一种电脑鼠走迷宫算法的设计与实现

电脑鼠是一个自主移动机器人系统.电脑鼠的任务是到达迷宫中心的目标区域.电脑鼠在不知道迷宫的布局情况下,必须自己找出到达目标的最优路径.整个过程是一种自主搜索的过程.研究和实现了基于位差值的搜索算法和基于等高图的最优路径算法,并作出改进提高机器人完成任务的能力.     

基于机器视觉和A*算法的迷宫机器人路径规划

针对迷宫机器人路径规划问题,以机器视觉和A*算法为基础,提出了一种新的迷宫机器人全局路径规划方法。该方法利用区域阀值分割对迷宫机器人系统采集的图像进行分析,结合A*算法逆向搜索全局最优路径。仿真结果表明,该方法实现简单,在复杂的迷宫环境下能有效地实现迷宫机器人路径规划。     

一种迷宫机器人的人工脑系统

提出一种迷宫机器人的人工脑系统,包括迷宫路标感知单元与行为决策单元.其中,感知单元基于ARTI神经网络,用于识别迷宫导向路标;决策单元基于行为概率实现矩阵,并以强化学习更新行动策略.机器人所在迷宫的特征为每个路口设有导向路标,路标为含噪声的符号图像.在仿真实验中,令机器人在迷宫中随机行走,通过调节人工脑系统的试验参数,经过一段时间的自主探索学习过程机器人能最终穿越迷宫.仿真实验结果表明,该人工脑系统能够自组织地理解迷宫中导向路标的含义,并引导机器人成功穿越迷宫.同时,该人工脑系统对于基于路标导航的城市巡逻机器人、高危复杂环境下的抢险机器人的研究发展有一定的推动作用.     

一种时间记录寻迹智能小车的设计

机器人是近年来迅速发展的一个重要的高技术领域。机器人适合在危险的环境中使用,可以进入或穿过这些危险区域进行维护和探测工作,且不需要得到像对人一样的保护。基于IEEE国际电工和电子工程学会电脑鼠走迷宫竞赛的方法,并根据小车行进的环境特点和性能指标,基于路径决策中如何选择耗时最低的路径进行了寻迹智能小车的设计。     

基于栅格模型的机器人路径规划快速算法

机器人路径规划算法通常要求是实时的,遗传算法和蚁群算法应用于机器人路径规划时性能并不突出。论文提出的基于栅格的快速算法,简单实用,且计算速度快、占用存贮空间少。实验结果表明,在同等规模下,该算法求解时间仅为蚁群算法求解时间的大约0.025%,算法在更大的数据集上也取得了成功。同时该算法还是一个高效的迷宫最短路的搜索算法。     

一种电脑鼠走迷宫的算法

电脑鼠(英文名称为Micromouse)是一个由微处理器控制的,集感知、判断、行走功能于一体,能够自动寻找最佳路径到达目的地的微型机器人.它可以在"迷宫"中自动感知并记忆迷宫地图,通过一定的算法寻找一条最佳路径,以最快的速度到达目的地.     

基于意向网络的足球机器人策略决策

意向是指某一结论成立的可能性，是一个模糊量，但这个模糊量的表现 形式是一个确定的数值。意向网络则处理了模糊量不确定的情况，即可能性属于某一范畴的情况，意向网络结构简单，推理运算迅速，实时性强，本文将意向网络运用在动态多变的足球机器人决策系统中，简单介绍了它的几种推理运算规则，并以一个简单的意向网络仿真实现了足球机器人的策略决策。     

基于笛卡儿坐标系的迷宫路径规划算法

针对迷宫这类特殊的环境模型,提出了基于笛卡儿坐标系的新的路径规划算法。该算法通过找出机器人行驶方向和位置变化的关系,方便快捷地实现了对路径的记忆,并且在到达终点后能够通过一种简单的路径优化算法算出最短路径,最终实现以最短路径返回。此算法的可行性已通过轮式机器人实例得到证实。     

虚拟磁场理论在足球机器人路径规划中的应用

机器人足球比赛是近年来开展的高科技对抗动行，对此为平台研究多智能体系统及其协调问题。路径规划是实现机器人智能的一个关键技术。根据对环境信息掌握的程度不同，可以将机器人路长规划分为两种类型，一个是基于环境先验完全信息的全避路径规划，     

迷宫电脑鼠控制系统设计

电脑鼠实际上是集传感与控制于一体的,能够自动穿越迷宫的微型机器人。本课题结合模拟电路、数字电路知识以及传感器知识,制作出一个性能优良的电脑鼠,在迷宫中找到一条最优路径,走出迷宫。根据国际标准迷宫电脑鼠的比赛规则,通过各种方案的对比,确定在本文中采用NXP公司ARM7LPC2138作为控制核心,将新兴的群智能算法运用到迷宫电脑鼠中。     

Assumptive planning and execution: A simple, working robot architecture

In this paper, we present a simple approach to interleaving planning and execution based on the idea of making simplifying assumptions. Since assumptions can be tragically wrong, this assumptive approach must ensure both that the robot does not believe it has solved a problem when it has not and that it does not take steps that make a problem unsolvable. We present an assumptive algorithm that preserves goal-reachability and in addition we specify conditions under which the assumptive architecture is sound and complete.We have successfully implemented the assumptive architecture on several real-world robots. Students in an introductory robot lab at Stanford University implement an assumptive system on robots that have incomplete information about their maze world. Dervish, our winning entry in the 1994 AAAI National Robot Competition, implements an assumptive architecture to cope with partially specified environments and unreliable effectors and sensors.     

Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques

In this paper, we propose an unsupervised neural network allowing a robot to learn sensory-motor associations with a delayed reward. The robot task is to learn the “meaning” of pictograms in order to “survive” in a maze. First, we introduce a new neural conditioning rule probabilistic conditioning rule (PCR) allowing us to test hypotheses (associations between visual categories and movements) during a given time span. Second, we describe a real maze experiment with our mobile robot. We propose a neural architecture overcoming the difficulty to build visual categories dynamically while associating them to movements. Third, we propose to use our algorithm on a simulation in order to test it exhaustively. We give the results for different kinds of mazes and we compare our system to an adapted version of the Q-learning algorithm. Finally, we conclude by showing the limitations of approaches that do not take into account the intrinsic complexity of a reasoning based on image recognition.     

基于测距红外传感器的轮式迷宫机器人设计

本设计基于轮式迷宫机器人红外检测系统多采用5组或6组一体式红外接收传感器,无法进行测距,设计者使用了4组测距式红外传感器的轮式迷宫机器人。给出了电路设计、传感器布局、直行姿态调整以及快速过弯的方案。实验结果表明,该设计方案可行,轮式迷宫机器人工作稳定可靠,速度较快。     

A new paradigm of humanoid robot motion programming based on touch interpretation

Most humanoid soccer robot teams design the basic movements of their robots, like walking and kicking, off-line and manually. Once these motions are considered satisfactory, they are stored in the robot’s memory and played according to a high level behavioral strategy. Much time is spent in the development of the movements, and despite the significant progress made in humanoid soccer robots, the interfaces employed for the development of motions are still quite primitive. In order to accelerate development, an intuitive instruction method is desired. We propose the development of robot motions through physical interaction. In this paper we propose a ”teaching by touching” approach; the human operator teaches a motion by directly touching the robot’s body parts like a dance instructor. Teaching by directly touching is intuitive for instructors. However, the robot needs to interpret the instructor’s intention since tactile communication can be ambiguous. This paper presents a method to learn the interpretation of the touch meaning and investigates, through experiments, a general (shared among different users) and intuitive touch manner.     

基于八方向跟踪算法的迷宫问题新解

本文提出了一个基于八方向跟踪算法的破解迷宫问题的新方法,避免了用深探法或广探法求解迷宫问题的诸多问题,它不仅为计算机的解题提供了一个快捷的算法,而且也为人工或机器人破解提供了一个无需记忆的简便方法。另外,本文还给出了迷宫次佳通路和最佳通路（即捷径）的求解算法;岔道剔除算法和最佳八连通选择算法。本文的所有方法尽管是针对求解单通路迷宫提出采的,但算法对多通路和有环的迷宫也同样有效。     

Robot teaching system based on hand-robot contact state detection and motion intention recognition

This paper presents a robot teaching system based on hand-robot contact state detection and human motion intent recognition. The system can detect the contact state of the hand-robot joint and extracts motion intention information from the human surface electromyography (sEMG) signals to control the robot's motion. First, a hand-robot contact state detection method is proposed based on the fusion of the virtual robot environment with the physical environment. With the use of a target detection algorithm, the position of the human hand in the color image of the physical environment can be identified and its pixel coordinates can be calculated. Meanwhile, the synthetic images of the virtual robot environment are combined with those of the physical robot scene to determine whether the human hand is in contact with the robot. Besides, a human motion intention recognition model based on deep learning is designed to recognize human motion intention with the input of sEMG signals. Moreover, a robot motion mode selection module is built to control the robot for single-axis motion, linear motion, or repositioning motion by combining the hand-robot contact state and human motion intention. The experimental results indicate that the proposed system can perform online robot teaching for the three motion modes.     

Reading a robot's mind: a model of utterance understanding based on the theory of mind mechanism

The purpose of this paper is to construct a methodology for smooth communication between humans and robots. Here, focus is on a mindreading mechanism, which is indispensable in human-human communication. We propose a model of utterance understanding based on this mechanism. Concretely speaking, we apply the model of a mindreading system to a model of human-robot communication. Moreover, we implement a robot interface system that applies our proposed model. The characteristic of our interface system is its ability to construct a relationship between a human and a robot by a method of having an agent, which interacts with the person, migrate from the mobile PC of the person to the robot. Psychological experiments were carried out to explore the validity of the following hypothesis: By reading a robot's mind based on such a relationship, a person can estimate the robot's intention with ease and, moreover, the person can even understand the robot's unclear utterances made by synthesized speech sounds. The results of the experiments statistically supported our hypothesis.     

一种基于轨迹分布模式的网络机器人用户意图识别方法

在Internet移动机器人遥操作系统中,网络本身的不确定性是影响系统性能和用户操作效果的一个重要因素。提出了一种新的基于轨迹分布模式的方法,能对用户意图进行建模和识别,利用网络机器人的本身的自主性削弱网络不确定性给系统带来的不良影响。通过实验验证,发现这种方法能够较好地识别出用户的控制意图,并根据识别结果自主执行用户期望完成的任务,从而改善了用户的操作体验和机器人接受用户操作的表现,提高了遥操作系统的效率。     

一种基于Gryphon机器人的在线人机交互新型远程培训方法

该文主要对设立该项目的初衷、Gryphon机器人的远程操作环境以及远程控制软件都做了概要性介绍。在此基础上,作者结合中国实际,提出了一些自己的看法。