基于功能／行为集成的自主式移动机器人进化控制体系结构

本文针对自主式移动机器人控制体系结构中两类典型方法的不足,提出了一种进化控制体系 结构,实现了基于AI模型的方法与基于行为的方法的优势互补．文中阐述了进化控制的思想 ,并应用于移动机器人控制结构的设计．该结构使移动机器人具备良好的学习和适应能力、 较快的响应速度,同时拥有较好的理性和完成给定任务的能力．     

自主移动机器人体系结构状况与发展研究

阐述了自主移动机器人体系结构的意义,并对分层式体系结构和包容式体系结构这两种典型体系结构详细比较分析后,在此基础上研究了基于两者优点的混合型体系结构。最后,对该领域的研究热点进行了阐述,并对未来的研究工作提出了建议。     

移动机器人的环境建模、路径规划与运动学计算

移动机器人的环境建模、路径规划与运动学计算曲效利（中国科学院沈阳自动化研究所）ＯＳＭＯＲ是一个开放式的移动机器人体系结构，本文介绍了在该体系结构中的环境建模、路径规划、运动学计算三部分的实现方法。环境建模：介绍了一种适用于结构环境的建模方法。该方法将...     

异质多移动机器人协同技术研究的进展

随着移动机器人应用的领域和范围的不断扩展，多移动机器人由于其单个机器人无法比拟的优越性已经越来越受到重视．从体系结构、协作与协调、协作环境感知与定位、重构及机器学习几个重要课题对多移动机器人协同技术进行了综述，尤其侧重于各种技术如何处理和包容团队中的异质性，并分析了本领域中的研究难点问题，最后展望了异质多移动机器人研究的前景与发展趋势．     

自主移动机器人混合式体系结构的一种Multi-agent实现方法

提出了一种简单实用的方法，优化了基于MAS的自主移动机器人系统在紧急状态下的性能．传统的基于MAS的体系结构在处理紧急事件时系统导航延迟时间较长．该方法将传统的基于MAS的体系结构修改为混合式体系结构，定义了紧急事件集合和紧急状态集合，改进了离散事件状态模型，实现了定序器Agent．实验结果表明，这种方法缩短了系统导航延迟时间，提高了系统的智能性和安全性．     

一种智能巡检机器人的体系结构分析与设计

介绍了一种变电站巡检智能移动机器人基于模块设计思想的软硬件体系结构,给出了基于该体系结构的车体运动学建模和避障算法．通过机器人的现场运行验证了该体系结构的合理性以及可行性．     

半自主侦察机器人的控制体系结构研究

本文针对半自主侦察机器人发展与任务需求，总结其设计过程中的关键技术，并提出了一种应用于半自主侦察机器人的基于模块化的控制体系结构。该结构是以分层递阶控制为主体系统结构，引入了基于模块化分布式电源管理系统，并在传感／底层决策层采用了基于包容式体系结构。最后，通过三种地面移动机器人验证了此控制体系结构的可行性与优越性。     

多移动机器人系统个体控制体系结构

本文面向多移动机器人系统,提出了一种适合于移动机器人个体的分层式体系结构, 包括系统监控层、协作规划层和行为控制层三个层次．其中系统监控层主要实现人对系统的 实时监控功能;协作规划层在与其它机器人相应层的交互过程中建立系统的分层式组织形式 ,合理快速地完成任务的分解和分配,实现了机器人之间的任务级协作;行为控制层主要采 用基于行为的方法实现具体的运动控制．该结构满足了移动机器人渐趋复杂的应用环境和日 益增大的系统规模的要求．     

自主移动机器人混合式体系结构的设计与实现

针对以往自主移动机器人体系结构在实时处理方面的不足，提出了一种基于多智能体的混合式体系结构，统一规划了机器人系统的软硬件结构，在该体系结构中设计并实现了协调Agent和推理Agent两种智能体，针对紧急事件进行了更实时的Agent实现，有效的提高的自主移动机器人在突发事件时的实时性，提出了使用多样化的信息组织形式，增强了系统的自适应能力和易扩展性。本文的实验结果表明在紧急状态下，系统的反应时间有效缩短，增加了系统的智能性和实时性。     

多移动机器人队形控制的研究方法

本文从基本思想、优缺点等方面论述了进行多移动机器人队形控制的三种研究方法.介绍了 一种包含这三种方法的系统体系结构，并对该体系结构进行了评价，指出了需要进一步研究 的问题．     

Classification and retrieval of knowledge on a parallelmarker-passing architecture

Frame-based systems or semantic networks have been generally used for knowledge representation. In such a knowledge representation system, concepts in the knowledge base are organized based on the subsumption relation between concepts, and classification is a process of constructing a concept hierarchy according to the subsumption relationships. Since the classification process involves search and subsumption test between concepts, classification on a large knowledge base may become unacceptably slow, especially for real-time applications. In this paper, a massively parallel classification and property retrieval algorithm on a marker passing architecture is presented. The subsumption relation is first defined by using the set relationship, and the parallel classification algorithm is described based on that relationship. In this algorithm, subsumption test between two concepts is done by parallel marker passing and multiple subsumption tests are performed simultaneously. To investigate the performance of the algorithm, time complexities of sequential and parallel classification are compared. Simulation of the parallel classification algorithm was performed using the SNAP (Semantic Network Array Processor) simulator, and the influence of several factors on the execution time is discussed     

Object‐oriented design of the subsumption architecture

The subsumption architecture is a layered mediator invented by Rodney Brooks for behaviour‐based control of robots. The layers are minimally dependent and use minimal communication. We develop an object‐oriented software design for the subsumption architecture, and demonstrate that each layer can be used as a slot for a set of plug‐and‐play components that implement different microstrategies for achieving a particular goal. Guidelines for the development of specific layers and components of a subsumption architecture are also presented. Copyright © 2001 John Wiley & Sons, Ltd.     

Supervenient hierarchies of behaviours-from robot vacuuming to space telerobotics

In this paper we describe the use of behaviour hierarchies based on ‘merging’ two models of multi-layer architecture—the supervenience model and the subsumption model. The behaviour hierarchy approach allows us to use the robustness of reactivity in behaviour design. It also encourages the design of modular behaviours that can be reused or more importantly recalibrated in different situations. We argue that behaviour hierarchies extend our ability to design and programme effective solutions that combine reactive and goal-driven components, but do not require any explicit planning. This work is used for two implemented systems in which autonomous mobile robots perform a vacuuming task and object tracking in support of a space telerobotics system.     

A methodology for provably stable behaviour-based intelligent control

This paper presents a design methodology for a class of behaviour-based control systems, arguing its potential for application to safety critical systems. We propose a formal basis for subsumption architecture design based on two extensions to Lyapunov stability theory, the Second Order Stability Theorems, and interpretations of system safety and liveness in Lyapunov stability terms. The subsumption of the new theorems by the classical stability theorems serves as a model of dynamical subsumption, forming the basis of the design methodology. Behaviour-based control also offers the potential for using simple computational mechanisms, which will simplify the safety assurance process.     

INTELLIGENT SENSOR FUSION AND LEARNING FOR AUTONOMOUS ROBOT NAVIGATION

ABSTRACT

This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behaviors such as target collecting in the ever-changing physical environment.     

Adaptive dynamic walking of a quadruped robot using a neural system model

We are trying to induce a quadruped robot to walk dynamically on irregular terrain by using a neural system model. In this paper, we integrate several reflexes, such as a stretch reflex, a vestibulospinal reflex and extensor/flexor reflexes, into a central pattern generator (CPG). We try to realize adaptive walking up and down a slope of 12°, walking over an obstacle 3 cm in height, and walking on terrain undulation consisting of bumps 3 cm in height with fixed parameters of CPGs and reflexes. The success in walking on such irregular terrain in spite of stumbling and landing on obstacles shows that the control method using a neural system model proposed in this study has the ability for autonomous adaptation to unknown irregular terrain. In order to clarify the role of a CPG, we investigate the relation between parameters of a CPG and the mechanical system by simulations and experiments. CPGs can generate stable walking suitable for the mechanical system by receiving inhibitory input as sensory feedback and generate adaptive walking on irregular terrain by receiving excitatory input as sensory feedback. MPEG footage of these experiments can be seen at: http://www.kimura.is.uec.ac.jp.     

Stability overlay for adaptive control laws

This paper proposes an architecture referred to as Stability Overlay (SO) for adaptive control of a class of nonlinear time-varying plants. The SO can be implemented in parallel with a wide range of “performance-based” adaptive control laws, i.e., adaptive control laws that seek to improve closed-loop performance, but may be susceptible to instability in the presence of unaccounted model uncertainty. In this architecture, the performance-based adaptive control law designates candidate controllers based on performance considerations, while the SO supervises this selection based upon online robust stability considerations. A particular selection of a performance-based adaptive control law is not specified. Rather, this selection can be from a wide range of adaptive control schemes. This paper provides stability proofs for the SO architecture and presents a simulation that illustrates the applicability of the proposed method.     

The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause

Searching the hypothesis space bounded below by a bottom clause is the basis of several state-of-the-art ILP systems (e.g. Progol, Aleph). These systems use refinement operators together with search heuristics to explore a bounded hypothesis space. It is known that the search space of these systems is limited to a sub-graph of the general subsumption lattice. However, the structure and properties of this sub-graph have not been properly characterised. In this paper firstly, we characterise the hypothesis space considered by the ILP systems which use a bottom clause to constrain the search. In particular, we discuss refinement in Progol as a representative of these ILP systems. Secondly, we study the lattice structure of this bounded hypothesis space. Thirdly, we give a new analysis of refinement operators, least generalisation and greatest specialisation in the subsumption order relative to a bottom clause. The results of this study are important for better understanding of the constrained refinement space of ILP systems such as Progol and Aleph, which proved to be successful for solving real-world problems (despite being incomplete with respect to the general subsumption order). Moreover, characterising this refinement sub-lattice can lead to more efficient ILP algorithms and operators for searching this particular sub-lattice. For example, it is shown that, unlike for the general subsumption order, efficient least generalisation operators can be designed for the subsumption order relative to a bottom clause.     

利用自适应假近邻方法优化卷积神经网络的图像目标分类算法

为了能够在处理不同的数据类型或任务时得到良好的结果,设计了基于自适应假近邻方法的卷积神经网络（CNN）架构。将中心矩的思想应用在CNN的池化操作中,利用稀疏滤波算法实现训练过程的无监督化,并设置CNN算法的卷积掩模（卷积核）的大小和每层卷积单位（CNN神经元）的数量;此外,该架构还利用自适应假近邻方法实现了简化建模和预测等任务。实验结果证实,提出的改进CNN架构的复杂度较低,它可以更快地接受训练并且不易产生过度拟合。