面向装配作业的多机器人合作协调系统

分布、自主、协调与合作是多机器人系统的发展趋势 ．本文作者在研究易于协调合作的多机器人系统的基础上,采用分层递阶和多Agent概念, 构造了一个装配系统——MROCAS系统．该系统具有任务自动建模分解,快速重组、良好柔性 、友好人机界面,各机器人具有一定自主能力等特点．它实现了在较复杂环境下快速完成装 配作业．     

Solving function distribution and behavior design problem for cooperative object handling by multiple mobile robots

This paper addresses the function distribution and behavior design problem for a multirobot system which incorporates a behavior-based dynamic cooperation strategy for object handling. The proposed multiple robot system is composed of a managing robot and homogeneous behavior-based robots. The cooperation strategy in this system is realized in two steps: designing the distributed robot's cooperative behavioral attributes according to the robot's abilities, and organizing these behavioral attributes so that team cooperation is realized. For indicating an incremental style of local behavior construction, an advanced design of cooperative behavior for coping with unknown disturbance is addressed. Additionally, two extended cooperation strategies designed for a path tracking task are described. These three strategies are based on the same concept on performing manipulation in coordination. Therefore, by considering the function distribution among the managing robot and worker robots, and considering behavior design of each worker robot, the proposed system is able to achieve the object handling task with different performances according to the task requirement, such as with or without path tracking and with or without contact with the environment. Experimental results demonstrate the applicability of the proposed system.     

Distributed optimization of tactical actions by mobile intelligent agents

In this article we compare the performance of a learning controller with and without a flexible and incrementally expandable distribution scheme. The learning method is modeled after Holland's learning classifier system, but it uses Q-learning as the reinforcement method instead of the “bucket-brigade.” The method for distributing the learning among sub-processes is modular, so that learning can be accomplished monolithically, as a redundant system of peers, or as a conceptual hierarchy of competence levels with varying degrees of abstraction. They can also communicate using short messages transmitted quickly between modules running in separate processes, on separate machines, or across a network connection. The system is applied to a mobile agent whose task is to defend itself against other mobile agents trying to attack it. As a simulation example, we consider a naval combat scenario wherein a surface vessel is considered as a mobile root with various radar and communications sensors. Its task is to detect other agents such as other ships and planes, identify and classify them, and devise optimal tactical decision policies. The results suggest that the learning method employed is useful in general for any sensor-based autonomous robot in dynamic, unstructured environments. © 1997 John Wiley & Sons, Inc.     

Adaptive task assignment for multiple mobile robots via swarm intelligence approach

This paper describes an adaptive task assignment method for a team of fully distributed mobile robots with initially identical functionalities in unknown task environments. A hierarchical assignment architecture is established for each individual robot. In the higher hierarchy, we employ a simple self-reinforcement learning model inspired by the behavior of social insects to differentiate the initially identical robots into “specialists” of different task types, resulting in stable and flexible division of labor; on the other hand, in dealing with the cooperation problem of the robots engaged in the same type of task, Ant System algorithm is adopted to organize low-level task assignment. To avoid using a centralized component, a “local blackboard” communication mechanism is utilized for knowledge sharing. The proposed method allows the robot team members to adapt themselves to the unknown dynamic environments, respond flexibly to the environmental perturbations and robustly to the modifications in the team arising from mechanical failure. The effectiveness of the presented method is validated in two different task domains: a cooperative concurrent foraging task and a cooperative collection task.     

Dynamic visual servo control of a 4-axis joint tool to track image trajectories during machining complex shapes

A large part of the new generation of computer numerical control systems has adopted an architecture based on robotic systems. This architecture improves the implementation of many manufacturing processes in terms of flexibility, efficiency, accuracy and velocity. This paper presents a 4-axis robot tool based on a joint structure whose primary use is to perform complex machining shapes in some non-contact processes. A new dynamic visual controller is proposed in order to control the 4-axis joint structure, where image information is used in the control loop to guide the robot tool in the machining task. In addition, this controller eliminates the chaotic joint behavior which appears during tracking of the quasi-repetitive trajectories required in machining processes. Moreover, this robot tool can be coupled to a manipulator robot in order to form a multi-robot platform for complex manufacturing tasks. Therefore, the robot tool could perform a machining task using a piece grasped from the workspace by a manipulator robot. This manipulator robot could be guided by using visual information given by the robot tool, thereby obtaining an intelligent multi-robot platform controlled by only one camera.     

足球机器人混合分层控制体系结构

为进一步提高足球机器人队伍在动态环境下的整体比赛能力,针对目前普遍存在的队伍体系结构过于偏向协作或应急的问题,对传统的慎思主体分层模型和算法作了较大改进.提出了混合分层模型,模型层次分明,兼备中短期任务规划能力和紧急情况下的应急能力,而且易于移植和扩展,最后通过仿真实验验证了其正确性和有效性.     

基于聚类分析和运动描述语言的扑翼飞行机器人行为规划

为了解决扑翼飞行机器人实时控制过程中操作者工作量大、操作较为复杂的难题,实现扑翼飞行机器人的分布式智能控制,提出了基于聚类分析和运动描述语言的扑翼飞行机器人行为规划方法.利用扑翼飞行机器人飞行数据聚类分析的结果,将机器人运动行为进行合理分类.在保证了运动描述语言的基元关系的同时,合理提取了扑翼飞行机器人的行为特征,并针对扑翼飞行机器人绕杆任务定义了4类运动基元.以扑翼飞行机器人和机载陀螺仪搭建了扑翼飞行机器人实验系统.通过直接控制方法和基于运动描述语言的机器人行为规划方法进行了实物实验和仿真实验,实验结果验证了所提方法的可行性和有效性.     

Robot control code generation by task demonstration in a dynamic environment

Within mobile robotics, one of the most dominant relationships to consider when implementing robot control code is the one between the robot’s sensors and its motors. When implementing such a relationship, efficiency and reliability are of crucial importance. The latter aspects often prove challenging due to the complex interaction between a robot and the environment in which it exists, frequently resulting in a time consuming iterative process where control code is redeveloped and tested many times before obtaining an optimal controller. In this paper, we address this challenge by implementing an alternative approach to control code generation, which first identifies the desired robot behaviour and represents the sensor-motor task algorithmically through system identification using the NARMAX modelling methodology. The control code is generated by task demonstration, where the sensory perception and velocities are logged and the relationship that exists between them is then modelled using system identification. This approach produces transparent control code through non-linear polynomial equations that can be mathematically analysed to obtain formal statements regarding specific inputs/outputs. We demonstrate this approach to control code generation and analyse its performance in dynamic environments.     

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．     

Motivation-Based Dependable Behavior Selection Using Probabilistic Affordance

Abstract

In this paper, we generate probabilistic affordances to select a dependable behavior based on motivation values. Dependable behavior, in our context, refers to behavior that is situation-adequate as well as goaloriented. The probabilistic affordance is designed as a multilayer naïve Bayesian classifier with respect to uncertainties and reusability. A multilayer naïve Bayesian classifier is a probabilistic model with multiple layers comprising conditional probability tables or probability distributions based on equivalence classes. The affordances consider situation-adequateness in given situations and suggest possibilities of behaviors based on Bayesian inference. In order to select a dependable behavior to achieve a task, the affordances are arranged based on a sequential structure. This is because accomplishing a task usually requires sequentially performed behaviors. Motivation values are generated using the arranged affordances and a motivation value propagation algorithm. A robot selects a dependable behavior based on these motivation values. To validate our proposed methods, we present experimental results of an entertainment robot called AIBO handling three tasks.     

An active hybrid parallel robot for minimally invasive surgery

During the last years, there has been an increase in research in the field of medical robots. This trend motivated the development of a new robotics field called “robotic-assisted minimally invasive surgery”. The paper presents the kinematic and dynamic behavior of a parallel hybrid surgical robot PARASURG-9M. The robot consists of two subsystems: a surgical robotic arm, PARASURG 5M with five motors, and an active robotized surgical instrument PARASIM with four motors. The methodology for the robot kinematics is presented and the algorithm for robot workspace generation is described. PARASURG-9M inverse dynamic simulation is performed using MSC Adams and finally some numerical and simulation results of the developed experimental model with its system control are also described.     

Real-Time Motion Planning for a Volleyball Robot Task Based on a Multi-Agent Technique

This paper takes the volleyball robot task as an example of a dynamic manipulation task and presents a multi-agent-based motion-planning framework for it. Each subtask in the motion planning is defined as an agent. The motion planning is accomplished by the solution of each agent activated by a blackboard. It is convenient to extend the module and enable real-time control compared to the hierarchy and subsumption architecture. In the solution of each subtask, one difference from the motion planning of other ball-playing robots is that simple fuzzy rules are adopted to determine the hitting position, which leads to smaller hitting velocities beneficial for task control. The other difference is that the task-based directional manipulability measure (TDMM) is used to optimize the robot configuration on the basis of optimal hitting path, so as to improve the manipulating ability on task direction. The simulation of a planar three-link manipulator ball-hitting task is implemented by the MATLAB/Simulink tool. The virtual target of the hitting motion is validated on the ABB manipulator by the ball-catching experiment.     

Codevelopmental learning between human and humanoid robot using a dynamic neural-network model.

This paper examines characteristics of interactive learning between human tutors and a robot having a dynamic neural-network model, which is inspired by human parietal cortex functions. A humanoid robot, with a recurrent neural network that has a hierarchical structure, learns to manipulate objects. Robots learn tasks in repeated self-trials with the assistance of human interaction, which provides physical guidance until the tasks are mastered and learning is consolidated within the neural networks. Experimental results and the analyses showed the following: 1) codevelopmental shaping of task behaviors stems from interactions between the robot and a tutor; 2) dynamic structures for articulating and sequencing of behavior primitives are self-organized in the hierarchically organized network; and 3) such structures can afford both generalization and context dependency in generating skilled behaviors.     

Interactive Teaching for Vision-Based Mobile Robots: A Sensory-Motor Approach

For the last decade, we have been developing a vision-based architecture for mobile robot navigation. Using our bio-inspired model of navigation, robots can perform sensory-motor tasks in real time in unknown indoor as well as outdoor environments. We address here the problem of autonomous incremental learning of a sensory-motor task, demonstrated by an operator guiding a robot. The proposed system allows for semisupervision of task learning and is able to adapt the environmental partitioning to the complexity of the desired behavior. A real dialogue based on actions emerges from the interactive teaching. The interaction leads the robot to autonomously build a precise sensory-motor dynamics that approximates the behavior of the teacher. The usability of the system is highlighted by experiments on real robots, in both indoor and outdoor environments. Accuracy measures are also proposed in order to evaluate the learned behavior as compared to the expected behavioral attractor. These measures, used first in a real experiment and then in a simulated experiment, demonstrate how a real interaction between the teacher and the robot influences the learning process.     

双臂搬运机器人反应式导航控制系统设计

为增强双臂搬运机器人在作业任务过程中的行进避障能力,使其运动行为得到连续有效控制,设计双臂搬运机器人的反应式导航控制系统。根据单片机与电机电路的连接形式,选择合适的ARM微处理器元件与PIC单片机结构,再联合HN-9移动平台、智能导航平台、ROS操作平台,完善反应式导航子模块的运行能力,实现控制系统的硬件单元设计。求取绝对位姿向量、相对位姿向量的计算结果,以此作为自变量系数,确定速度雅可比指标,并推断得出动力学递推表达式,完成对双臂搬运机器人的协调控制,联合相关硬件应用结构,实现双臂搬运机器人反应式导航控制系统的设计。对比实验结果：反应式导航控制系统可使机器人准确躲避行进障碍物,且躲避过程中机器人完成作业任务的能力不会受到影响,符合连续有效控制机器人搬运行为的实际应用需求。     

基于产生式系统和轨迹优化的巡线机器人控制

阐述了一种新型的基于产生式系统控制方法的高压输电线路自动巡线机器人的嵌入式控制系统及其实现.提出了一种基于人工控制规则库和分层规划的产生式系统的控制策略.该控制策略通过人工控制机器人的方法,建立相应的专家规则库,再采用分层规划的方式与手爪的自动微调相结合来实现机器人的自动行走.最后,通过最优算法并结合机器人各个关节伺服系统的运作特性,对机器人运动轨迹进行优化,实现了对巡线机器人的最优控制.     

机器人班组的自组织递阶控制

研究了机器人班组在执行各种任务时的协调控制.由于机器人班组是由多个能力有限的 机器人组成的,被操作的对象可以是一个刚体、柔性体或机械系统,而且需要跟踪的运动也可 以是各种各样的,所以整个系统是相当复杂的.这样的机械系统,按其力学性质可以将要实现 的控制任务加以分解,从而实现递阶控制,各层的控制只完成被分解出来的特定的较简单的任 务,而各机器人之间的协调由自组织算法自动完成.     

Training a Vision Guided Mobile Robot

This paper presents the design, implementation and evaluation of a trainable vision guided mobile robot. The robot, CORGI, has a CCD camera as its only sensor which it is trained to use for a variety of tasks. The techniques used for training and the choice of natural light vision as the primary sensor makes the methodology immediately applicable to tasks such as trash collection or fruit picking. For example, the robot is readily trained to perform a ball finding task which involves avoiding obstacles and aligning with tennis balls. The robot is able to move at speeds up to 0.8 ms^-1 while performing this task, and has never had a collision in the trained environment. It can process video and update the actuators at 11 Hz using a single $20 microprocessor to perform all computation. Further results are shown to evaluate the system for generalization across unseen domains, fault tolerance and dynamic environments.     

微操作机器人中基于原语的智能控制方法

本文提出了一种基于原语的微操作机器人智能控制方法．首先，给出了面向对象的软件体系 结构；然后，在智能机器人分层递阶的层次结构基础上，提出了原语控制的概念，通过原语 函数对系统模块的二次封装，把机器人任务简单抽象为任务原语序列，实现了基于原语的机 器学习和示教再现．实验证明，基于原语的控制方法可以有效地提高微操作机器人的智能和 自动化程度．