基于内在动机的智能机器人自主发育算法

针对两轮自平衡机器人在学习过程中主动性差的问题,受心理学内在动机理论启发,提出一种基于内在动机的智能机器人自主发育算法。该算法在强化学习的理论框架中,引入模拟人类好奇心的内在动机理论作为内部驱动力,与外部奖赏信号一起作用于整个学习过程。采用双层内部回归神经网络存储知识的学习与积累,使机器人逐步学会自主平衡技能。最后针对测量噪声污染对机器人平衡控制中两轮角速度的影响,进一步采用卡尔曼滤波方法进行补偿,以提高算法收敛速度,降低系统误差。仿真实验表明,该算法能够使两轮机器人通过与环境的交互获得认知,成功地学会运动平衡控制技能。     

基于BP神经网络的移动机器人避障设计及仿真

为了解决移动机器人在复杂环境中如何高效精确地躲避障碍物的问题,提出了一种基于BP神经网络的避障方法。建立了机器人的避障运动模型并设计了神经网络避障控制系统;分析了机器人在运动过程中与障碍物的位置关系,使用超声波传感器采集距离信息,进行BP神经网络输入、输出训练并采用Matlab工具进行仿真试验。结果表明,该方法可以高效精确地实现移动机器人的自主避障,运行相对稳定、轨迹连续平滑,达到了较为理想的避障效果。验证了方法的可行性和有效性,为移动机器人自主避障提供了一种新的控制方法。     

Vision-based path following by using a neural network Guidance System

This article describes a neural network controller for guidance of a robot arm, used to model some aspects of autonomous vehicle technology. The controller uses video images with adaptive view-angles for the sensory input, and the system was configured to simulate an autonomous vehicle guidance system on a flat terrain using a high-contrast guiding path. To demonstrate the feasibility of using neural networks in this type of application, an Intelledex 405 robot fitted with a video camera and associated vision system was used. Phase I of the project consisted of a single-speed implementation and limited network training. Phase II featured a multi-speed implementation using adaptively varied view-angles based on robot arm velocity. It was shown that the neural network controller was able to control the robot arm along a path composed of path segments unlike those with which it was trained. In addition it was shown that a multi-speed implementation with adaptive view angles improved system performance. © 1994 John Wiley & Sons, Inc.     

Conceptual representations of actions for autonomous robots

An autonomous robot involved in long and complex missions should be able to generate, update and process its own plans of action. In this perspective, it is not plausible that the meaning of the representations used by the robot is given from outside the system itself. Rather, the meaning of internal symbols must be firmly anchored to the world through the perceptual abilities and the overall activities of the robot. According to these premises, in this paper we present an approach to action representation that is based on a “conceptual” level of representation, acting as an intermediate level between symbols and data coming from sensors. Symbolic representations are interpreted by mapping them on the conceptual level through a mapping mechanism based on artificial neural networks. Examples of the proposed framework are reported, based on experiments performed on a RWI-B12 autonomous robot.     

From learning to new goal generation in a bioinspired robotic setup

In the field of cognitive bioinspired robotics, we focus on autonomous development, and propose a possible model to explain how humans generate and pursue new goals that are not strictly dictated by survival. Autonomous lifelong learning is an important ability for robots to make them able to acquire new skills, and autonomous goal generation is a basic mechanism for that. The Intentional Distributed Robotic Architecture (IDRA) here presented intends to allow the autonomous development of new goals in situated agents starting from some simple hard-coded instincts. It addresses this capability through an imitation of the neural plasticity, the property of the cerebral cortex supporting learning. Three main brain areas are involved in goal generation, cerebral cortex, thalamus, and amygdala; these are mimicked at a functional level by the modules of our computational model, namely Deliberative, Working-Memory, Goal-Generator, and Instincts Modules, all connected in a network. IDRA has been designed to be robot independent; we have used it in simulation and on the real Aldebaran NAO humanoid robot. The reported experiments explore how basic capabilities, as active sensing, are obtained by the architecture.     

基于情感-时空信息的机器人服务自主认知及个性化选择

为了提高机器人的人机交互能力,针对家庭服务机器人在认知服务任务时往往忽略用户情感因素的弊端,提出了以用户情感为核心的机器人服务任务自主认知方法以及个性化服务选择策略.首先,利用智能空间本体技术结合用户情感状态与时间空间信息建立情感-时空本体模型,消除智能空间中的信息异构性.在此基础上,将与情感-时空相关的服务规则库编码并训练BP（逆向传播）神经网络构建推理机,将实时更新的智能空间信息与神经网络相匹配,推理出机器人需要执行的服务,实现机器人对以用户情感为核心的服务任务自主认知.最后,将用户情感状态作为执行服务的奖惩反馈信号,对服务集合中的子类服务进行动态的偏好度调节,完成有针对性的服务选择.仿真结果表明,基于该方法能够实现以用户情感为核心的机器人服务任务自主认知,同时可以根据用户偏好变化提供个性化的服务,有效提高了家庭服务机器人的智能性和灵活性,增强了用户的服务体验.     

基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统设计

变磁力吸附爬壁机器人是一种具有快速、灵活移动方式的爬行机器人,但其吸附力难以控制,越障稳定性较差,难以保证机器人的平稳爬行;为实现爬壁机器人在大型建筑结构外表面的自主避障,提升机器人与运动平面之间的吸附紧密性,设计基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统;按照PCB控制要求,连接外置SRAM设备与传感器模块,借助驱动I/O口电路提供的电力驱动作用,控制气动阀门的闭合情况,完成变磁力吸附爬壁机器人控制系统硬件结构设计;建立Netvlad神经网络体系,通过划分控制指令程序任务的方式,确定移植参数取值范围,实现对控制协议的移植处理,联合相关硬件应用结构,完成基于Netvlad神经网络的变磁力吸附爬壁机器人控制系统设计;实验结果表明,在所设计系统作用下,障碍物所在位置与爬壁机器人所在位置之间的实测距离未大于30cm,能够有效实现自主避障,保证机器人与运动平面之间的紧密吸附。     

A new artificial life body: biologically inspired dynamic bipedal humanoid robots

Recently, a biologically inspired, bipedal, dynamic, humanoid robot was developed at the Artificial Life and Robotics Laboratory of Oita University. This bipedal humanoid robot is able to walk dynamically and to go up and down stairs. The central pattern generator developed produces various types of walking pattern. This robot has a pair of small CMOS color CCD cameras, a speaker, and a microphone in the head part, and will have a GPS, a portable telephone, and other sensors in the body part, so that the integration of locomotion and behavior to achieve specific demonstrations will be realized. This project develops dynamic mobility and the ability for autonomous recognition and navigation using the biological central nervous system, the brain system, and the real-time control system. Also, the design principles that demonstrate the dynamic interaction between neural and mechanical controls will be clarified. In Phase I, the platform of a small, bipedal, humanoid robot is used to develop autonomous locomotion and autonomous sensing and navigation. In Phase II of the project, an iteration on the platform design for human-size, bipedal, humanoid robots will be performed for operational testing. The development of bipedal humanoid robots that capture biological systems with unique principles and practices could dramatically increase their performance in tasks for national security needs.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

PerAc: A neural architecture to control artificial animals

In this paper, we propose a new neural architecture called PerAc witch is a systematic way to decompose the control of an autonomous robot in perception and action flows. The PerAc architecture is used for the simulation of a vision system with a moving eye and then for landmark-based navigation on a mobile robot to learn without any a priori symbolic representation.     

Learning to generate proactive and reactive behavior using a dynamic neural network model with time-varying variance prediction mechanism

This paper discusses a possible neurodynamic mechanism that enables self-organization of two basic behavioral modes, namely a ‘proactive mode’ and a ‘reactive mode,’ and of autonomous switching between these modes depending on the situation. In the proactive mode, actions are generated based on an internal prediction, whereas in the reactive mode actions are generated in response to sensory inputs in unpredictable situations. In order to investigate how these two behavioral modes can be self-organized and how autonomous switching between the two modes can be achieved, we conducted neurorobotics experiments by using our recently developed dynamic neural network model that has a capability to learn to predict time-varying variance of the observable variables. In a set of robot experiments under various conditions, the robot was required to imitate other’s movements consisting of alternating predictable and unpredictable patterns. The experimental results showed that the robot controlled by the neural network model was able to proactively imitate predictable patterns and reactively follow unpredictable patterns by autonomously switching its behavioral modes. Our analysis revealed that variance prediction mechanism can lead to self-organization of these abilities with sufficient robustness and generalization capabilities.     

Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot

In this paper, two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD fuzzy logic controller (PDFLC). Basically, mobile robots are required to work and navigate under exigent circumstances where the environment is hostile, full of disturbances such as holes and stones. The robot navigation leads to an autonomous decision making to overcome an obstacle and/or to stop the engine to protect it. In fact, the actuators that drive the robot should in no way be damaged and should stop to change direction in case of insurmountable disturbances. In this context, two controllers are implemented and a comparative study is carried out to demonstrate the effectiveness of the proposed approaches. For the first one, neural networks are used to optimize the parameters of a PID controller and for the second a fuzzy inference system type Mamdani based controller is adopted. The goal is to implement control algorithms for safe robot navigation while avoiding damage to the motors. In these two control cases, the smart robot has to quickly perform tasks and adapt to changing environment conditions while ensuring stability and accuracy and must be autonomous with regards to decision making. Simulations results aren't done in real environments, but are obtained with the Matlab/Simulink environment in which holes and stones are modeled by different load torques and are applied as disturbances on the mobile robot environment. These simulation results and the robot performances are satisfactory and are compared to a PID controller in which parameters are tuned by the Ziegler–Nichols tuning method. The applied methods have proven to be highly robust.     

Neural Networks for Autonomous Path-Following with an Omnidirectional Image Sensor

This paper presents a path-following system implemented with two different types of neural networks, that enables an autonomous mobile robot to return along a previously learned path in a dynamic environment. The path-following is based on data provided by an omnidirectional conical visual system, derived from the COPIS sensor, but with different optical reflective properties. The system uses optical and software processing and a neural network to learn the path, described as a sequence of selected points. In the navigation phase it drives the robot along this learned path. Interesting results have been achieved using low cost equipment. Test and results are presented.     

基于模糊神经网络的六足机器人自主导航闭环控制系统设计

针对未知环境中六足机器人的自主导航问题,设计了一种基于模糊神经网络的自主导航闭环控制算法,并依据该算法设计了六足机器人的导航控制系统．算法融合了模糊控制的逻辑推理能力与神经网络的学习训练能力,并引入闭环控制方法对算法进行优化．所设计的控制系统由信息输入、模糊神经网络、指令执行以及信息反馈4个模块组成．环境及位置信息的感知由GPS（全球定位系统）传感器、电子罗盘传感器和超声波传感器共同完成．采用C语言重建模糊神经网络控制算法,并应用于该系统．通过仿真实验,从理论上论证了基于模糊神经网络的闭环控制算法性能优于开环控制算法,闭环控制算法能够减小六足机器人在遇到障碍物时所绕行的距离,行进速度提高了6.14%,行进时间缩短了8.74%．在此基础上,开展了实物试验．试验结果表明,该控制系统能够实现六足机器人自主导航避障控制功能,相对于开环控制系统,能有效地缩短行进路径,行进速度提高了5.66%,行进时间缩短了7.25%,验证了闭环控制系统的可行性和实用性．     

Dynamic perception after visually guided grasping by a human-like autonomous robot

We will explore dynamic perception following the visually guided grasping of several objects by a human-like autonomous robot. This competency serves for object categorization. Physical interaction with the hand-held object gives the neural network of the robot the rich, coherent and multi-modal sensory input. Multi-layered self-organizing maps are designed and examined in static and dynamic conditions. The results of the tests in the former condition show its capability of robust categorization against noise. The network also shows better performance than a single-layered map does. In the latter condition we focus on shaking behavior by moving only the forearm of the robot. In some combinations of grasping style and shaking radius the network is capable of categorizing two objects robustly. The results show that the network capability to achieve the task largely depends on how to grasp and how to move the objects. These results together with a preliminary simulation are promising toward the self-organization of a high degree of autonomous dynamic object categorization.     

基于深度学习的移动机器人自主导航实验平台

随着移动机器人的发展,其应用场景越来越复杂,对自主导航这一关键技术提出了更高要求。本文搭建了移动机器人实验平台,设计了基于深度学习的自主导航方法,将RGB图像作为卷积神经网络模型的输入,即可直接输出导航控制信号,不仅降低硬件成本,而且避免复杂的特征工程和规划策略。实验结果表明该平台具有良好的自主导航性能,对移动机器人适应未知复杂环境作业有着重要参考价值。同时,能够为机器人工程专业实践教学提供实验平台,通过开展相关应用拓展,促进学生创新研究能力的培养。     

基于AOCA仿生学习模型的两轮机器人自主平衡学习研究

以两轮机器人的自主平衡学习控制为研究对象,针对传统控制方法无法实现机器人类似人或动物的渐进学习过程,依据斯金纳的操作条件反射理论建立了一种自治操作条件反射自动机（Autonomous operant conditioning automaton,AOCA）模型,设计一种基于AOCA的仿生学习算法,并进行机器人姿态平衡学习实验仿真研究. 实验结果表明,基于AOCA的仿生学习方法能有效地实现机器人的自主平衡学习控制,机器人系统的平衡能力在学习控制过程中自组织地渐进形成,并得以发展和完善.     

Evolution of neural control structures: some experiments on mobile robots

From perception to action and from action to perception, all elements of an autonomous agent are interdependent and need to be strongly coherent. The final behavior of the agent is the result of the global activity of this loop and every weakness or incoherence of a single element has strong consequences on the performances of the agent. We think that, for the purpose of building autonomous robots, all these elements need to be developed together in continuous interaction with the environment. We describe the implementation of a possible solution (artificial neural networks and genetic algorithms) on a real mobile robot through a set of three different experiments. We focus our attention on three different aspects of the control structure: perception, internal representation and action. In all the experiments these aspects are not considered as single processing elements, but as part of an agent. For every experiment, the advantages and disadvantages of this approach are presented and discussed. The results show that the combination of genetic algorithms and neural networks is a very interesting technique for the development of control structures in autonomous agents. The time necessary for evolution, on the other hand, is a very important limitation of the evolutionary approach.     

Dynamics of behavior: Theory and applications for autonomous robot architectures

Limitations both for the further development as well as for the actual technical application of autonomous robots arise from the lack of a unifying theoretical language. We propose three concepts for such a language: (1) Behaviors are represented by variables, specific constant values of which correspond to task demands; (2) Behaviors are generated as attractors of dynamical systems; (3) Neural field dynamics lift these dynamic principles to the representation of information. We show how these concepts can be used to design autonomous robots. Because behaviors are generated from attractor states of dynamical systems, design of a robot architecture addresses control-theoretic stability. Moreover, flexibility of the robot arises from bifurcations in the behavioral dynamics. Therefore techniques from the qualitative theory of dynamical systems can be used to design and tune autonomous robot architectures. We demonstrate these ideas in two implementations. In one case, visual sensory information is integrated to achieve target acquisition and obstacle avoidance in an autonomous vehicle minimizing the known problem of spurious states. In a second implementation of the same behavior, a neural dynamic field endows the system with a form of obstacle memory. A critical discussion of the approach highlights strengths and weaknesses and compares to other efforts in this direction.     

多用途欠驱动手爪的自主抓取研究

对欠驱动手爪自主抓取进行了研究,将其分为自主决策和抓取控制两个过程．首先分析了欠驱动手爪的特点、主要的抓取模式,并借鉴人的抓取经验,采用模糊输入方法,综合考虑抓取任务要求和物体本身的特征属性,利用模糊神经网络良好的分类特性选择合适的抓取模式．在此基础上,完成手指姿势调整,采用基于传感器反馈的控制策略,在被抓物体上形成的合适的力分布以获得稳定抓取,并通过抓取实例验证了抓取决策和控制的正确性,提高了欠驱动手爪抓取的自动化水平．     

Evolutionary robot controllers with competitive and cooperative neural networks

This article describes a new approach for control systems for an autonomous mobile robot by using sandwiches of two different types of neural network. One is a neural network with competition and cooperation, and is used for recognizing sensor information where synaptic coupling are fixed. The second is a neural network with adaptive synaptic couplings corresponding to a genotype in a creature, and used for self-learning for the wheel controls. In a computer simulation model, we were successful in obtaining four types of robot with good performance when going along a wall. The model also showed robustness in a real environment. This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo Japan, January 15–17, 2001