工业机器人虚拟样机系统的研究

该文提出将虚拟样机技术应用于工业机器人仿真研究过程，研究与开发工业机器人虚拟样机系统，首先说明虚拟样机技术并分析其关键技术，然后说明了工业机器人虚拟样机系统的构成与样机系统在机器人仿具研究中的研究内容，总结该项技术主要解决以下两方面的问题，即机器人仿真研究中的系统集成及为以机器人为主体的生产线虚拟设计，验证环境提供底层的数字化环境。     

Resource-efficient routing and scheduling of time-constrained streaming communication on networks-on-chip

Network-on-chip-based multiprocessor systems-on-chip are considered as future embedded systems platforms. One of the steps in mapping an application onto such a parallel platform involves scheduling the communication on the network-on-chip. This paper presents different scheduling strategies that minimize resource usage by exploiting all scheduling freedom offered by networks-on-chip. It also introduces a technique to take the dynamism in applications into account when scheduling the communication of an application on the network-on-chip while minimizing the resource usage. Our experiments show that resource-utilization is improved when compared to existing techniques.     

Transformative CAD based industrial robot program generation

Industrial robots are widely used in various processes of surface manufacturing, such as spray painting, spray forming, rapid tooling, spray coating, and polishing. Robot programming for these applications is still time consuming and costly. Typical teaching methods are not cost effective and efficient. There are many off-line programming methods developed to reduce the robot programming effort. However, these methods suffer many practical issues, such as cable/hose tangling, robot configuration, collision, and reachability. To solve these problems, this paper discusses a new method to generate robot programs. Since industrial robots have been used in production for decades, there are many robot programs for different parts generated by the robot programmers. These robot programs, which contain not only the robot paths, but also the programmers' knowledge and process parameters, can be transformed to generate new robot programs for similar parts. In this paper, a transformative robot program generation method is developed based on the existing ones in the database. Experiments were performed to validate the developed methodology. The results are very promising in reducing the programming efforts in surface manufacturing.     

Development of a decision support system for robot selection

With the availability of more different robot types and models along with their separate specifications, selecting the most appropriate robot is becoming more difficult and complicated for companies. Furthermore, a common set of robot selection criteria is not available for the decision makers. In this study, a two-phase robot selection decision support system, namely ROBSEL, is developed to help the decision makers in their robot selection decisions. In development of ROBSEL, an independent set of criteria is obtained first and arranged in the Fuzzy Analytical Hierarchy Process (FAHP) decision hierarchy. In the first elimination phase of the decision support system, the user obtains the feasible set of robots by providing limited values for the 15 requirements. ROBSEL, then, uses FAHP decision hierarchy to rank the feasible robots in the second phase. ROBSEL is illustrated and tested and several critical issues in its practical usage are explored in the paper. The applications of ROBSEL show that ROBSEL is a useful, practical and easy to use robot selection tool and improves robot selection decisions in the companies.     

Bicriteria scheduling of a material handling robot in an m-machine cell to minimize the energy consumption of the robot and the cycle time

This study considers a flowshop type production system consisting of m machines. A material handling robot transports the parts between the machines and loads and unloads the machines. We consider the sequencing of the robot moves and determining the speeds of these moves simultaneously. These decisions affect both the robot’s energy consumption and the production speed of the system. In this study, these two objectives are considered simultaneously. We propose a second order cone programming formulation to find Pareto efficient solutions. We also develop a heuristic algorithm that finds a set of approximate Pareto efficient solutions. The conic formulation can find robot schedules for small cells with less number of machines in reasonable computation times. Our heuristic algorithm can generate a large set of approximate Pareto efficient solutions in a very short computational time. Proposed solution approaches help the decision-maker to achieve the best trade-off between the throughput of a cell and the energy efficiency of a material handling robot.     

关节驱动系统非线性摩擦最小能耗分析

节能降耗是现代工业生产的普遍需要。针对直流电机和减速齿轮驱动下的机械臂关节,详细论述了非线性摩擦最小能耗的分析和计算方法。在PTP位置控制系统中,耗散能量的最小化取决于最佳穿越时间。仿真和实验的结果证明了这一结论。     

基于DSP的足球机器人目标识别

目标识别是足球机器人视觉子系统的关键,论文对M i r o S o t中型组足球机器人视觉子系统的预处理和目标识别提出了改进的算法。基于D S P提出了改进的提取目标物体几何特征进行模式匹配的方法,实现了对复杂环境中目标物体的识别,并通过Code Composer Studio(CCS)进行了仿真测试,有较高的识别率。     

GPU/CPU异构系统任务节能调度方法仿真

研究GPU/CPU异构系统任务调度的节能问题.与传统同构体系结构相比,异构系统任务调度呈现较大的随机性和不定性,GPU/CPU异构系统中时间间隙片段呈现了较大的随机性,导致传统调度方法很难建立规则的描述时间片段的模型,调度能耗较高.为解决上述问题,提出了一种改进功耗优化的GPU/CPU异构环境下的任务调度算法,将任务关系图按照依赖关系计算量拆分,并分配到计算节点.在计算节点内根据权重法的思想,统计所有计算节点的处理情况,进而将节点内的子任务调度到合适的处理器.实验结果表明,在不影响应用性能的前提下,降低了异构系统的能耗开销,优化效果明显.     

基于光伏预测的微电网能源随机优化调度

可再生能源的间歇性和负荷的随机性对微电网能源管理系统（ EMS）产生了巨大的挑战。在随机环境下的能源优化调度问题在微电网的研究中具有重要意义。以微电网中光伏发电系统的功率预测为基础,将光伏预测误差当做随机变量,建立了一种基于期望模型的能源随机优化调度模型。用Monte Carlo模拟方法生成了光伏发电预测误差的情景集,应用粒子群优化算法来解决随机优化调度模型。通过与确定性模型产生的调度方案相对比,证明了随机优化调度模型更加有效。     

Energy analysis of robot task motions

An energy criterion for choosing the best type of manipulator for a specified task is developed. First, the energy required to perform the robotic task is calculated. Then the lower bound of the mechanical energy consumed by the various kinds of manipulators during their motion, while performing a task, is calculated. Thus, the efficiency of a manipulator for the task is determined. Some examples show that the proper selection of the manipulator configuration can reduce the required energy to a quarter of that of a less suitable one. Once the most suitable manipulator is chosen, the criterion for its most energy efficient motion is developed. The model takes into account the kinematic configuration of the robot, gravitational and other external and internal forces acting on the robot during its operation, and the electric motor driving the robot links. Energy optimization of different paths of motion in joint coordinates is discussed briefly.     

On generating the motion of industrial robot manipulators

In this study, an intelligent search algorithm is proposed to define the path that leads to the desired position and orientation of an industrial robot׳s manipulator end effector. The search algorithm gradually approaches the desired configuration by selecting and evaluating a number of alternative robot׳s configurations. A grid of the robot׳s alternative configurations is constructed using a set of parameters which are reducing the search space to minimize the computational time. In the evaluation of the alternatives, multiple criteria are used in order for the different requirements to be fulfilled. The alternative configurations are generated with emphasis being given to the robot׳s joints that mainly affect the position of the end effector. Grid resolution and size parameters are set on the basis of the desired output. High resolution is used for a smooth path and lower for a rough estimation, by providing only a number of the intermediate points to the goal position. The path derived is a series of robot configurations. This method provides an inexperienced robot programmer with flexibility to generate automatically a robotic path that would fulfill the desired criteria without having to record intermediate points to the goal position.     

Kinematic calibration of a five-bar planar parallel robot using all working modes

We present a simple low-cost calibration procedure that improves the planar positioning accuracy of a double-arm SCARA robot to levels difficult or impossible to achieve using an equivalent serial robot. Measurements are based on the use of five custom designed magnetic tooling balls fixed to the periphery of a detachable working plate. Three of these tooling balls define the world reference frame of the robot, and the positions of the centers of all balls are measured on a CMM. A special magnetic cup end-effector is used. Measurements are taken by manually positioning the end-effector over each of the tooling balls, with each of the maximum of four possible robot configurations. Each of these measurements is repeatable to within±0.015 mm. The robot calibration model includes all 12 kinematic parameters, and the calibration method used is based on the linearization of the direct kinematics model in each calibration configuration. The optimal number and location of the tooling balls is obtained by studying the observability index. Finally, an experimental validation at 14 additional tooling balls shows that the maximum position error with respect to the world frame is reduced to 0.080 mm within the entire robot's workspace of 600 mm×600 mm.     

Task-based optimization of reconfigurable robot manipulators

Reconfigurable Manipulators are structurally redundant robots that utilize a subset of their joints to perform a specific task optimally. This paper presents a method of finding a task-based optimal configuration for a new type of reconfigurable robot manipulator, called the modular autonomously reconfigurable serial (MARS) manipulator. The reconfiguration optimization treats the joint space of the MARS manipulator as a 12-dimensional smooth configuration manifold. The manifold is discretized and ranked based on a variety of criteria, and then clustered into attractive and repellent regions. The user then specifies which regions are desired in the target configuration, and the manifold is reduced in dimension in order to maximize the number of attractive regions and minimize the number of repellent regions. Six manipulator configurations are synthesized using this approach, and their effectiveness is compared.     

Development of CAM system based on industrial robotic servo controller without using robot language

This paper describes the development of a robotic CAM system for an articulated industrial robot RV1A from the view point of robotic servo controller. It is defined here that the CAM system includes an important function which allows an industrial robot to move along cutter location data (CL data) consisting of position and orientation components. In addition, the developed CAM system has a high applicability to other industrial robots whose servo systems are technically opened to end-users. The developed robotic CAM system works as a straightforward interface between a general CAD/CAM and an industrial robot. At the present stage, the relationship between CAD/CAM and industrial robots is not well established compared to NC machine tools that are widely spread in manufacturing industries. The CAM systems for NC machine tools are already established, however, the CAM system for industrial robots has not been sufficiently considered and developed yet. A teaching pendant is generally used to obtain position and orientation data of the arm tip before an industrial robot works. Here, in order to enhance the relationship between a conventional CAD/CAM system and an industrial robot, a simple and straightforward CAM system without using any robot language is developed and implemented. The basic design of the robotic CAM system and the experimental results are presented in this paper.     

自适应缓冲控制的移动机器人分布式多跳路由算法

针对移动机器人平台下60GHz无线信号太弱而无法存在于高衰减的长距离或非直线环境的问题,提出了一种基于自适应缓冲控制的分布式鲁棒多跳路由算法。该算法的核心思想是通过求解单位时间平均预期功耗总和最小化和每单位时隙缓冲稳定性的联合随机优化来确定功率分配量,使每个机器人平台能够计算从它自己的缓冲区传输数据包的功率分配量,从而优化整个系统。拥有二十个移动监控机器人平台的仿真实验验证了提出的算法的有效性。实验结果表明,相比分布式多跳路由协议中较为著名的自组织网络按需距离向量(AODV)算法,提出的算法在平均功耗和缓冲占有率方面均有优势,在权重因子为0.5和2时,缓冲占有率分别为AODV的0.14和0.34倍,平均功耗也有所降低。     

Perception of safe robot idle time in virtual reality and real industrial environments

The main objective of this study was to investigate human perception of safe idle time of an industrial robot in a virtual reality environment. Studying operators' perception of robot operational characteristics such as safe robot idle time can help develop hazard prevention strategies, and ultimately improve robot safety. Results of data collected from 32 participants showed that robot size and speed had significant effects on the perception of safe robot idle time. This study also examined operators' perceived acceptability level for the robot speed, perceived level of hazard of robot motion, perceived chance of error, and self-reported mental workload. Results of this study were compared to the findings of Rahimi, M., Karwowski, W. [1990. Human perception of robot safe speed and idle time. Behaviour & Information Technology 9(5), 381–389], in which their experiment was conducted in a real industrial environment. This study demonstrated the feasibility of testing human perception of dynamic moving objects in a virtual reality environment. The virtual reality technology is believed to be capable of modeling a complex machinery system such as a robotic system.Relevance to industry: Human perception of the operational characteristics of industrial robots is an important concern for robot safety since misperception can cause robot operators to err, which in turn can cause injuries and fatalities. Through this study we (1) understand human perception, safety behavior, and decision making in a robotic system and (2) demonstrate the capability of modeling a complex machinery system using virtual reality technology. Our experiments designed to study human perception of safe robot idle time could lead to safety interventions and guidelines or hazard prevention strategy development.     

旋翼空中机器人系统架构及设计

设计并验证了某型旋翼空中机器人的系统架构。整个空中机器人系统由直升机和地面站两部分组成。直升机是空中机器人的主体,可以自主飞行并完成指定任务。地面站用于监控无人直升机的飞行,并实现人机交互等多项功能。此外,地面站还可通过视觉导航系统引导直升机的自主着陆。直升机与地面站之间通过指令数字链路和视频模拟链路进行信息交互和实时通讯。经实际飞行验证,该空中机器人系统具有鲁棒和实时的特点,能实现直升机自主飞行和自主起降功能。     

Hierarchical stochastic modeling and optimization for petroleum field development under geological uncertainty

Real-world simulation optimization (SO) problems entail complex system modeling and expensive stochastic simulation. Existing SO algorithms may not be applicable for such SO problems because they often evaluate a large number of solutions with many simulation calls. We propose an integrated solution method for practical SO problems based on a hierarchical stochastic modeling and optimization (HSMO) approach. This method models and optimizes the studied system at increasing levels of accuracy by hierarchical sampling with a selected set of principal parameters. We demonstrate the efficiency of HSMO using the example problem of Brugge oil field development under geological uncertainty.     

Selection, identification and comparison of industrial robots using digraph and matrix methods

In the present work, a methodology based on digraph and matrix methods is developed for evaluation of alternative industrial robots. A robot selection index is proposed that evaluates and ranks robots for a given industrial application. The index is obtained from a robot selection attributes function, obtained from the robot selection attributes digraph. The digraph is developed considering robot selection attributes and their relative importance for the application considered. A step by step procedure for evaluation of robot selection index is suggested. Coefficients of similarity and dissimilarity and the identification sets are also proposed. These are obtained from the robot selection attributes function and are useful for easy storage and retrieval of the data. Two examples are included to illustrate the approach.     

BN-LSTM-based energy consumption modeling approach for an industrial robot manipulator

Industrial robots (IRs) are widely used to increase productivity and efficiency in manufacturing industries. Therefore, it is critical to reduce the energy consumption of IRs to maximize their use in polishing, assembly, welding, and handling tasks. This study adopted a data-driven modeling approach using a batch-normalized long short-term memory (BN-LSTM) network to construct a robust energy-consumption prediction model for IRs. The adopted method applies batch normalization (BN) to the input-to-hidden transition to allow faster convergence of the model. We compared the prediction accuracy with that of the 1D-ResNet14 model in a UR (UR3e and UR10e) public database. The adopted model achieved a root mean square (RMS) error of 2.82 W compared with the error of 6.52 W achieved by 1D-ResNet14 model prediction, indicating a performance improvement of 56.74%. We also compared the prediction accuracy over the UR3e dataset using machine learning and deep learning models, such as regression trees, linear regression, ensemble trees, support vector regression, multilayer perceptron, and convolutional neural network-gated recurrent unit. Furthermore, the layers of the well-trained UR3e power model were transferred to the UR10e cobot to construct a rapid power model with 80% reduced UR10e datasets. This transfer learning approach showed an RMS error of 3.67 W, outperforming the 1D-ResNet14 model (RMS error: 4.78 W). Finally, the BN-LSTM model was validated using unseen test datasets from the Yaskawa polishing motion task, with an average prediction accuracy of 99%.