Efficient Simultaneous Localization and Mapping Based on Ceiling-View: Ceiling Boundary Feature Map Approach

For simultaneous localization and mapping (SLAM) based on the extended Kalman filter, the size of the state vector is an essential factor because the feasibility depends on it. In this paper, a new SLAM based on ceiling vision (cv-SLAM) is proposed. To keep the size of the state vector compact, the boundaries between ceiling and walls are used as landmarks for visual SLAM (vSLAM). The ceiling boundaries are robust to illuminative variations and they are not as numerous as the point features. Some constraints are imposed on the features based on the characteristics of the ceiling boundaries and an efficient update method called 'double update' is proposed to improve the SLAM performance. The basic idea of the double update is to fully utilize the intersections of the boundary features. Finally, the proposed SLAM is applied to some simulations and experiment, and its effectiveness is demonstrated through them.     

Through arc sensing in gas metal arc welding with signature images

Abstract

This work presents a new method for through the arc estimation of welding parameters in gas metal arc welding based on signature images. The method is generally applicable and makes use of a novel data fitting procedure. The work covers unassisted through the arc estimation, without auxiliary techniques such as welding head oscillation. The technique is demonstrated for both pulse and short circuiting welding, with estimation of position relative to an overlap joint and a narrow gap weld, and estimation of the width of a 'V' groove. The accuracy of the technique is affected by how closely the welding signatures match the fitting data and the signature image quality match provides a valuable check which shows when through the arc estimates cannot be relied on. Overall, the results demonstrate that through the arc measurements can have application in specialised situations, although accuracy is ultimately limited by phenomena that affect the arc and are not correlated with the measurement.     

MOTOMAN-UP系列机器人离线编程系统

采用VISUAL C 6.0及OpenGL技术开发了MOTOMAN-UP系列弧焊机器人离线编程系统.建立了MOTOMAN-UP系列机器人D-H坐标系及连杆参数表,编写了运动学逆解模块;针对典型马鞍形工件的特点,规划出焊枪的运动路径与姿态,且按比例同步显示工件;集成了机器人通信模块MOTOCOM32和运动学仿真模块ROTSY并同步导入马鞍形工件,不仅能单步生成作业指令,且可自动生成作业程序.通过对焊接过程进行离线仿真试验及传输作业实焊,验证该系统是切实可行的.     

利用VISUAL FORTRAN实现重力式挡土墙稳定性分析及地基检测绘图

针对重力式挡土墙稳定分析及地基检测分析,利用VISUAL FORTRAN语言编程对基础数据进行处理,控制简单,应用方便.通过对话框程序实现了对重力式挡土墙稳定性的计算处理分析,并在编辑窗口中直观地显示分析结果,能更方便快捷地为挡土墙设计提供依据.同时,利用程序生成SCR脚本文件,能迅速地在AUTOCAD中自动生成地基检测曲线.     

淬透性曲线应用系统软件

利用VISUAL BASICA6．0在计算机上建立了常用钢种淬透性曲线,并利用淬透性曲线和建立的数据库自动完成相关的作图和计算。     

First results in omni-directional visual servoing

In this paper, we describe a visual servoing system developed as a human-robot interface to drive a mobile robot toward any chosen target. An omni-directional camera is used to get the 360° of field of view and an efficient tracking technique is developed to track the target. The use of the omni-directional geometry eliminates many of the problems common in visual tracking and makes the use of visual servoing a practical alternative for robot-human interaction. The experiments demonstrate that it is an effective and robust way to guide a robot. In particular, the experiments show robustness of the tracker to loss of template, vehicle motion, and change in scale and orientation.     

Inevitable collision states — a step towards safer robots?

An inevitable collision state for a robotic system can be defined as a state for which, no matter what the future trajectory followed by the system is, a collision with an obstacle eventually occurs. An inevitable collision state takes into account the dynamics of both the system and the obstacles, fixed or moving. The main contribution of this paper is to lay down and explore this novel concept (and the companion concept of inevitable collision obstacle). Formal definitions of the inevitable collision states and obstacles are given. Properties fundamental for their characterization are established. This concept is very general, and can be useful both for navigation and motion planning purposes (for its own safety, a robotic system should never find itself in an inevitable collision state). To illustrate the interest of this concept, it is applied to a problem of safe motion planning for a robotic system subject to sensing constraints in a partially known environment (i.e. that may contain unexpected obstacles). In safe motion planning, the issue is to compute motions for which it is guaranteed that, no matter what happens at execution time, the robotic system never finds itself in a situation where there is no way for it to avoid collision with an unexpected obstacle.     

Self-organization of Dynamic Object Features Based on Bidirectional Training

This paper presents a method to self-organize object features that describe object dynamics using bidirectional training. The model is composed of a dynamics learning module and a feature extraction module. Recurrent Neural Network with Parametric Bias (RNNPB) is utilized for the dynamics learning module, learning and self-organizing the sequences of robot and object motions. A hierarchical neural network is linked to the input of RNNPB as the feature extraction module for self-organizing object features that describe the object motions. The two modules are simultaneously trained through bidirectional training using image and motion sequences acquired from the robot's active sensing with objects. Experiments are performed with the robot's pushing motion with a variety of objects to generate sliding, falling over, bouncing and rolling motions. The results have shown that the model is capable of self-organizing object dynamics based on the self-organized features.     

A Robust Solution to the Stereo-Vision-Based Simultaneous Localization and Mapping Problem with Steady and Moving Landmarks

The problem of visual simultaneous localization and mapping (SLAM) is examined in this paper using recently developed ideas and algorithms from modern robust control and estimation theory. A nonlinear model for a stereo-vision-based sensor is derived that leads to nonlinear measurements of the landmark coordinates along with optical flow-based measurements of the relative robot–landmark velocity. Using a novel analytical measurement transformation, the nonlinear SLAM problem is converted into the linear domain and solved using a robust linear filter. Actually, the linear filter is guaranteed stable and the SLAM state estimation error is bounded within an ellipsoidal set. A mathematically rigorous stability proof is given that holds true even when the landmarks move in accordance with an unknown control input. No similar results are available for the commonly employed extended Kalman filter, which is known to exhibit divergence and inconsistency characteristics in practice. A number of illustrative examples are given using both simulated and real vision data that further validate the proposed method.     

Sensor-based probabilistic roadmaps: experiments with an eye-in-hand system

We present a real implemented eye-in-hand test-bed system for sensor-based collision-free motion planning for articulated robot arms. The system consists of a PUMA 560 with a triangulation-based area-scan laser range finder (the eye) mounted on its wrist. The framework for our planning approach is inspired by recent motion planning research for the classical model-based case (known environment) and incrementally builds a roadmap that represents the connectivity of the free configuration space, as the robot senses the physical environment. We present some experimental results with our sensor-based planner running on this real test-bed. The robot is started in completely unknown and cluttered environments. Typically, the planner is able to reach (planning as it senses) the goal configuration in about 7-25 scans (depending on the scene complexity), while avoiding collisions with the obstacles throughout.