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     

基于IHDR算法和BP神经网络复合框架的机器人服务自主认知和发育系统

为了解决传统的基于知识或基于学习的机器人服务认知机制的智能性和普适性较差的问题,构建了一个基于IHDR(增量分层判别回归)算法和BP(反向传播)神经网络复合框架的机器人服务任务自主认知和自主发育系统.在家庭服务机器人智能空间中丰富的传感器和物联网技术的支持下,采集大量用于机器人学习和发育的样本数据;在此基础上,针对智能空间样本数据的混合特性,设计改进的IHDR算法,实现对混合型样本数据的聚类更新和响应计算,并将生成的IHDR树作为机器人存储历史经验的"大脑",使机器人能够利用"大脑"中已有的经验进行自主学习和相应判断,以实现对服务的自主认知;利用JSHOP2(Java simple hierarchical planner)规划器对认知的复杂任务进行分解,得到可被机器人直接执行的原子任务.为了避免IHDR树规模不足的局限性,设计基于BP神经网络的服务认知算法,利用样本数据训练BP神经网络,实现智能空间实际场景到用户所需服务的映射,在IHDR树无法提供历史经验的情况下,使机器人仍能基于BP神经网络自主进行服务决策.然后将此映射结果以增量的方式更新到IHDR树中,丰富其具备的经验知识,实现机器人服务自主认知能力的发育.仿真实验结果表明,该复合框架可以有效提高服务机器人对智能空间情景下用户所需服务的认知准确性及认知发育能力,推进人机共融的实现.     

机器人自主感知模型在网络机器人中的应用

基于机器人自主感知模型(RAPM)构建了机器人系统，该系统可通过网络自主感知获得动态网络信息，实时建立路径，使机器人完成超出其视野范围的任务．机器人自主感知模型具有很强的扩展能力和合理的构架，突破了以往移动机器人仅使用自身传感器或使用网络固定传感器的局限，对提高基于Internet的机器人智能及其行为能力有重要意义．实验结果证明了机器人自主感知模型的可行性和有效性．     

基于STM32的智能感知自主运输机器人

项目是基于STM32(以ARM Cortex-M3为内核)和OpenMV3 CamM7可编程摄像头,结合红外线数字避障传感器和US-100超声波传感器设计的一款智能感知自主运输机器人。根据小车作业需求,利用3D建模技术对机器人的外观进行设计。首先利用OpenMV摄像头模块识别货物的二维码,得到货物信息,之后机器人通过循迹模块按照指定路线将货物送到指定位置完成作业,在这个过程中机器人可自主避障。通过机器自主运输可大大降低运输成本。     

基于意会知识的机器人仿生体系结构研究

首先根据知识表征对现有体系结构分类,明确了意会知识在机器人体系结构研究中的地位。借鉴认知心理学、认知科学中的研究成果,给出基于程序记忆和情节记忆的仿生体系结构,并对该体系结构的各模块详细设计。该体系结构在不需要先验知识的情况下,完全利用经验获得智能行为,模仿了动物的认知过程,使机器人具备更好的实时学习能力和自适应能力。     

基于人工生命的自主游戏角色

利用人工生命的方法,通过构建游戏角色的几何模型,动作模型,行为模型,认知模型,感知系统等,使游戏角色成为能够自己决定做什么以及如何做的自主角色。并且构建了一个能够给自主角色提供各种感知、寻径信息的分层虚拟三维环境。     

基于强化学习和模糊逻辑的移动机器人导航

自主导航是移动机器人的一项关键技术。该文采用强化学习结合模糊逻辑的方法实现了未知环境下自主式移动机机器人的导航控制。文中首先介绍了强化学习原理,然后设计了一种未知环境下机器人导航框架。该框架由避碰模块、寻找目标模块和行为选择模块组成。针对该框架,提出了一种基于强化学习和模糊逻辑的学习、规划算法：在对避碰和寻找目标行为进行独立学习后,利用超声波传感器得到的环境信息进行行为选择,使机器人在成功避碰的同时到达目标点。最后通过大量的仿真实验,证明了算法的有效性。     

基于传感器信息融合的移动机器人自主爬楼梯技术研究

机器人自主爬楼梯是移动机器人完成危险环境探查、侦察、救灾等任务需要具备的基本智能行为之一.分析了楼梯的多样性和履带式机器人爬楼梯固有的不稳定性导致机器人爬楼梯工作的复杂性,描述了带前导手臂的履带式移动机器人爬楼梯的步骤,简要介绍了利用超声波、视频摄像头和激光扫描测距仪信息来感知楼梯和判断机器人与楼梯相对位置的算法,最后提出了一个基于传感器测量值可信度的信息融合方法进行楼梯参数感知和行驶方向计算的机器人自主爬楼梯的控制系统结构.     

具有操作条件反射机能的人工感觉运动系统

为探索认知机理、模拟认知机能进而提高机器人的认知及智能水平,提出了一种具有操作条件反射机能的人工感觉运动系统.该系统以复现感觉运动系统的方式重现了生物的运动神经认知,实现了从感知到运动的映射关系,同时借鉴了斯金纳的操作条件反射理论,使得该系统具有操作条件反射机能,遵从"刺激-反应-强化"的逻辑形成了感知与运动之间的闭环系统.为验证系统的正确有效性,复现了行为心理学及《控制论》中的两个经典实验.对比实验结果证明,本系统成功地模拟了生物感觉运动系统,使机器人具有类似生物的自学习能力,能自主的认知环境,实现对环境的自适应.     

基于KQML语言的多自主移动机器人仿真系统

用JAVA语言开发了栅格环境下的多自主移动机器人仿真系统,通过KQML语言通信模拟了多个自主的移动机器人,机器人的自主性主要体现在自主感知环境和自主进行路径规划、任务执行和安全导航等工作．该仿真系统具有平台无关性、地图无关性、算法无关性以及机器人配置的无关性,为多自主机器人系统的研究提供了一个可借鉴的平台．     

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.     

基于行为的自主微小移动机器人智能体系结构研究

该文提出了一种模拟人类学习与进化过程的机器人智能体系结构,微小机器人利用设计人员事先设计的机器人基本行为,根据实际环境和具体任务要求,采用增强学习方式,通过群体行为进化,自主创建满足任务要求和适应环境的具体动作。克服了设计人员在采用基于符号的传统人工智能方法时,由于对外部环境和任务的认识不足而造成的局限性,使机器人的行为动作更适合环境和任务要求。     

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。     

Autonomous knowledge acquisition based on artificial curiosity: Application to mobile robots in an indoor environment

This paper describes an autonomous system for knowledge acquisition based on artificial curiosity. The proposed approach allows a humanoid robot to discover, in an indoor environment, the world in which it evolves, and to learn autonomously new knowledge about it. The learning process is accomplished by observation and by interaction with a human tutor, based on a cognitive architecture with two levels. Experimental results of deployment of this system on a humanoid robot in a real office environment are provided. We show that our cognitive system allows a humanoid robot to gain increased autonomy in matters of knowledge acquisition.     

Modeling robot cognitive activity through active mental entities

This paper aims at laying down the foundations of an approach to the development of autonomous robot control architecture based on the explicit representation of mental attitudes underlying robot behavior, considered as autonomous active entities. The approach is intended to integrate concepts from the area of distributed architectures and of mental attitude representation and aims at realizing an explicit motivational basis for robot behavior. Starting from an analysis of the evolution of autonomous robot control architectures, we discuss and motivate the introduction of active mental entities in the context of a distributed control architecture. Attention is then focused on two classes of mental entities, namely intender and attender: their main features are illustrated and discussed. A prototypical implementation of the proposed paradigm and its application to the control of the Khepera simulator are then described. A comparison with related works and a discussion of the main directions of future research conclude the paper.     

LOGUE: an architecture for task and behavior object transmission among multiple autonomous robots

This paper proposes a new communication and control architecture which improves the capability and the flexibility of multiple autonomous robot systems in performing a complicated task and coping with unpredictable situations. This system treats robot’s information as a Behavior Element Object (BEO) and a Task Object (TO) in terms of Object Oriented paradigm. Both BEO and TO can be serialized, so they can be communicated among the robots and behavior server system in the network. The action manager module, device module, and some checking mechanisms are also designed for executing new TO or BEO sent from other robots or a server system. A simulation and basic experiments are presented for a situation of robots’ relief for an emergency purpose.     

自主式移动机器人的递阶分布式监控

本文提出面向任务集成的自主式移动机器人体系结构,组成一个由分布式黑板协调和组织的多智能体系统,具有自组织、自适应的特点．应用离散事件系统的监控理论,建立单个智能体的ＤＥＳ模型,给出其满足一定运行指标的监控器,并证明整个系统的可控性     

Development of distance recognition using an ocellus camera for an autonomous personal robot

We are attempting to develop an autonomous personal robot that has the ability to perform practical tasks in a human living environment by using information derived from sensors. When a robot operates in a human environment, the issue of safety must be considered in regard to its autonomous movement. Thus, robots absolutely require systems that can recognize the external world and perform correct driving control. We have thus developed a navigation system for an autonomous robot. The system requires only image data captured by an ocellus CCD camera. In this system, we allow the robot to search for obstacles present on the floor. Then, the robot obtains distance recognition necessary for evasion of the object, including data of the obstacle’s width, height, and depth by calculating the angles of images taken by the CCD camera. We applied the system to a robot in an indoor environment and evaluated its performance, and we consider the resulting problems in the discussion of our experimental results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Experimental study on autonomous mobile robot acquiring optimal action to avoid moving obstacles

The principal aim of this study was to show how an autonomous mobile robot can acquire the optimal action to avoid moving multiobstacles through interaction with the real world. In this paper, we propose a new architecture using hierarchical fuzzy rules, a fuzzy evaluation system, and learning automata. By using our proposed method, the robot autonomously acquires finely tuned behavior which allows it to move to its goal and avoid moving obstacles by using the steering and velocity control inputs simultaneously. We also show experimental results which confirm the feasibility of our method.     

一种基于自治Agent的分布式入侵检测系统

设计并实现了一个基于自治Agent的分布式入侵检测系统。系统采用层次化的管理控制结构，自上而下包括监控器、收发器和Agent 3种功能实体。使用基于publish/subscribe的通信模式减少了IDS系统的通信开销，提高了通信效率。