基于测距声信标的深水机器人导航定位技术研究

基于水声测距的导航定位技术在水下机器人中获得广泛应用，并成为现今水下机器人技术的研究热点。不管从实际应用前景还是理论研究意义看，这项研究都具有巨大的研究价值，并极富挑战性。纯距离（Range-only）导航是对外部观测量仅有距离信息的一类导航问题的统称。本文研究了基于测距原理的“CR-02”AUV长基线定位系统的两方面问题，即长基线定位系统的应用及导航问题。 本文分别对测距系统存在不确定性和扰动情况下的接收性能、稳定性、检测延时估计以及距离修正、声传播特性对测距系统应用的影响进行了研究，设计了适用于微弱CW脉冲信号接收的高增益滤波放大器与检测电路，湖上试验和海上试验证明了声信标接收电路的优良性能。用蒙特卡洛方法对检测延时分布特征进行了统计分析，得到了检测延时的分布规律，该方法得到的检测延时结果和分布规律与实际物理实验完全吻合，为处理任意时变系统的延时特征提供了一种新的分析方法。在前人研究基础上，利用声线修正算法解决了长基线系统中的有关重要问题，这些方法在深海试验中得到成功应用，使得长基线系统更加可靠有效。提出一种新的垂线相交标定方法，根据几何原理直接求取声信标坐标。利用这个新方法，声信标位置标定精度大大提高，系统实现也相对简单，深海海上试验证明方法有效而可靠。 “CR-02”长基线定位系统仅具有对AUV的定位能力，但没有导航功能，为满足应用需要，本文以”CR-02”AUV为研究对象，在原有设备基础上扩展其导航功能，从而实现监控型AUV的导航能力。通过分析水下机器人导航系统能观性，制定了保证系统能观的机动航行策略，分别设计出水下机器人在三个声信标和固定单信标情况下的卡尔曼滤波导航方法。这些方法能够提高传统多信标系统下机器人导航的强容错能力和鲁棒性，还为水下机器人位置修正问题提供一种新方法。最后，在移动声信标条件下机器人导航问题深入分析基础上，提出主从式多UUV群体协作导航机制，给出了多UUV群体中随从UUV导航算法的计算机仿真结果。计算机仿真证明了方法的有效性和正确性。

基于实时障碍物预测的机器人运动规划

本文给出了移动机器人的虚力导航和运动规划系统．这种方法结合最小方差估计算法（ＬＭ ＳＥ）能有效地对机器人进行实时导航和避撞．在预测过程中，根据导航的不同阶段和预测误差的变化情况，采用Ｆｕｚｚｙ 规则动态地调整误差函数中的权重，使预测过程尽可能准确．导航算法的基本思想是首先通过预测算法来获得移动机器人的运动信息，然后虚力系统根据预测信息决定机器人的未来运动，仿真结果表明该方法实时性好，能准确躲避障碍物并且到达目标点

基于修正的最小均方误差分类算法的移动机器人运动规划

研究了不确定性环境下移动机器人躲避运动轨迹未知的移动障碍物的一种新方法．通过实时最小均方误差估计算法预测每个障碍物的位置及运动轨迹，并利用模式识别中最小均方误差分类器的修正模型计算出机器人的局部避障路径，再运用船舶导航中使用的操纵盘技术来确定每个导航周期中移动机器人的速．度仿真结果表明了该方法的可行性

越野移动机器人自动驾驶专家系统的研究

本文研究越野移动机器人驾驶专家系统等有关问题．首先介绍了系统的硬件支持环境，然后阐述了自动驾驶专家系统的总体结构，有关知识库的内容以及使用知识的各功能模块的作用与运行机理．该系统已部分得以应用，能够完全代替驾驶员完成各种驾驶操作，并能进行自主导航、运动规划、自动绕障、动态跟踪目标、原路返回以及示教再现等复杂任务。

无缆水下机器人组合导航系统

无缆水下机器人是我国首次开展的高技术课题,组合导航系统是无缆水下机器人研究中的关键部分,本文介绍了多种导航设备同时工作,经过信息综合处理获得高精度导航信息的组合导航系统和组合导航系统中所需要的坐标转换及卡尔曼滤波器的计算,为无缆水下机器人建立一个较理想的导航方式。

一个用于自治智能移动式机器人导航和监控的实验系统

文中重点讨论了系统实现过程中,任务分解与行走命令下达,时序分配与同步,路标定位与行走误差修正.动态障碍感知,测定与响应和特别情况紧急处理等难题的解决策略及遇到的问题.

A Robot that Walks: Emergent Behaviors from a Carefully Evolved Network

Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous one, which control a six legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented, the robot's performance and behavior repertoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical problems have been ignored.

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!

Recovering Heading for Visually-Guided Navigation

We present a model for recovering the direction of heading of an observer who is moving relative to a scene that may contain self-moving objects. The model builds upon an algorithm proposed by Rieger and Lawton (1985), which is based on earlier work by Longuet-Higgens and Prazdny (1981). The algorithm uses velocity differences computed in regions of high depth variation to estimate the location of the focus of expansion, which indicates the observer's heading direction. We relate the behavior of the proposed model to psychophysical observations regarding the ability of human observers to judge their heading direction, and show how the model can cope with self-moving objects in the environment. We also discuss this model in the broader context of a navigational system that performs tasks requiring rapid sensing and response through the interaction of simple task-specific routines.

Robust Agent Control of an Autonomous Robot with Many Sensors and Actuators

This thesis presents methods for implementing robust hexpod locomotion on an autonomous robot with many sensors and actuators. The controller is based on the Subsumption Architecture and is fully distributed over approximately 1500 simple, concurrent processes. The robot, Hannibal, weighs approximately 6 pounds and is equipped with over 100 physical sensors, 19 degrees of freedom, and 8 on board computers. We investigate the following topics in depth: distributed control of a complex robot, insect-inspired locomotion control for gait generation and rough terrain mobility, and fault tolerance. The controller was implemented, debugged, and tested on Hannibal. Through a series of experiments, we examined Hannibal's gait generation, rough terrain locomotion, and fault tolerance performance. These results demonstrate that Hannibal exhibits robust, flexible, real-time locomotion over a variety of terrain and tolerates a multitude of hardware failures.

Error Detection and Recovery for Robot Motion Planning with Uncertainty

Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions.

Description and Theoretical Analysis (Using Schemata) of Planner: A Language for Proving Theorems and Manipulating Models in a Robot

Planner is a formalism for proving theorems and manipulating models in a robot. The formalism is built out of a number of problem-solving primitives together with a hierarchical multiprocess backtrack control structure. Statements can be asserted and perhaps later withdrawn as the state of the world changes. Under BACKTRACK control structure, the hierarchy of activations of functions previously executed is maintained so that it is possible to revert to any previous state. Thus programs can easily manipulate elaborate hypothetical tentative states. In addition PLANNER uses multiprocessing so that there can be multiple loci of changes in state. Goals can be established and dismissed when they are satisfied. The deductive system of PLANNER is subordinate to the hierarchical control structure in order to maintain the desired degree of control. The use of a general-purpose matching language as the basis of the deductive system increases the flexibility of the system. Instead of explicitly naming procedures in calls, procedures can be invoked implicitly by patterns of what the procedure is supposed to accomplish. The language is being applied to solve problems faced by a robot, to write special purpose routines from goal oriented language, to express and prove properties of procedures, to abstract procedures from protocols of their actions, and as a semantic base for English.

A Planning System for Robot Construction Tasks

This paper describes BUILD, a computer program which generates plans for building specified structures out of simple objects such as toy blocks. A powerful heuristic control structure enables BUILD to use a number of sophisticated construction techniques in its plans. Among these are the incorporation of pre-existing structure into the final design, pre-assembly of movable sub-structures on the table, and use of the extra blocks as temporary supports and counterweights in the course of construction. BUILD does its planning in a modeled 3-space in which blocks of various shapes and sizes can be represented in any orientation and location. The modeling system can maintain several world models at once, and contains modules for displaying states, testing them for inter-object contact and collision, and for checking the stability of complex structures involving frictional forces. Various alternative approaches are discussed, and suggestions are included for the extension of BUILD-like systems to other domains. Also discussed are the merits of BUILD's implementation language, CONNIVER, for this type of problem solving.

Motion Planning with Six Degrees of Freedom

The motion planning problem is of central importance to the fields of robotics, spatial planning, and automated design. In robotics we are interested in the automatic synthesis of robot motions, given high-level specifications of tasks and geometric models of the robot and obstacles. The Mover's problem is to find a continuous, collision-free path for a moving object through an environment containing obstacles. We present an implemented algorithm for the classical formulation of the three-dimensional Mover's problem: given an arbitrary rigid polyhedral moving object P with three translational and three rotational degrees of freedom, find a continuous, collision-free path taking P from some initial configuration to a desired goal configuration. This thesis describes the first known implementation of a complete algorithm (at a given resolution) for the full six degree of freedom Movers' problem. The algorithm transforms the six degree of freedom planning problem into a point navigation problem in a six-dimensional configuration space (called C-Space). The C-Space obstacles, which characterize the physically unachievable configurations, are directly represented by six-dimensional manifolds whose boundaries are five dimensional C-surfaces. By characterizing these surfaces and their intersections, collision-free paths may be found by the closure of three operators which (i) slide along 5-dimensional intersections of level C-Space obstacles; (ii) slide along 1- to 4-dimensional intersections of level C-surfaces; and (iii) jump between 6 dimensional obstacles. Implementing the point navigation operators requires solving fundamental representational and algorithmic questions: we will derive new structural properties of the C-Space constraints and shoe how to construct and represent C-Surfaces and their intersection manifolds. A definition and new theoretical results are presented for a six-dimensional C-Space extension of the generalized Voronoi diagram, called the C-Voronoi diagram, whose structure we relate to the C-surface intersection manifolds. The representations and algorithms we develop impact many geometric planning problems, and extend to Cartesian manipulators with six degrees of freedom.