基于高阶CMAC网络的机器人自学习控制器

提出了将CMAC网络与PD控制器相结合的机器人在线自学习控制器,通过引入高阶网络的概念,采用简单的叠加处理法将多维输入空间的CMAC神经网络化为多个一维输入子网络,从而简化系统的网络结构.同时对进行叠加的一维输入CMAC网络选取不同的学习步长,提高了学习的收敛速度和模型的逼近能力.计算机仿真结果表明,所提出的新型机器人自学习控制器具有很好的控制性能.     

可重构斜拉索磁性无损检测机器人技术研究

针对斜拉索的特征,提出一种在役斜拉索检测机器人技术方案.采用模块化可重构磁性检测技术,利用磁性检测系统励磁场产生的磁场力作为机器人沿斜拉索爬升所需的附着力,使单元模块同时具有检测及爬升功能.根据不同的拉索直径,选用不同数量的模块进行重构形成对应的自主爬升检测机器人.理论分析及试验研究表明,该可重构斜拉索磁性检测机器人可对不同直径、任意倾斜角的斜拉索进行无损检测,具有体积小、重量轻、安全可靠等特点,有良好的应用前景.     

Adaptive cruise control design for active congestion avoidance

We present an adaptive cruise control (ACC) strategy where the acceleration characteristics, that is, the driving style automatically adapts to different traffic situations. The three components of the concept are the ACC itself, implemented in the form of a car-following model, an algorithm for the automatic real-time detection of the traffic situation based on local information, and a strategy matrix to adapt the driving characteristics (that is, the parameters of the ACC controller) to the traffic conditions. Optionally, inter-vehicle and infrastructure-to-car communication can be used to improve the accuracy of determining the traffic states. Within a microscopic simulation framework, we have simulated the complete concept on a road section with an on-ramp bottleneck, using empirical loop-detector data for an afternoon rush-hour as input for the upstream boundary. We found that the ACC vehicles improve the traffic stability and the dynamic road capacity. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were already significantly reduced for much lower penetration rates. The efficiency of the proposed driving strategy even for low market penetrations is a promising result for a successful application in future driver assistance systems.     

基于混合学习算法的水下机器人神经网络辨识

借鉴Elman和Jordan神经网络的特点,构造了一种新的动态神经网络.该网络能对隐含层的历史进行状态记忆,实时调整过去的信号对现在值的影响,并且增加了输出层节点的反馈以增强神经网络的信号处理能力.将基于遗传算法(GA)和误差反传算法(BP)的混合学习算法用于神经网络权值的修改,既可提高收敛速度又能避免陷于局部极小值.最后,将改进的神经网络应用于水下机器人动力学模型辨识,仿真结果表明,基于混合学习算法的神经网络提高了学习的收敛速度和辨识精度.     

道路标志自动分类方法

为了对道路标志图像进行自动分类,通过图像颜色空间变换,将图像的RGB量值转换为H(色度)S(饱和度)I(亮度)量值,采用Sobel算子进行道路标志图像的边缘检测,利用行扫描法进行区域填充,以获取二值化的道路标志图像区域,提取道路标志二值化图像的不变矩与形状参数作为图像特征值,设计BP神经网络道路标志图像几何形状分类器,以道路标志图像的H、I为特征值,设计了欧式距离分类器,实现道路标志背景颜色的识别。融合道路标志图像几何形状和背景颜色的识别算法,并利用道路标志的分类知识和自动分类方法,能有效实现道路标志图像的自动识别。     

一种水上搜救机器人系统设计与实现

针对传统救援船舶受抗风浪能力和操控能力的限制,在恶劣天气条件下救援落水人员成功率不高的问题,设计了一款能够适应恶劣海况,而且能灵活抵近落水人员并直接施救的水上搜救机器人。该机器人系统由岸基控制子系统和机器人子系统组成,机体采用等角三浮筒结构布置,利用推进器差速原理实现机器人转向和机动控制。通过设计基于机器视觉的水面目标检测方法实现对落水人员的检测与识别,通过基于S面控制算法和LOS制导算法,实现了对一定范围内落水人员搜寻路径跟踪控制,利用滑模镇定控制算法实现了搜救机器人救援状态下的点镇定控制。水上实测证明,本文设计的搜救机器人整体结构设计合理,目标识别、运动控制算法运行稳定,救援能力达到了预期的设计目标。     

Optimal motion control for collision avoidance at Left Turn Across Path/Opposite Direction intersection scenarios using electric propulsion

Collision avoidance at intersections involving a host vehicle turning left across the path of an oncoming vehicle (Left Turn Across Path/Opposite Direction) have been studied in the past, but mostly using simplified interventions and rarely considering the possibility of crossing the intersection ahead of a bullet vehicle. Such a scenario where the driver preference is to avoid a collision by crossing the intersection ahead of a bullet vehicle is considered in this work. The optimal vehicle motion for collision avoidance in this scenario is determined analytically using a particle model within an optimal control framework. The optimal manoeuvres are then verified through numerical optimisations using a two-track vehicle model, where it was seen that the wheel forces followed the analytical global force angle result independently of the other wheels. A Modified Hamiltonian Algorithm controller for collision avoidance that uses the analytical optimal control solution is then implemented and tested in CarMaker simulations using a validated Volvo XC90 vehicle model. Simulation results showed that collision risk can be significantly reduced in this scenario using the proposed controller, and that more benefit can be expected in scenarios that require larger speed changes.     

RESEARCH ON THE DOF DISPOSITION AND MECHANISMS DESIGN OF HUMANOID ROBOT

IntroductionSome researchers[1～ 3] in Yugoslavia,Japan andAmerica have got many achievements in bipedrobot since the beginning of1 970 s.The bipedrobot can walk in static and dynamic gait and al-so can turn around and go up stairs.Recentlyhumanoid robot has become a hot researchingpoint[4,5 ] .Humanoid robot has body includinghead,neck,waist and hands.Biped robot is abasis of humanoid robot.Usually a humanoidrobot should have more than 2 0 DOF.All thetechnology problems in DOF planning,m…     

PERFORMANCE OF A KIND OF PERMANENT MAGNETIC SUCKERS USED IN WALL-CLIMBING ROBOTS

IntroductionWall- climbing robots with magnetic tracks arewidely used in the fields such as oil tank surfaceinspection,paints spraying shown in Fig.1 .Therobot attaches to the steel wall surface due to anumber of magnetic suckers on both tracks andis driven by the rear chain wheels. In order todecrease the robot's weight and improve its at-tractive ability on steel wall surface,the mag-netic suckers should be small in size,light inweight and powerful in attractive force.Theseare some of the i…     

驼峰解体作业中自动提钩机制的研究

用机器人解决驼峰提钩是驼峰自动化长期未解决的问题.结合驼峰综合自动化发展的要求、当今的铁路信息基础及技术发展水平,提出综合硬件解决技术和软件实现方案,以及可靠性技术,为驼峰自动提钩提出一种新解决方案及心得思路