limb??s physical condition.     

基于改进人工势场法的足球机器人避碰控制

本文在分析足球机器人避碰的特点及传统人工势场法不足的基础上，提出了引入 障碍物的速度和加速度矢量的改进人工势场法，用于足球机器人的避碰控制．仿真实验表明 ，这种方法是可行的，并弥补了传统人工势场法的不足．     

A method to learn the inverse kinematics of multi-link robots by evolving neuro-controllers

A general method to learn the inverse kinematic of multi-link robots by means of neuro-controllers is presented. We can find analytical solutions for the most used and well-known robots in the literature. However, these solutions are specific to a particular robot configuration and are not generally applicable to other robot morphologies. The proposed method is general in the sense that it is independent of the robot morphology. The method is based on the evolutionary computation paradigm and works obtaining incrementally better neuro-controllers. Furthermore, the proposed method solves some specific issues in robotic neuro-controller learning: it avoids any neural network learning algorithm which relies on the classical supervised input-target learning scheme and hence it lets to obtain neuro-controllers without providing targets. It can converge beyond local optimal solutions, which is one of the main drawbacks of some neural network training algorithms based on gradient descent when applied to highly redundant robot morphologies. Furthermore, using learning algorithms such as the neuro-evolution of augmenting topologies it is also possible to learn the neural network topology which is a common source of empirical testing in neuro-controllers design. Finally, experimental results are provided when applying the method to two multi-link robot learning tasks and a comparison between structural and parametric evolutionary strategies on neuro-controllers is shown.     

A Study on Acquiring Underlying Behavioral Criteria for Manipulator Motion by Focusing on Learning Efficiency

Conventional humanoid robotic behaviors are directly programmed depending on the programmer's personal experience. With this method, the behaviors usually appear unnatural. It is believed that a humanoid robot can acquire new adaptive behaviors from a human, if the robot has the criteria underlying such behaviors. The aim of this paper is to establish a method of acquiring human behavioral criteria. The advantage of acquiring behavioral criteria is that the humanoid robots can then autonomously produce behaviors for similar tasks with the same behavioral criteria but without transforming data obtained from morphologically different humans every time for every task. In this paper, a manipulator robot learns a model behavior, and another robot is created to perform the model behavior instead of being performed by a person. The model robot is presented some behavioral criteria, but the learning manipulator robot does not know them and tries to infer them. In addition, because of the difference between human and robot bodies, the body sizes of the learning robot and the model robot are also made different. The method of obtaining behavioral criteria is realized by comparing the efficiencies with which the learning robot learns the model behaviors. Results from the simulation have demonstrated that the proposed method is effective for obtaining behavioral criteria. The proposed method, the details regarding the simulation, and the results are presented in this paper.     

模仿学习:一种新人工生命动画方法

提出一种新的人工生命动画方法—模仿学习. 模仿是一种非常有效的掌握运动技能的学习方式. 一项运动技能为无数个相关运动序列的集合. 通过模仿代表性运动序列,将蕴含的局部运动技能泛化,可获得完整的运动技能. 模仿学习以运动相似度匹配和简单--复杂行为方法论为核心,并以进化计算为优化方法. 模仿学习降低进化计算对传统评价函数的依赖,减少评价函数设计时间,提高优化复杂目标的能力,因此提高了制作效率. 基于PhysX仿真平台,本文以人工猫的着陆行为验证了本文方法的有效性,并取得了良好的效果.     

Multiobjective Evolution of Neural Controllers and Task Complexity

Robots operating in everyday life environments are often required to switch between different tasks. While learning and evolution have been effectively applied to single task performance, multiple task performance still lacks methods that have been demonstrated to be both reliable and efficient. This paper introduces a new method for multiple task performance based on multiobjective evolutionary algorithms, where each task is considered as a separate objective function. In order to verify the effectiveness, the proposed method is applied to evolve neural controllers for the Cyber Rodent (CR) robot that has to switch properly between two distinctly different tasks: 1) protecting another moving robot by following it closely and 2) collecting objects scattered in the environment. Furthermore, the tasks and neural complexity are analyzed by including the neural structure as a separate objective function. The simulation and experimental results using the CR robot show that the multiobjective-based evolutionary method can be applied effectively for generating neural networks that enable the robot to perform multiple tasks simultaneously.     

自律个体的一种遗传强化模型研究

自律个体的遗传强化模型是模拟实际生物进化机制的计算模型。本文利用进化算法和人工神经网络的研究方法,设计一种自律个体的遗传强化模型。该模型强调多层次学习,实现了先天的遗传学习进化和后天的个体神经系统学习进化的有机结合。本文同时将该模型应用于模拟机器人的生存控制,观察它在环境中的行为表现及经能力,取得了满意的实验结果。     

A cooperative behavior learning control of multi-robot using trace information

The distributed autonomous robotic system has superiority of robustness and adaptability to dynamical environment, however, the system requires the cooperative behavior mutually for optimality of the system. The acquisition of action by reinforcement learning is known as one of the approaches when the multi-robot works with cooperation mutually for a complex task. This paper deals with the transporting problem of the multi-robot using Q-learning algorithm in the reinforcement learning. When a robot carries luggage, we regard it as that the robot leaves a trace to the own migrational path, which trace has feature of volatility, and then, the other robot can use the trace information to help the robot, which carries luggage. To solve these problems on multi-agent reinforcement learning, the learning control method using stress antibody allotment reward is used. Moreover, we propose the trace information of the robot to urge cooperative behavior of the multi-robot to carry luggage to a destination in this paper. The effectiveness of the proposed method is shown by simulation. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

一种基于改进Elman神经网络的机器人行为控制器

为了增强移动机器人在动态环境中的学习和适应能力,提出了一种新的基于改进Elman神经网络的具有学习和记忆功能的机器人行为控制器,并且利用遗传算法来优化神经网络的连接权值,提高了机器人行为的准确性和快速型。仿真实验结果显示,本文提出的方法对机器人的学习和适应能力有很大的提高。