A CAD-based inspection planning strategy for video measuring instrument

A CAD-based inspection planning strategy for video measuring instrument is proposed to improve manufacturing effec- tiveness. The system consists of a video probe that enables itself to inspect a work piece based on a CAD model. The measurement software includes CAIP module and MDP module. The CAIP module is developed based on a CAD development platform whose kernel is the Open CASCADE. The entire system was tested, and relevant examples show that the system can accomplish automatic inspection planning task for common parts efficiently.

A path planning method for robot patrol inspection in chemical industrial parks

Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.

Minimum dose path planning for facility inspection based on the discrete Rao-combined ABC algorithm in radioactive environments with obstacles

Workers who conduct regular facility inspections in radioactive environments will inevitably be affected by radiation.Therefore,it is important to optimize the inspection path to ensure that workers are exposed to the least amount of radiation.This study proposes a discrete Rao-combined artificial bee colony(ABC)algorithm for planning inspection paths with minimum exposure doses in radioactive environments with obstacles.In this algorithm,retaining the framework of the traditional ABC algorithm,we applied the directional solution update rules of Rao algorithms at the employed bee stage and onlooker bee stage to increase the exploitation ability of the algorithm and implement discretion using the swap operator and swap sequence.To increase the randomness of solution generation,the chaos algorithm was used at the initialization stage.The K-opt operation technique was introduced at the scout bee stage to increase the exploration ability of the algorithm.For path planning in an environment with complex structural obstacles,an obstacle detour technique using a recursive algorithm was applied.To evaluate the performance of the proposed algorithm,we performed experimental simulations in three hypothetical environments and compared the results with those of improved particle swarm optimization,chaos particle swarm optimization,improved ant colony optimization,and discrete Rao’s algorithms.The experimental results show the high performance of the proposed discrete Rao-combined ABC algorithm and its obstacle detour capability.

Cost-Benefit Assessment of Inspection and Repair Planning for Ship Structures Considering Corrosion Model Uncertainty

Owing to high costs and unnecessary inspections necessitated by the traditional inspection planning for ship structures, the risk-based inspection and repair planning should be investigated for the most cost-effective inspection. This paper aims to propose a cost-benefit assessment model of risk-based inspection and repair planning for ship structures subjected to corrosion deterioration. Then, the benefit-cost ratio is taken to be an index for the selection of the optimal inspection and repair strategy. The planning problem is formulated as an optimization problem where the benefit-cost ratio for the expected lifetime is maximized with a constraint on the minimum acceptalbe reliability index. To account for the effect of corrosion model uncertainty on the cost-benefit assessment, two corrosion models, namgly, Paik＇ s model and Guedes Soares＇ model, are adopted for analysis. A numerical example is presented to illustrate the proposed method. Sensitivity studies are also providet. The results indicate that the proposed method of risk-based cost-benefit analysis can effectively integrate the economy with reliability of the inspection and repair planning. A balance can be achieved between the risk cost and total expected inspection and repair costs with the proposed method, which is very. effective in selecting the optimal inspection and repair strategy. It is pointed out that the corrosion model uncertainty and parametric uncertaintg have a significant impact on the cost-benefit assessment of inspection and repair planning.

Plans for the Development and Revision of Industry Standards on Inspection and Quarantine from 2005-2007

The inspection and quarantine standard is one of important basics to executeinspection and quarantine by laws. In recent years, with the development of economy and trade inChina, standardization has played an important role in inspection and quarantine. During 2002-2004,department of Standards of Inspection and Quarantine, Certification and Accreditation Administrationof China has organized the research of the standards of inspection and quarantine, and has builtstandard sub- systems of nine fields in inspection and quarantine.

Optimal Risk-Based Maintenance Planning of Ship Hull Structure

Various structures such as marine structures age over time. In order to always maintain safety conditions, maintenance processes including inspection and repair should be implemented on them. Corrosion and fatigue cracks are two main factors that reduce the ultimate strength of the ship's hull girder over time and thus increase the probability and risk of failure. At the time of inspection,the structural conditions must be checked so that, if necessary, the required repairs can be done on it. The main objective of this paper is to provide optimized maintenance plans of the ship structure based on probabilistic concepts with regard to corrosion and fatigue cracks. Maintenance activities increase the operational costs of ships; therefore, it is advisable to inspect and repair in the optimal times. Optimal maintenance planning of the ship structure can be conducted by formulating and solving a multi-objective optimization problem. The use of risk as a structural performance indicator has become more common in recent years. The objective functions of the optimization problem include minimizing the structure's lifecycle maintenance costs, including inspection and repair costs, and also minimizing the maximum risk of structural failure during the ship's life. In the following,to achieve better responses, reliability index has been added to the problem as the third objective function. The multi-objective optimization problem is solved using genetic algorithms. The proposed risk-based approach is applied to the hull structure of a tanker ship.

Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests

A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)concepts.Under accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive.An economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s risks.Numerical results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are discussed.Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions.The AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling plans.Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.

电力巡检机器人路径规划方法综述

随着电力系统规模和复杂性的不断增加,传统的人工巡检方式已很难满足当下电力巡检要求。巡检机器人因具有安全性、可靠性和智能性已被逐步应用到电力巡检中。机器人在运行过程中通过提前预测、自主决策或智能导航的方式完成全局遍历与避障。电力巡检的核心任务是规划出一条安全且最优的运行轨迹。系统地分析和梳理了巡检机器人巡检过程中的各类方法;阐述了在变电站和输电线路等不同场景中算法的应用;综述了各类路径规划算法的研究现状和改进方法。在此基础上,对未来电力巡检机器人路径规划的研究和发展方向做出了展望。

面向多规格蒸发器传热管检修的新型机器人机构设计及其运动规划

面向核电站蒸汽发生器的管板爬行式检修机器人存在适应性差、检修效率低等问题。为实现蒸汽发生器多规格传热管的检修任务,设计了一款新型机构的四足爬行检修机器人,并对相关的步态规划展开了研究。首先,根据需要适应多规格管板的要求,提出机器人的构型以及整体方案设计,确定了爬行机器人的主要运动方式,并给出了工具工作空间;之后,根据不同类型管板开展了机器人步态规划相关研究,规划了三角形、正方形两种排布管板上的直线运动步态与转向运动步态;最后,利用V-REP与Python联合仿真进行机器人运动仿真实验,结果表明,爬行机器人能够完成多规格管板下的直线运动与转向运动,证明了机构的合理性与运动的可行性。

民机外表面上部自主视觉检查中的降维路径规划研究

传统的民机外表面检查过程中,人工目视检查存在成本高、效率低、工作时间长等诸多不足,而带有机载视觉系统的机器人可以安全、快速、准确地检查民机外表面。本文提出一种用于民机外表面上部自主视觉检查的三维覆盖路径规划方法。通过对民机外表面上部进行初步点云获取,对机身、机翼区域切片,将三维点云映射到平面,然后进行二维覆盖路径的规划及三维空间的路径复原,快速生成优化的三维全覆盖检查路径。结果表明,该方法可以缩短三维覆盖路径规划的时间,较大程度地降低民机外表面上部自主视觉检查的成本,推动自动化检查民机表面技术的发展。

融合A^(*)和TEB算法的巡检机器人路径规划研究

针对激光雷达智能巡检机器人在复杂环境下静态障碍物路径规划复杂,动态障碍物躲避效果不佳等问题,提出一种路径规划融合算法以提高机器人避障效果。采用Karto-SLAM进行激光SLAM建图,将A^(*)算法与TEB算法有效融合完成机器人的全局路径规划与局部路径规划。仿真实验与实测表明,通过Karto算法所建地图的平均误差约为1.259%,A^(*)与TEB的融合算法在完成单一A^(*)算法最优路径生成的同时还具备良好的避障功能,有效避障率达到90%以上,满足巡检机器人移动避障等要求。

中厚板多层多道焊视觉测量与工艺规划

针对中厚板多层多道焊的焊道测量问题,提出利用焊缝检测和焊道尺寸视觉测量的信息融合自适应微调焊枪位置的方法。首先基于结构光视觉传感器系统采集焊缝图像,在典型图像处理算法的基础上,结合FROSAC提取算法提取焊缝特征信息;将提取到的特征点进行坐标转换,采用视觉测量获得焊道轮廓和尺寸信息,来修正机器人的运动路径;根据焊缝特征信息分析工艺参数对焊道成型的影响,确定焊道层数、各焊道的工艺参数以及焊枪的偏移量,完成多层多道焊接工艺规划;最后基于搭建的机器人焊接视觉系统在12 mm母材上进行V形坡口多层多道焊接试验。结果表明:该方法下坡口填充良好,焊道尺寸平均测量误差小于0.2 mm,满足多层多道焊接工业应用需求。

智能变电站巡检机器人路径规划的改进ABC算法及其应用研究

为提升智能变电站巡检机器人的路径规划的效率,针对传统人工蜂群算法(ABC)的缺点,通过反向学习策略改进初始种群,利用精英反向学习优化算法的时间滞后性,同时借助差分算法的变异思维改进蜜源更新环节,提高算法的深度挖掘性能和全局寻优性能。实验数据表明,改进的ABC算法能够有效降低73.3%的巡检路径规划时间,巡检准确率可达95.2%,该算法可以提高智能变电站巡检机器人路径规划的效率,对提升智能变电站巡检机器人的智能化水平具有积极影响。

基于改进跳点搜索和蚁群算法的机器人多目标点巡检规划

针对移动机器人的多目标点巡检规划问题,本文提出了一种融合改进跳点搜索算法(JPS)与蚁群算法(ACO)的路径规划算法.首先,在JPS算法的评估函数中引入角度引导因子,使路径具有更强的导向性;然后,综合考虑路径距离、平滑度、安全性对评估函数的影响,以获得综合性能更优的路径;其次,提出了双向的逆向跳点剔除规则,筛除了多余节点,从而进一步降低路径长度并提高路径平滑度;最后,将多目标优化得到的路径综合性能替代传统旅行商问题(TSP)中的距离因子,并使用自适应蚁群算法来实现多巡检点的路径规划问题.仿真结果表明,改进JPS算法与传统JPS算法相比,具有更好的综合性能;同时应用于多巡检点规划时,具有更强的有效性和实用性.

基于改进D*Lite⁃APF算法的巡检机器人路径规划

针对巡检机器人在动态场景下路径规划存在非全局最优、路径不平滑及局部避障效果不佳的问题,提出一种将改进D*Lite算法和人工势场法融合的算法。首先优化D*Lite算法启发代价函数,提升规划效率,并引入Dubins曲线平滑生成的全局路径;其次改进人工势场法势场函数并添加随机半径扰动点,解决局部碰撞问题,提高避障性能;最后将两种优化算法有效融合,实现全局规划和局部避障。仿真实验结果表明,相较于单一D*Lite算法,融合算法在路径长度、时间花销、路径拐点及扩展节点数方面均表现更优,能在确保全局路径最优的情况下有效避障。

风机叶片巡检机器人快速检测路径规划算法

提出一种基于风力机叶片仿真分析的巡检机器人快速检测方法,通过对风机叶片进行静强度仿真分析得到其应力云图,基于云图模型进行环境建模,针对传统A^(*)算法提出改进,进行了权重值改进、拐角优化以及路径平滑处理,并针对不同情况分别进行仿真分析对比实验。实验结果表明,所设计方案可以改进算法搜索效率,在完成既定目标的前提下缩短了路径长度,减少了搜索时间,可以实现风机叶片巡检机器人快速检测任务。

面向管道焊缝渗透检测机器人的管道定位与规划方法

为了实现核电设施管路的自动化焊缝检测作业,在降低人工作业安全风险的同时提高工作效率,提出了一种集喷涂、喷擦、刷涂、擦洗、环境感知和场景建模的机器人自主作业软件系统及相关算法。分析作业机器人的工作环境和功能需求,进行机械臂和感知模块的选型,制定三维建模和参数辨识方案,搭建焊缝检测作业系统;提出了一种基于机器人感知到的视觉和深度信息进行场景建模的算法,该算法可以输出检测关到作业面的位置、三维模型以及作业区域范围;提出了一种基于三维重构模型和作业区域范围进行作业轨迹规划的算法,并搭建了仿真平台验证运动轨迹的正确性;开展了管道定位与三维重构实验以及轨迹规划实验,实验的结果证明,提出的管道定位方法能够实现精确的点云数据采集和高精度的管道参数辨识,机械臂能够实现准确性高、连续性好的运动,满足管道焊缝检测作业的要求。

桥梁检测机器人运动分析与运动规划研究

针对桥梁检测机器人对于高墩、高厚度桥梁不同部位的便捷检测需求,开展五自由度桥检机器人运动分析与运动规划研究。采用改进型D-H方法进行正向运动学建模,通过解析方法求解两种形式的运动学逆解,推导雅可比矩阵,分析机器人的奇异位型;通过蒙特卡洛方法得到机器人工作空间,确定工作空间可覆盖待检测区域;由连杆力与力矩平衡方程推导各关节受力关系,基于关节负载能力开展运动规划研究。仿真分析结果证明了方法的可行性,说明机器人能够完成预定的工作任务需求。

不确定检测环境下强化学习覆盖路径规划研究

机器人扫描测量系统在汽车质量检测领域获得广泛应用,尤其数模环境下基于仿生优化算法的视点采样与路径规划研究取得较大进展。然而,基于名义数模环境的路径规划结果难以适用实际不确定性检测环境。为此,本文提出基于改进的蒙特卡洛树搜索的视点自适应采样方法,在线生成工业机器人运动轨迹。通过车门内板案例的对比分析,验证本文方法的有效性,为实现不确定制造环境下工艺路径的在线规划提供理论依据。

基于Transformer改进强化学习的无人机电力巡检规划

为实现无人机电力巡检过程的全自主决策,针对传统强化学习轨迹规划存在的收敛速度慢、易陷入局部最优的问题,基于Transformer模型改进深度强化学习,设计了电量约束下的无人机充电巡检决策算法。首先建立对电力巡检任务场景的能耗模型和马尔可夫决策模型。然后分别设计了基于图神经网络的静态编码器和基于门控循环的动态编码器以提取不同类型环境数据,同时设计了基于多头注意力机制的解码器,输出不定长的全局充电巡检策略序列以预测未来奖励。最后对收敛后的推理模型在电力巡检仿真环境进行验证。仿真结果表明,相比于传统强化学习,所提算法可以提取地图深层状态特征,路径能耗降低了26.61%,并具有更好的收敛性。