Hybrid Control for Autonomous Mobile Robot Navigation Using Neural Network Based Behavior Modules and Environment Classification

A hybrid control architecture combining behavior based reactive navigation and model based environment classification has been developed. It is also hybrid in the sense that both competitive coordination and cooperative coordination are used for the BBC (Behavior Based Control) part. The contributions are as follows. First, a Neural Network (NN) in charge of environment classification has been developed based on 16 prototypes of topological maps roughly describing various local navigation environments. This environment classification NN not only enables the navigator to avoid local minimum points but also eliminates the requirement for prior detailed modeling of the environment since it needs to memorize only rough information on local environments encountered along the way that might be sufficient for navigation. Next, an NN based reactive behavior controller will be trained to learn human steering commands for each of the 16 prototype local environments. Third, the modified potential field (MPF) method obtained by adding the free space vector as the third component is used to select a particular reactive behavior in conjunction with the classification NN. Finally, a hybrid control architecture integrating all three of these concepts was developed. It avoids local minimum traps as well as solves the problems of poor obstacle clearance or oscillation. It is robust against sensor noise and adaptive to dynamic environments. This hybrid architecture is also amenable to easy addition of new behaviors due to the modularity of the BBC architecture. The effectiveness of the proposed architecture has been verified through both computer simulation and an actual robot called MORIS (MObile Robot as an Intelligent System).     

Homeostatic control for a mobile robot: Dynamic replanning in hazardous environments

A longstanding goal of robotics has been to introduce intelligent machines into environments dangerous to humans. These environments also pose hazards to the robots themselves. By embedding sensing devices as a means for monitoring the internal state of the robot, dynamic plan reformulation can occur in situations that threaten the existence of the robot. A method exploiting an analogy to the endocrine control system is forwarded as the preferred method for homeostatic control—the maintenance of a safe internal environment for the machine. Examples are given describing the impact of fuel reserve depletion and global temperature stress. A methodology using signal schemas as a means to supplement the existing motor schema control found in the Autonomous Robot Architecture (AuRA) is presented.     

基于Agent的遥操作机器人控制器研究

研究网络环境下基于Agent的遥操作机器人控制器结构，提出了人／智能体／机器人（M／AR）模型，探讨了基于Agent的机器人控制器的体系 和功能，介绍了采用任务规划和反应式行为的复合机器人控制系统，对意外事件的处理和运动轨迹的规划实验表明，这种遥操作机器人控制器能提高系统的实时性，可靠性和对环境的适应性。     

A Fuzzy Perceptual Model for Ultrasound Sensors Applied to Intelligent Navigation of Mobile Robots

The way of understanding the role of perception along the intelligent robotic systems has evolved greatly since classic approaches to the reactive behavior-based approaches. Classic approaches tried to model the environment using a high level of accuracy while in reactive systems usually the perception is related to the actions that the robot needs to undertake so that such complex models are not generally necessary. Regarding hybrid approaches is likewise important to understand the role that has been assigned to the perception in order to assure the success of the system. In this work a new perceptual model based on fuzzy logic is proposed to be used in a hybrid deliberative-reactive architecture. This perceptual model deals with the uncertainty and vagueness underlying to the ultrasound sensor data, it is useful to carry out the data fusion from different sensors and it allows us to establish various levels of interpretation in the sensor data. Furthermore, using this perceptual model an approximate world model can be built so that the robot can plan its motions for navigating in an office-like environment. Then the navigation is accomplished using the hybrid deliberative-reactive architecture and taking into account the perceptual model to represent the robot's beliefs about the world. Experiments in simulation and in an real office-like environment are shown for validating the perceptual model integrated into the navigation architecture.     

Cable-Direct-Driven Robot (CDDR) with Passive SCARA Support: Theory and Simulation

This article presents a new planar translational cable-direct-driven robot (CDDR) with actuation redundancy and supported against loading normal to the motion plane with a passive planar two-degree-of-freedom SCARA-type (Selective Compliance Assembly Robot Arm) serial manipulator. This allows the robot to resist cable sag without being supported on the motion plane. The proposed robot architecture may assure high payload-to-weight ratio, resistance to forces normal to the plane of motion, and a potentially large workspace. Another benefit is that the passive SCARA has structure to provide end-effector moment resistance, which is not possible with many proposed translational CDDRs. Moreover, the passive robot can also serve as an independent Cartesian metrology system. This article derives the kinematics and dynamics models for the proposed hybrid serial/parallel architecture. Additionally it proposes a dynamic Cartesian controller always ensuring positive cable tensions while minimizing the sum of all the torques exerted by the actuators. Simulation examples are also presented to demonstrate the novel CDDR concept, dynamics, and controller.Category (7) – System Modelling/Simulation/Control/Computer–Aided Design/Robot Control/Teleoperation/Moving Robots     

ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling

Reactive and effective hybrid manufacturing control architectures, combining hierarchy and heterarchy adapted to the current constraints of the industrial market and its environment were created. In this article, a new generic hybrid control architecture called ORCA (dynamic Architecture for an Optimized and Reactive Control) is first proposed. This hybrid architecture is able to dynamically and partially switch between a hierarchical predictive architecture and a heterarchical reactive architecture, if an event forbidding the planned behavior to be followed occurs. In this article, this architecture was applied to a Flexible Manufacturing System (FMS) problem and denoted ORCA-FMS. ORCA-FMS was tested on an existing manufacturing cell with simulations and real experiments to prove the applicability and the effectiveness of this kind of hybrid architecture in an industrial environment.     

基于包容结构的移动机器人混合式控制结构

本文提出以包容式框架结构为基础，将计划作为一种行为，与应激式行为并行处理 的思想．与之对应的是将传感器检测区域分为避障区和规划区，如果在避障区有障碍物，则 由应激避障模块采取相应的避障行为；如果在规划区有障碍物，则由规划模块产生相应的输 出．由于规划模块最终输出子目标，因此使此结构的规划模块具有很好的扩展性．另外，在 处理应激避障行为和直接规划行为时，分别提出新的基于传感器的避障和规划方法，通过机 器人路径规划的仿真证明，这些方法是简洁、有效的．     

一种基于行为的双层动态智能体结构及其在RoboCup中的应用

该文针对目前智能体结构模型的问题,提出了一种基于行为的双层动态智能体结构模型,它是一种混合结构模型,包括有反应式结构和慎思结构,并采用自信度来连接这两种结构,既可以提高在实时动态环境下智能体反应的敏捷性,也使自主机器人能够在动态环境下识别任务。这种混合结构模式已成功地应用于RoboCup仿真机器人足球赛中,并取得了比较好的成绩。     

基于混合式控制结构的多机器人编队控制研究

针对多机器人的编队控制问题,提出把基于Motor Schema的反应式控制结构和分层式结构结合起来,设计了一种混合式体系结构。根据反应式结构设计了四种基本行为,并提出利用模糊神经网络来设计各种行为。最后利用神经元来融合各种行为的输出作为最终的输出,控制执行器来执行动作。该方法提高了整个编队系统的智能性和环境适应性。通过仿真验证了该方法的可行性和有效性。     

一种混合Agent体系结构及决策算法

提出的混合Agent体系结构包括反应式结构和慎思结构，反应式结构提高Agent的反应性，慎思结构增强Agent的智能性；为了提高智能体决策的速度和智能性。提出一种新颖的Agent决策算法。该文提出的混合Agent体系结构及决策算法已成功地应用到RoboCup仿真机器人系统中，仿真结果说明了该结构和算法的有效性。     

Modelling Shared Attention Through Relational Reinforcement Learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human?Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.     

带电作业机器人控制系统

带电作业机器人是针对配电线路带电作业而研制开发的一种安全、可靠的带电作业设备. 介绍了带电作业机器人系统组成, 运动控制系统的软硬件实现, 最后详细介绍了系统的位置伺服和力/位置混合控制, 并且通过MATLAB仿真对两种控制方法进行了位置跟踪比较, 仿真曲线证明带有力反馈的控制效果更佳.     

ON THE ROLE OF BDI MODELING FOR INTEGRATED CONTROL AND COORDINATED BEHAVIOR IN AUTONOMOUS AGENTS

This paper describes an architecture for controlling and coordinating autonomous agents, building on previous work addressing reactive and deliberative control methods. The proposed multilayered hybrid architecture allows a rationally bounded, goal-directed agent to reason predictively about potential conflicts by constructing knowledge level models that explain other agents' observed behaviors and hypothesize their beliefs, desires, and intentions; at the same time, it enables the agent to operate autonomously, to react promptly to changes in its real-time environment, and to coordinate its actions effectively with other agents. A principal aim of this research is to understand the role dzfferent functional capabilities play in constraining an agent5 behavior under varying environmental conditions. To this end, an experimental test bed has been constructed comprising a simulated multi-agent world in which a variety of agent configurations and behaviors have been investigated. A number of experimentalfindings are reported.     

A hybrid agent architecture integrating desire,intention and reinforcement learning

This paper presents a hybrid agent architecture that integrates the behaviours of BDI agents, specifically desire and intention, with a neural network based reinforcement learner known as Temporal Difference-Fusion Architecture for Learning and COgNition (TD-FALCON). With the explicit maintenance of goals, the agent performs reinforcement learning with the awareness of its objectives instead of relying on external reinforcement signals. More importantly, the intention module equips the hybrid architecture with deliberative planning capabilities, enabling the agent to purposefully maintain an agenda of actions to perform and reducing the need of constantly sensing the environment. Through reinforcement learning, plans can also be learned and evaluated without the rigidity of user-defined plans as used in traditional BDI systems. For intention and reinforcement learning to work cooperatively, two strategies are presented for combining the intention module and the reactive learning module for decision making in a real time environment. Our case study based on a minefield navigation domain investigates how the desire and intention modules may cooperatively enhance the capability of a pure reinforcement learner. The empirical results show that the hybrid architecture is able to learn plans efficiently and tap both intentional and reactive action execution to yield a robust performance.     

基于模糊神经网络的Agent反应器结构及实现

针对复杂适应系统(ComplexAdaptiveSystem,CAS)仿真中的Agent结构建模问题,提出了一种基于模糊神经网络的Agent反应器结构及实现,以提高Agent混合模型的性能,满足复杂适应系统仿真中对Agent反应器的快速性、自适应性与准确性的要求。最后,结合跨流域调水管理复杂适应系统仿真中Agent反应器的规则存储与推理过程,进行了实例分析。     

自主机器人多智能体软件架构及伴随行为机制

自主机器人是一类由计算机软件控制的信息物理系统，如何支持该类机器人在开放环境下的有效和协调运行是自主机器人控制软件（CSAR：Control Software of Autonomous Robot）研究与实践面临的一项重要挑战.本文基于组织理论的思想，采用Structure-in-5的组织架构模式，提出了基于多智能体的CSAR的软件架构MaRSA（Multi-agent Robotic Software Architecture），通过独立抽象CSAR的行为规划、分发、执行等软构件并显式加强这些构件间的交互，从而为自主机器人行为的有效规划和协调实施奠定架构基础；提出了基于MaRSA架构的伴随行为机制，从因果性、时序性和按需性三个方面建立了机器人观察行为和任务行为间的伴随关系，并基于分步规划和动态决策的思想设计并实现了伴随行为的自主决策算法DAAB（Decision Algorithm of Accompanying Behaviors）.论文分别在仿真环境和实际机器人环境下设计了对比性实验，结果表明，与主流的反应式行为决策算法和BDI式概率决策算法相比较，基于MaRSA和伴随行为机制的DAAB算法所生成的伴随行为规划在开放环境下具有可行性和更高效的执行效率.     

从宏观和微观论多智能体系统与足球机器人系统体系结构

在半自主型的MAS组织结构的基础上,建立具有Agent、逻辑Agent与物理Agent的异构体系,从微观和宏观两种技术路线分析了多智能体系统 (MAS)的组织结构和Agent的体系结构,提出了一种足球机器人系统的分层知识、分层推理的体系结构模型,可简化推理过程,又增强推理能力,提高足球机器人的应变能力和实时性.     

Robot Cloud: Bridging the power of robotics and cloud computing

Cloud computing is shaping the cyber world and evolves as a key computing and service platform for sharing resources including platforms, software applications and everything in the form of services. This is known “X as a Service”. Although it brings our age unparalleled computing ability and economic benefits, the application of cloud computing is still limited currently in the cyberspace due to the cloud services can only reside in cloud instead of our daily life environment. In fact, there are still a plethora of physical position based on-site service demands that cloud computing could help little due to the “cyber limitation”. In this paper, we aim to integrate the cyber world and the physical world by bringing up the idea of “Robot Cloud” to bridge the power of robotics and cloud computing. To make it possible, we design a novel Robot Cloud stack to support our idea and adopt the service-oriented architecture (SOA) to make the functional modules in the Robot Cloud more flexible, extensible and reusable. Then we develop a prototype of Robot Cloud using the popular Google App Engine to demonstrate our design method. Finally, we conduct the simulation experiments with a “robot show” application scenario to evaluate our scheduling policy and identify the effect of different request distributions and robot center solutions.     

基于DCOM的分布式机器人控制

机器人的应用领域不断在扩展，为了适应机器人在非工业环境的应用，机器人 的控制逐渐向分布式结构发展．DCOM技术是Microsoft对组件化编程的扩展，能够支持在局 域网、广域网甚至Internet 上不同计算机的对象之间的通讯．本文介绍了目前该领域的研 究状况，提出了一种基于DCOM的分布式的组件机器人架构．     

Scaling for agility: A reference model for hybrid traditional-agile software development methodologies

The adoption of agility at a large scale often requires the integration of agile and non-agile development elements for architecting a hybrid adaptive methodology. The challenge is ”which elements or components (agile or non-agile) are relevant to develop the context-aware hybrid adaptive methodology reference architecture?” This paper addresses this important challenge and develops a hybrid adaptive methodology reference architecture model using a qualitative constructive empirical research approach. In this way, we have uncovered the agility, abstraction, business value, business policy, rules, legal, context and facility elements or components that have not been explicitly modelled or discussed in International Standards (IS) such as the ISO/IEC 24744 metamodel. It is anticipated that a context-aware hybrid adaptive methodology can be architected by using the proposed context-aware hybrid adaptive methodology reference architecture elements for a particular situation when using a situational method engineering approach.