双机械臂协调系统的一类最优轨迹规划方法

自治式水下机器人回收系统的研究与设计

本文介绍了我们承担研制的国家“８６３”计划１０００ｍ及６０００ｍ无缆水下机器人的回收系统．回收系统在４级海况下不用专用母船能够成功地回收水下机器人，依据母船、海况、水下机器人及其他具体情况，介绍了两种不同的回收方案和回收器，经海上试验证明是有效和可行的。

最优控制具有非完整性的两轮驱动机器人小车

研究了移动机器人反馈控制问题．这里所考虑的机器人是一个两轮驱动的具有非完整性的移动机器人小车．考虑了笛卡儿空间中轨线跟踪问题的扩展．且表明只要参考小车保持运动，在虚设的参考小车位形周围的小车位形的稳定成为可能．提出了最优控制律并给出了仿真结果。

智能机器人分层系统中的人机交互

在非结构环境里工作的智能机器人,人智能的介入有时是必不可少的.本文研究了这类机器人系统人机接口某些问题,提出了人智能输入的分层输入原则,实时性原则和协调性原则,讨论了人机接口的结构和实现问题,给出了人机接口命令的一种格式,并提出了人机操作的实际例子.

双机器人协调控制系统

本文描述了基于集散控制原理,采用分层递阶结构建立的一个3级结构双机器人协调控制系统.该系统共分3层,最高层为任务规划级.它对双机器人作业进行离线编程及仿真,并负责对整个系统的监控和管理.第2层为协调级,它实现机器人作业的协调控制.并通过传感器信息对机器人轨迹进行实时修正.该层既可接收来自上层的命令或指令.对机器人进行控制,亦可在该层上独立编程.实现对机器人的控制.第3层为执行级,它实现高精度伺服控制.此外,该系统还提供了灵活的编程环境及良好的人机界面.用户可根据需要选择离线编程环境或实时编程环境,并且可在其相应的界面下工作,运行结果表明,系统性能稳定可靠,结构合理,编程环境灵活.我们在该系统上,成功地进行了PUMA562机器人和PUMA760机器人的点位协调和轨迹协调以及复杂的联合作业.

面向任务的机器人语言的实现

本文提出的面向任务的机器人语言是为移动机器人提供的一种编程工具。利用环境描述语言和其他灵活的人机对话方式建立环境模型;根据各种不同的性能指标,规划出最佳路径;根据安全等因素自动规划出最佳路径上的行走参数;对来自传感器的信息进行处理,以确定机器人实际位置及障碍情况;引导机器人沿规划好的路径行走,纠正偏差或重新选择避障路径。

监控式遥控机器人初探

本文初步探讨了遥控机器人监控的基本概念,得出这样一种认识:监控方式是机器人向智能化发展的一个恰当模式,监控系统是由人的高级智能与机器人的低级智能构成的系统.监控是系统中这两种智能相互作用的过程,人的智能应当能够在机器智能的不同级别上输入,我们研制了一个遥控机器人监控操作器的实验系统,通过实例和实验作了说明.

智能机器人基于作业子空间划分的自学习自适应控制方法

现有的机器人自适应控制基本上都是在建立机器人线性化的动力学模型的基础上,采用某种显式或隐式参数辨识的方法,在线地修正控制作用.本文针对机器人运动和动力学参数变化的固有特点,提出一种完全不同的自学习自适应方法.这种方法基于智能机器人分级系统中的两级结构,并且在空间域里而不是在时间域里处理机器人参数的变化.把机器人的作业空间划分成子空间,其中包括重力载荷的作用,每个子空间对应一组控制器.规划的轨迹映射到作业空间形成子空间序列.用自学习方法选择与这个序列对应的最佳控制器序列.该方法算法简单,计算量小.避开了通常的自适应方法遇到的一系列困难问题.

一个简单的机器人视觉引导装配实验系统

本文对视觉控制下的一个简单实验室装配系统作了介绍,讨论了系统组成、机器人控制、二维图象特征的提取、对物体自动识别、定位定向、系统标定、实现垒积木装配工作.本实验系统用的是我所研制的国内第一台示教再现机器人.

海洋机器人的闭环控制与流体动力学模型

本文给出一开启式海洋机器人的水平面运动与垂直面运动的流体动力学模型,闭环控制,解耦设计和仿真结果。本文对海洋机器人控制系统设计,有一定参考价值。

Battling Reality

In the four years that the MIT Mobile Robot Project has benn in existence, we have built ten robots that focus research in various areas concerned with building intelligent systems. Towards this end, we have embarked on trying to build useful autonomous creatures that live and work in the real world. Many of the preconceived notions entertained before we started building our robots turned out to be misguided. Some issues we thought would be hard have worked successfully from day one and subsystems we imagined to be trivial have become tremendous time sinks. Oddly enough, one of our biggest failures has led to some of our favorite successes. This paper describes the changing paths our research has taken due to the lessons learned from the practical realities of building robots.

Olympic Robot Building Manual

The 1989 AI Lab Winter Olympics will take a slightly different twist from previous Olympiads. Although there will still be a dozen or so athletic competitions, the annual talent show finale will now be a display not of human talent, but of robot talent. Spurred on by the question, "Why aren't there more robots running around the AI Lab?", Olympic Robot Building is an attempt to teach everyone how to build a robot and get them started. Robot kits will be given out the last week of classes before the Christmas break and teams have until the Robot Talent Show, January 27th, to build a machine that intelligently connects perception to action. There is no constraint on what can be built; participants are free to pick their own problems and solution implementations. As Olympic Robot Building is purposefully a talent show, there is no particular obstacle course to be traversed or specific feat to be demonstrated. The hope is that this format will promote creativity, freedom and imagination. This manual provides a guide to overcoming all the practical problems in building things. What follows are tutorials on the components supplied in the kits: a microprocessor circuit "brain", a variety of sensors and motors, a mechanical building block system, a complete software development environment, some example robots and a few tips on debugging and prototyping. Parts given out in the kits can be used, ignored or supplemented, as the kits are designed primarily to overcome the intertia of getting started. If all goes well, then come February, there should be all kinds of new members running around the AI Lab!

The 1990 AI Fair

This year, as the finale to the Artificial Intelligence Laboratory's annual Winter Olympics, the Lab staged an AI Fair ??night devoted to displaying the wide variety of talents and interests within the laboratory. The Fair provided an outlet for creativity and fun in a carnival-like atmosphere. Students organized events from robot boat races to face-recognition vision contests. Research groups came together to make posters and booths explaining their work. The robots rolled down out of the labs, networks were turned over to aerial combat computer games and walls were decorated with posters of zany ideas for the future. Everyone pitched in, and this photograph album is a pictorial account of the fun that night at the AI Fair.

Vision by Man and Machine

The development of increasingly sophisticated and powerful computers in the last few decades has frequently stimulated comparisons between them and the human brain. Such comparisons will become more earnest as computers are applied more and more to tasks formerly associated with essentially human activities and capabilities. The expectation of a coming generation of "intelligent" computers and robots with sensory, motor and even "intellectual" skills comparable in quality to (and quantitatively surpassing) our own is becoming more widespread and is, I believe, leading to a new and potentially productive analytical science of "information processing". In no field has this new approach been so precisely formulated and so thoroughly exemplified as in the field of vision. As the dominant sensory modality of man, vision is one of the major keys to our mastery of the environment, to our understanding and control of the objects which surround us. If we wish to created robots capable of performing complex manipulative tasks in a changing environment, we must surely endow them with (among other things) adequate visual powers. How can we set about designing such flexible and adaptive robots? In designing them, can we make use of our rapidly growing knowledge of the human brain, and if so, how at the same time, can our experiences in designing artificial vision systems help us to understand how the brain analyzes visual information?

Evolving Robocode Tank Fighters

In this paper, I describe the application of genetic programming to evolve a controller for a robotic tank in a simulated environment. The purpose is to explore how genetic techniques can best be applied to produce controllers based on subsumption and behavior oriented languages such as REX. As part of my implementation, I developed TableRex, a modification of REX that can be expressed on a fixed-length genome. Using a fixed subsumption architecture of TableRex modules, I evolved robots that beat some of the most competitive hand-coded adversaries.