Case-based reasoning for interpretation of data from non-destructive testing

Non-destructive testing (NDT) is a name for a range of methods and procedures used to determine fitness of industrial products for further use. The use of NDT testing techniques results in data in the form of signals, images, or sequences of these, which have to be analysed in order to determine if they contain any indications of defects in the inspected objects. This analysis is often quite complex. In the past, systems have been built which used neural networks (and other statistical classifiers) as well as expert systems to interpret NDT data; however, successful uses of these systems in inspection practice are rare. This article presents how the case-based reasoning methodology (where interpretation of new data is based on previous data-interpretation cases) can be used to tackle the problem of NDT data interpretation. The article presents the characteristics of CBR, which make it an interesting alternative to statistical classifiers and to expert systems. Suitability of CBR for NDT data interpretation is illustrated based on examples of two applications: a CBR system for ultrasonic rail inspection and a CBR system for eddy-current inspection of heat exchangers.     

Coverage Path Planning with Real‐time Replanning and Surface Reconstruction for Inspection of Three‐dimensional Underwater Structures using Autonomous Underwater Vehicles

We present a novel method for planning coverage paths for inspecting complex structures on the ocean floor using an autonomous underwater vehicle (AUV). Our method initially uses a 2.5‐dimensional (2.5D) prior bathymetric map to plan a nominal coverage path that allows the AUV to pass its sensors over all points on the target area. The nominal path uses a standard mowing‐the‐lawn pattern in effectively planar regions, while in regions with substantial 3D relief it follows horizontal contours of the terrain at a given offset distance. We then go beyond previous approaches in the literature by considering the vehicle's state uncertainty rather than relying on the unrealistic assumption of an idealized path execution. Toward that end, we present a replanning algorithm based on a stochastic trajectory optimization that reshapes the nominal path to cope with the actual target structure perceived in situ. The replanning algorithm runs onboard the AUV in real time during the inspection mission, adapting the path according to the measurements provided by the vehicle's range‐sensing sonars. Furthermore, we propose a pipeline of state‐of‐the‐art surface reconstruction techniques we apply to the data acquired by the AUV to obtain 3D models of the inspected structures that show the benefits of our planning method for 3D mapping. We demonstrate the efficacy of our method in experiments at sea using the GIRONA 500 AUV, where we cover part of a breakwater structure in a harbor and an underwater boulder rising from 40 m up to 27 m depth.     

Path planning method for on-machine inspection of aerospace structures based on adjacent feature graph

On-Machine Inspection (OMI) system has been playing an important role in modern aeronautical manufacturing, owing to its high efficiency, great convenience, and low application cost. The inspection path planning of OMI, which aims to find an optimal path that traverses all inspection points without collisions and by costing as little time as possible, is a main bottleneck that limits the achievements of higher efficiency and less inspection time. Besides, the path planning for OMI in aerospace manufacturing faces new challenges, which are barely explored, because aerospace structures have unique characteristics of complex geometrical features, large-scale dimensions and high-precision processing requirements. Thus, this paper proposes a novel, easily-implemented and robust inspection path planning method to plan paths for OMI of aerospace structures based on the properties of aerospace structures. In order to lift the inspection efficiency, this method makes three improvements on the path planning. First, reorganize the inspection features based on the cluster technology. Second, construct the adjacent feature graph based on Voronoi Diagram to plan the path. Third, a search algorithm is designed to search the adjacent feature graph to decide the sequence of inspection features and a convex hull based algorithm is used to avoid collisions. The proposed method has been tested for several cases and solid experimental results have shown that these improvements take effects in path planning for OMI of aerospace structures and suited paths can be provided for the inspection.     

Multiobjective coverage path planning: Enabling automated inspection of complex,real-world structures

An important open problem in robotic planning is the autonomous generation of 3D inspection paths – that is, planning the best path to move a robot along in order to inspect a target structure. We recently suggested a new method for planning paths allowing the inspection of complex 3D structures, given a triangular mesh model of the structure. The method differs from previous approaches in its emphasis on generating and considering also plans that result in imperfect coverage of the inspection target. In many practical tasks, one would accept imperfections in coverage if this results in a substantially more energy efficient inspection path. The key idea is using a multiobjective evolutionary algorithm to optimize the energy usage and coverage of inspection plans simultaneously – and the result is a set of plans exploring the different ways to balance the two objectives. We here test our method on a set of inspection targets with large variation in size and complexity, and compare its performance with two state-of-the-art methods for complete coverage path planning. The results strengthen our confidence in the ability of our method to generate good inspection plans for different types of targets. The method's advantage is most clearly seen for real-world inspection targets, since traditional complete coverage methods have no good way of generating plans for structures with hidden parts. Multiobjective evolution, by optimizing energy usage and coverage together, ensures a good balance between the two – both when 100% coverage is feasible, and when large parts of the object are hidden.     

Intelligent environment recognition and prediction for NDT inspection through autonomous climbing robot

This paper presents a novel approach to environment mapping prediction with focus on autonomous climbing robot to NDT (Non-Destructive Technique) inspection. In industrial installations, the inspection of non-planar surfaces requires that NDT probes passe on whole surface, while the autonomous robot navigates over an unknown environment based only on its perception abilities. However, the path planning of inspection is not a trivial task specially when there is no precise information about environment. In this work, a special kind of climbing robot is used to inspect large metallic surfaces such as spherical pressure vessels used to store Liquified Petroleum Gas (LPG). The robot has adherence skills that allow it to safely navigate through the internal and external surface of the vessel. As a result, robot mobility suffers from hard magnetic adhesion constraints. A new approach is proposed to environment detailed prediction, including specific characteristic (like weld beads and plates) of inspected surface. The goal is the automatic extraction of some environment characteristics to predict the storage tank dimensions and robot localization, based on a group of 3D perception sources (laser rangefinder, light detection and ranging and depth camera) mounted over a rolling platform to improve its reach. The environment prediction is carried out after the robot visually detects two or more weld beads corners. A multi-measuring environment is firstly build by Fuzzy data fusion of the different perception measurements allowing to estimate plates and weld beads based on design and safety standards. Virtual and real experiments are carried out to illustrate proposed method performance.     

Data mining corrosion from eddy current non-destructive tests

Quicker, more effective methods of corrosion prediction and classification can help to ensure a safe and operational transportation system for both civilian and military sectors. This is especially critical now as transportation providers attempt to meet the increased expense of repairing aging aircraft with smaller budgets. These budget constraints make it imperative to find corrosion and to correctly determine the appropriate time to replace corroded parts. If the part is replaced too soon, the result is wasted resources. However, if the part is not replaced soon enough, it could cause a catastrophic accident. The discovery of models that limit the possibility of a costly accident while optimizing resource utilization would allow transportation providers to efficiently focus their maintenance efforts. While our concern in this study was with aircraft, the results will also be useful to other transportation providers. This paper describes the discovery and comparison of empirical models to predict corrosion damage from non-destructive test (NDT) data. The NDT data were derived from eddy current (EC) scans of the United States Air Force's (USAF) KC-135 aircraft. While we might suspect a link between NDT results and corrosion, up until now this link has not been formally established. Instead, the NDT data have been converted into false color images that are analyzed visually by maintenance operators. The models we discovered are quite complex and suggest that with the appropriate data mining approaches we can sometimes more effectively handle noisy data through more complex models rather than simpler ones. Our results also show that while a variety of modeling techniques can predict corrosion with reasonable accuracy, regression trees are particularly effective in modeling the complex relationships between the EC measurements and the actual amount of corrosion.     

基于自定义特征的飞机结构件智能检测规划技术

飞机结构件常用坐标测量机检测形状特征的加工质量。为了满足飞机结构件需要检测复杂形状特征以及便于拓展形状特征检测范围的迫切需求,研究了独立于形状特征类型的特征定义框架及统一的特征识别算法;提出了检测知识驱动的测量点智能分布方法;设计了结合聚类和改进模拟退火算法的测量方向优化算法;实现了基于自定义特征及检测知识的智能检测规划。该技术已在多个重大航空产品的研制中得到应用,显著提升了飞机结构件检测规划的效率和智能化水平。     

Coverage path planning with targetted viewpoint sampling for robotic free-form surface inspection

Surface metrology systems are increasingly used for inspecting dimensional quality in manufacturing. The gauge of these measurement systems is often mounted as an end-effector on robotic systems to exploit the robots’ high degrees of freedom to reposition the gauge to different viewpoints. With this repositioning flexibility, a planning methodology becomes necessary in order to carefully plan the viewpoints, as well as the optimal sequence and quickest path to move the gauge to each viewpoint. This paper investigates coverage path planning for robotic single-sided dimensional inspection of free-form surfaces. Reviewing existing feasible state-of-the-art methodologies to solve this problem led to identifying an unexplored opportunity to improve the coverage path planning, specifically by replacing random viewpoint sampling strategy. This study reveals that a non-random targetted viewpoint sampling strategy significantly contributes to solution quality of the resulting planned coverage path. By deploying optimisation during the viewpoint sampling, an optimal set of admissible viewpoints can be obtained, which consequently significantly shortens the cycle-time for the inspection task. Results that evaluate the proposed viewpoint sampling strategy for two industrial sheet metal parts, as well as a comparison with the state-of-the-art are presented. The results show up to 23.8% reduction in cycle-time for the inspection task when using targetted viewpoints sampling.     

面向变电站机器人巡检路径规划中的算法研究

针对目前在变电站中应用广泛的巡检机器人,探讨了其巡检路径的问题,依据离散数学理论中的哈密顿回路方法和路径的常规规划算法,研究了一种变电站的巡检路径为矩形分布时的机器人巡检路径问题。针对不同巡检方式,分别提出：全局巡检方式,采用新提出的哈密顿算法,当巡视起点被设定后,其他被巡视的任何点均可被归类,从而找出其巡检路径的哈密顿回路;重点设备巡检方式,采用Dijkstra算法和遗传算法相结合的方法;固定点巡检方式,采用Dijkstra算法。其中,三种巡检方式分别针对电量充足、低电量返回及充电完毕继续巡视等方面给出仿真结果,并将其在某变电站进行了试用。研究结果表明,三种巡检方式下的算法均有效可行,新提出的哈密顿回路算法巡视的路径比常规方法相对要短,算法的运算速度相对更快。     

Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots

This paper presents a new algorithm for three-dimensional coverage path planning for autonomous structural inspection operations using aerial robots. The proposed approach is capable of computing short inspection paths via an alternating two-step optimization algorithm according to which at every iteration it attempts to find a new and improved set of viewpoints that together provide full coverage with decreased path cost. The algorithm supports the integration of multiple sensors with different fields of view, the limitations of which are respected. Both fixed-wing as well as rotorcraft aerial robot configurations are supported and their motion constraints are respected at all optimization steps, while the algorithm operates on both mesh- and occupancy map-based representations of the environment. To thoroughly evaluate this new path planning strategy, a set of large-scale simulation scenarios was considered, followed by multiple real-life experimental test-cases using both vehicle configurations.     

基于鱼群算法的智能机器人全覆盖路径规划

为保证机器人的行驶轨迹可以全方位地的覆盖地图的全部坐标点,并降低路径重复率,基于鱼群算法设计智能机器人全覆盖路径规划方法。建立智能机器人死区脱困模型,计算栅格地图模型中的目标活性值,获取整体栅格数量,描述地图中栅格状态,得到脱困时的行驶角度差。基于鱼群算法设计全路径覆盖判定方法,描述不同目标鱼个体之间的距离,在三重移动目标坐标系下,获取元素坐标向量,建立每个目标点的求解代价和,计算下一个目标点行驶的最小距离。设计机器人全覆盖路径规划算法,判断当前位置是否为死区,获取路径规划的全局最优解,实现智能机器人的全覆盖路径规划。利用Matlab仿真软件完成智能机器人全覆盖路径规划实验。结果表明,在简单环境下,该路径规划方法覆盖率为100%,重复率为5.23%,路径长度为15.36m；在复杂环境下,该路径规划方法的覆盖率为100%,重复率则为10.24%,路径长度为20.34m。由此证明,该方法具有较好地规划效果较好。     

油罐检测爬壁机器人结构与控制系统设计

研究智能爬壁机器人检测技术及其系统 ,提出了机器人在大型垂直罐壁移动作业的路径规划方法 ,并分析了抗倾覆机构的作用 ,设计给出了机器人总体结构和控制系统 .系统以磁吸附爬壁机器人为运动载体 ,采用非接触式无损检测技术 ,并配备多种传感器 ,具备较高的智能化水平 .现场实验表明 ,该系统自动化程度高、运动稳定可靠、定位精度高 ,大幅度提高了检测效率 .     

A neural network approach to complete coverage path planning.

Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.     

Coverage based tool-path planning for automated polishing using contact mechanics theory

Presented in this paper is a tool-path planning method for automated polishing. This work is an integral part of research program on automated polishing/deburring being carried out at Ryerson University. Whereas tool-path planning for machining is treated as a geometry problem, it is shown here that tool-path planning for polishing should be treated as a contact mechanics problem because of contact action between the polishing tool and the part. To develop this method, contact mechanics is applied for contact area modeling and analysis. Once the contact area is determined for multiple points along the given polishing path, a Contact Area Map (CAM) is established and used to show the coverage area during polishing. This map is then used to plan polishing paths that ensures a complete coverage for polishing. Simulation has been conducted to show the effectiveness of this new tool-path method.     

救灾无人机的优化A*航迹规划算法

针对抢险救灾中无人机派遣量及空间航迹规划最短路径相制约的问题,提出了一种优化A*的航迹算法。通过设计的蛇形割圆法对圆形巡查区域进行路径规划,通过提取感兴趣区域的方法选择较佳搜索方向,提高搜索效率,采用加权评估法优化自然威胁权重系数,重定义航迹估计函数。将提出的方法在灾情巡查和生命勘测实际问题中进行性能检测。仿真结果表明,该算法能够合理分配无人机数量且能快速规划出较优飞行轨迹,实现巡查覆盖率达88.96%。     

基于符号执行与模糊测试的混合测试方法

软件测试是保障软件质量的常用方法,如何获得高覆盖率是测试中十分重要且具有挑战性的研究问题.模糊测试与符号执行作为两大主流测试技术已被广泛研究并应用到学术界与工业界中,这两种技术都具有一定的优缺点：模糊测试随机变异生成测试用例并动态执行程序,可以执行并覆盖到较深的分支,但其很难通过变异的方法生成覆盖到复杂条件分支的测试用例.而符号执行依赖约束求解器,可以生成覆盖复杂条件分支的测试用例,但在符号化执行过程中往往会出现状态爆炸问题,因此很难覆盖到较深的分支.有工作已经证明,将符号执行与模糊测试相结合可以获得比单独使用模糊测试或者符号执行更好的效果.分析符号执行与模糊测试的优缺点,提出了一种基于分支覆盖将两种方法结合的混合测试方法——Afleer,结合双方优点从而可以生成具有更高分支覆盖率的测试用例.具体来说,模糊测试（例如AFL）为程序快速生成大量可以覆盖较深分支的测试用例,符号执行（例如KLEE）基于模糊测试的覆盖信息进行搜索,仅为未覆盖到的分支生成测试用例.为了验证Afleer的有效性,选取标准程序集LAVA-M以及实际项目oSIP作为评测对象,以漏洞检测能力以及覆盖能力作为评测指标.实验结果表明：（1）在漏洞检测能力上,Afleer总共可以发现755个漏洞,而AFL仅发现1个;（2）在覆盖能力上,Afleer在标准程序集上以及实际项目中都有不同程度的提升.其中,在oSIP中,Afleer比AFL在分支覆盖率上提高2.4倍,在路径覆盖率上提升6.1倍.除此之外,Afleer在oSIP中还检测出一个新的漏洞.     

Orientation-point relation based inspection path planning method for 5-axis OMI system

Inspection path planning methods are of great importance to OMI systems. Most researches only optimize the inspection path length. However, the number of probe orientations (NPOs) also has a great influence on inspection efficiency and accuracy, especially for parts with complicated features. To optimize the NPOs and the path length, this paper firstly proposes the concept of orientation-point (O-P) relation schema which includes all valid O-P inspection cases. Then based on the O-P relation schema, two algorithms – the minimum orientation algorithm (MOA) and the shortest path algorithm (SPA), which consider NPOs and path length in different priorities were proposed. MOA was designed to find the minimum orientations that can measure all the inspection points according to the O-P relation schema, then the inspection points were categorized into groups by the selected orientations. The traveling salesman problem (TSP) algorithm was used to plan the inspection path for each group's points, finally the total path was obtained by connecting paths of different groups in sequence. SPA firstly uses the TSP algorithm to obtain the shortest inspection sequence, then minimizes the NPOs by finding orientations that can inspect most consecutive points along the planned inspection sequence according to the O-P relation schema. To avoid possible collisions during the inspection process, a safe box that contains the inspection part was defined before planning the path, which enables the probe to cross obstacles by moving to the safe planes of the safe box. Simulation experiments were conducted on three parts, the results show that both MOA and SPA can obtain a collision-free inspection path. MOA can effectively decrease the NPOs compared with SPA, and the path length can be even shorter than that of SPA in some complicated cases.     

The control of a quick turnaround cell — An integrated CAD and CAM system

Quick turnaround Cell (QTC) is an integrated software/hardware system for rapid product prototyping. A QTC consists of some engineering workstations, one of a few machine tools, and a vision inspection system. The major objective of the project is to study the integration of CAD, process planning, cell control, and vision inspection. Currently a prototype QTC has been implemented at Purdue Uniersity. It is capable of producing prismatic parts. After a part is designed, it can be machined and inspected automatically. There is no human decision necessary for the planning, part programming, and cell control. The cell controller plays an extremely important role on coordinating all the system functions and providing user and machine interfaces. In this paper we will address the issues encountered in designing the cell control. The structure and the functionality of the specific design will be discussed.     

Automatic inspection planning within a feature-based CAD system

This paper describe a CIM system which incorporates automatic generation of inspection on plans for coordinate measuring machines (CMMs). Design is done in terms of form features. Associated with each feature are inspection plan fragments (IPFs) which are instances of inspection procedures. There are three basic types of plan fragments for CMMs: (1) fragments for checking the internal dimensions of a feature, (2) fragments for checking the relationships between features, and (3) modified fragments for use when the feature to be inspected intersects another feature. Using this approach, a list of suitable inspection points and approach vectors is generated. A path planner then combines the elements of the list to produce a valid inspection path for the CMM. The system is consistent with the accepted ANSI Y14.5M standard for dimensioning and tolerancing as well as with observed industrial practice.     

A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autoxnomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh , 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.