Nature inspired algorithms to optimize robot workcell layouts

Multi-objective layout optimization methods for the conceptual design of robot cellular manufacturing systems are proposed in this paper. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. Layout area, operation time and manipulability of robot are the three important criteria when it comes to designing manufacturing system. The use of nature inspired algorithms are not extensively explored to optimize robot workcell layouts. The contribution in this paper is the use of five nature-inspired algorithms, viz. genetic algorithm (GA), differential evolution (DE), artificial bee colony (ABC), charge search system (CSS) and particle swarm optimization (PSO) algorithms and to optimize the three design criteria simultaneously. Non-dominated sorting genetic algorithm-II is used to handle multiple objectives and to obtain pareto solutions for the problems considered. The performance of sequence pair and B*-Tree layout representation schemes are also evaluated. It is found that sequence pair scheme performs better than B*-Tree representation and it is used in the algorithms. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that PSO performs better over the other algorithms in terms of solution quality.     

A problem space genetic algorithm in multiobjective optimization

In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in flexible manufacturing systems. The PSGA is used to generate approximately efficient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the first implementation of PSGA to solve a multiobjective optimization problem (MOP). In multiobjective search, the key issues are guiding the search towards the global Pareto-optimal set and maintaining diversity. A new fitness assignment method, which is used in PSGA, is proposed to find a well-diversified, uniformly distributed set of solutions that are close to the global Pareto set. The proposed fitness assignment method is a combination of a nondominated sorting based method which is most commonly used in multiobjective optimization literature and aggregation of objectives method which is popular in the operations research literature. The quality of the Pareto-optimal set is evaluated by using the performance measures developed for multiobjective optimization problems.     

Computer-aided analysis of reconfigurable fixtures and sheet metal parts for robotic drilling

This paper presents an automated manufacturing system for drilling sheet metal parts. All stand-alone systems such as the robot, a set of reconfigurable fixtures and the CAD/CAM workstation have been integrated into a flexible manufacturing system. This system analyzes and evaluates a given fixturing layout and assembles the reconfigurable fixtures automatically using a robot manipulator. An optimum fixturing layout and assembly are achieved by examining the workpart from a stress-strain point of view. In addition, issues such as geometric constraints, yielding and buckling, database design, collision detection, fixturing sequential control and the automatic assembly of fixture elements are considered. The computational and analytical concepts for the reconfigurable fixturing and drilling system are also presented in this paper.     

Integration of design and manufacturing for structural shape optimization

This paper presents an integrated design and manufacturing approach that supports shape optimization of structural components. The approach starts from a primitive concept stage, where boundary and loading conditions of the structural component are given to the designer. Topology optimization is conducted for an initial structural layout. The discretized structural layout is smoothed using parametric B-Spline surfaces. The B-Spline surfaces are imported into a CAD system to construct parametric solid models for shape optimization. Virtual manufacturing (VM) techniques are employed to ensure that the optimized shape can be manufactured at a reasonable cost. The solid freeform fabrication (SFF) system fabricates physical prototypes of the structure for design verification. Finally, a computer numerical control (CNC) machine is employed to fabricate functional parts as well as mold or die for mass production of the structural component. The main contribution of the paper is incorporating manufacturing into the design process, where manufacturing cost is considered for design. In addition, the overall design process starts from a primitive stage and ends with functional parts. A 3D tracked vehicle roadarm is employed throughout this paper to illustrate the overall design process and various techniques involved.     

Component allocation and supporting frame topology optimization using global search algorithm and morphing mesh

This paper proposes a stepwise structural design methodology where the component layout and the supporting frame structure is sequentially found using global search algorithm and topology optimization. In the component layout design step, the genetic algorithm is used to handle system level multiobjective problem where the optimal locations of multiple components are searched. Based on the layout design searched, a new Topology Optimization method based on Morphing Mesh technique (TOMM) is applied to obtain the frame structure topology while adjusting the component locations simultaneously. TOMM is based on the SIMP method with morphable FE mesh, and component relocation and frame design is simultaneously done using two kinds of design variables: topology design variables and morphing design variables. Two examples are studied in this paper. First, TOMM method is applied to a simple cantilever beam problem to validate the proposed design methodology and justify inclusion of morphing design variables. Then the stepwise design methodology is applied to the commercial Boeing 757 aircraft wing design problem for the optimal placement of multiple components (subsystems) and the optimal supporting frame structure around them. Additional constraint on the weight balance is included and the corresponding design sensitivity is formulated. The benefit of using the global search algorithm (genetic algorithm) is discussed in terms of finding the global optimum and independency of initial design guess. It has been proved that the proposed stepwise method can provide innovative design insight for complex modern engineering systems with multi-component structures.     

Control-structure integrated multiobjective design for flexible spacecraft using MOEA/D

This paper proposes a multiobjective optimization method for the control-structure integrated design of flexible spacecraft to reduce the total mass and optimize the control performance. The equations of motion for flexible spacecraft are derived from the Lagrange’s principle and the assumed modes method. The design variables are the structural dimensions of the flexible structure and controller parameters. The objectives and constraints are derived from structure and control performance indexes. The objectives include total mass, control cost, and vibrational energy, and the constraints include the stability of the closed-loop system, settling time, overshoot, maximum control, and maximum vibrational displacement of the tip. A modified version of the multiobjective evolutionary algorithm based on decomposition (MOEA/D) with our proposed hybrid constraint handling method is proposed for optimization. As a case study, it has been applied to a spacecraft with symmetrically installed flexible appendages to find optimal tradeoffs in control-structure design. The simulation results show that the multiobjective optimization method for the control-structure integrated design of flexible spacecraft is feasible and effective, and could give an improvement of structural and control designs.     

Loop layout design problem in flexible manufacturing systems using genetic algorithms

A common layout for flexible manufacturing systems is a loop network with machines arranged in a cycle and materials transported in only one direction around the cycle. Traffic congestion is usually used as the measure for evaluating a loop layout, which is defined as the number of times a part traverses the loop before its processing is completed. This paper investigates the problem of designing a loop layout system with genetic algorithms. The essence of the problem is how to determine the order of machines around the loop subject to a set of part-route constraints so as to optimize some measures. A hybrid approach of genetic algorithms and neighborhood search is developed for solving the problem. The proposed method is tested on hypothetical problems. Computational results demonstrate that genetic algorithms can be a promising approach for loop layout design in flexible manufacturing systems. Two typical measures, the minsum and minmax congestion measures, are examined and computational experiments show that the minsum approach outperforms the minmax approach.     

Concurrent optimal design of modular robotic configuration

This paper presents a new optimization design methodology that is applicable to modular systems. This new methodology is called concurrent optimization design method (CODM). A modular robot is taken as a case study. The CODM is superior to the existing methods for modular robot configuration design in the sense that traditional type synthesis and dimensional synthesis now can be treated once. This mathematically implies that (i) variables are defined for both types and dimensions, and (ii) all the variables are defined in one optimization problem formulation. This paper illustrates that, in fact, optimization design for modular architectures necessitates a multiobjective optimization problem. A genetic algorithm is used to solve for this complex optimization model which contains both discrete and continuous variables. © 2001 John Wiley & Sons, Inc.     

A methodology for implementation of mobile robot in adaptive manufacturing environments

With the rapid development of technologies, many production systems and modes has been advanced with respect to manufacturing, management and information fields. The paper deals with the problem of the implementation of an autonomous industrial mobile robot in real-world industrial applications in which all these fields are considered, namely mobile robot technology, planning and scheduling and communication. A methodology for implementation consisting of: a mobile robot system design (Little Helper prototype), an appropriate industrial application (multiple-part feeding), an implementation concept for the industrial application (the Bartender Concept), a mathematical model and a genetic algorithm-based heuristic is proposed. Furthermore, in order for the mobile robot to work properly in a flexible (cloud-based) manufacturing environment, the communications and exchange of data between the mobile robot with other manufacturing systems and shop-floor operators are addressed in the methodology. The proposed methodology provides insight into how mobile robot technology and abilities contribute to cloud manufacturing systems. A real-world demonstration at an impeller production line in a factory and computational experiments are conducted to demonstrate the effectiveness of the proposed methodology.     

Design and optimization of integrated E-supply chain for agile and environmentally conscious manufacturing

An agile and environmentally conscious manufacturing paradigm refers to the ability to reconfigure a flexible system quickly, economically, and environmentally responsibly. In modern manufacturing enterprises, e-supply chains integrate Internet and web-based electronic market and are promising systems to achieve agility. A key issue in the strategic logistic planning of integrated e-supply chains (IESCs) is the configuration of the partner network. This paper proposes a single- and multiobjective optimization model to configure the network of IESCs. Considering an Internet-based distributed manufacturing system composed of different stages connected by material and information links, a procedure is presented to select the appropriate links. A set of performance indices is associated with the network links. Single-criterion and multicriteria optimization models are presented under structural constraint definitions. The integer linear programming (ILP) problem solution provides different network structures that allow to improve supply chain (SC) flexibility, agility, and environmental performance in the design process. The proposed optimization strategy is applied to two case studies describing two networks for desktop computer production.     

A method for grinding removal control of a robot belt grinding system

As a kind of manufacturing system with a flexible grinder, the material removal of a robot belt grinding system is related to a variety of factors, such as workpiece shape, contact force, robot velocity, and belt wear. Some factors of the grinding process are time-variant. Therefore, it is a challenge to control grinding removal precisely for free-formed surfaces. To develop a high-quality robot grinding system, an off-line planning method for the control parameters of the grinding robot based on an adaptive modeling method is proposed in this paper. First, we built an adaptive model based on statistic machine learning. By transferring the old samples into the new samples space formed by the in-situ measurement data, the adaptive model can track the dynamic working conditions more rapidly. Based on the adaptive model the robot control parameters are calculated using the cooperative particle swarm optimization in this paper. The optimization method aims to smoothen the trajectories of the control parameters of the robot and shorten the response time in the transition process. The results of the blade grinding experiments demonstrate that this approach can control the material removal of the grinding system effectively.     

制造系统中的单向环型设备布局设计

提出一种优化建模与虚拟现实技术相结合的求解策略，较好地解决了制造系统中的单向环型设备布局问题．研究该问题的固有特性，提出三条定理，构建了一个启发式算法，并实现了一个沉浸式虚拟布局设计的例子．     

Energy-optimal layout design of robotic work cells: Potential assessment on an industrial case study

This paper presents a new method for optimizing the layout position of several Industrial Robots (IRs) placed within manufacturing work-cells, in order to execute a set of specified tasks with the minimum energy consumption. At first, a mechatronic model of an anthropomorphous IR is developed, by leveraging on the Modelica/Dymola built-in capabilities. The IR sub-system components (namely mechanical structure, actuators, power electronic and control logics) are modeled with the level of detail strictly necessary for an accurate prediction of the system power consumption, while assuring efficient computational efforts. Secondly, once each IR task is assigned, the optimal work-cell layout is computed by using proper optimization techniques, which numerically retrieve the IR base position corresponding to the minimum energy consumption. As an output to this second development stage, a set of color/contour maps is provided, that depicts both energy demand and time required for the task completion as function of the robot position in the cell to support the designer in the development of an energy-efficient layout. At last, two robotic manufacturing work-cells are set-up within the Delmia Robotics environment, in order to provide a benchmark case study for the evaluation of any energy saving potential. Numerical results confirm possible savings up to 20% with respect to state-of-the-art work-cell design practice.     

Automatic Tuning of a Fuzzy Visual System Using Evolutionary Algorithms: Single-Objective Versus Multiobjective Approaches

One of the main advantages of fuzzy systems is their ability to design comprehensible models of real-world systems, thanks to the use of a fuzzy rule structure easily interpretable by human beings. This is especially useful for the design of fuzzy logic controllers, where the knowledge base can be extracted from expert knowledge. Even more, the availability of a readable structure allows the human expert to customize the fuzzy controller to different environments by manually tuning its components. Nevertheless, this tuning task is usually a time-consuming procedure when done manually, especially when several measures are considered to evaluate the controller performance, and thus the interest in the design of automatic tuning procedures for fuzzy systems has increased along the last few years. In this paper, we tackle the tuning of the fuzzy membership functions of a fuzzy visual system for autonomous robots. This fuzzy visual system is based on a hierarchical structure of three different fuzzy classifiers, whose combined action allows the robot to detect the presence of doors in the images captured by its camera. Although the global knowledge represented in the fuzzy system knowledge base makes it perform properly in the door detection task, its adaptation to the specific conditions of the environment where the robot is operating can significantly improve the classification accuracy. However, the tuning procedure is complex as two different performance indexes are involved in the optimization process (true positive and false positive detections), thus becoming a multiobjective problem. Hence, in order to automatically put the fuzzy system tuning into effect, different single and multiobjective evolutionary algorithms are considered to optimize the two criteria, and their behavior in problem solving is compared.     

Fuzzy multiobjective optimization of truss-structures using genetic algorithm

In this paper, a method for solving fuzzy multiobjective optimization of space truss with a genetic algorithm is proposed. This method enables a flexible method for optimal system design by applying fuzzy objectives and fuzzy constraints. The displacement, tensile stress, fuzzy sets, membership functions and minimum size constraints are considered in formulation of the design problem. An algorithm was developed by using MATLAB programming. The algorithm is illustrated on 56-bar space truss system design problem and the results are discussed.     

基于RMC的可重构制造系统设备布局优化研究*

为实现多品种变批量生产制造系统阵列式布局的动态重构,提出一种新的设备布局优化方法.建立了可重构制造系统(RMS)设备优化选择数学模型,设计了基于蚁群优化和阶序聚类算法的可重构制造单元(RMC)动态重构算法.以交货期内最小成本为目标,引入系统复杂度和系统响应度从系统能力角度完善了实现RMS重构的约束条件,最终完成了可重构制造系统设备布局优化.最后通过布局实例仿真验证该方法的可行性和有效性.     

A design of a controller as a component of a robotic manufacturing system

This paper specifies the functional design of a robot or machine tool controller suitable for inclusion in robotic manufacturing systems. The principle interest is the unmanned manufacturing cells which are being developed as major components of new manufacturing facilities. The functional specifications are motivated by recent experiences with the creation of a prototype cell for an open die forging process. There are two key technical design requirements. The first relates to the robot or machine tool being controlled. The controller must direct the machine actions, and, for unmanned operation, the controller must receive and process sensor information for process modification and for fault tolerance. The second relates to communication with the central cell computer (the host). The operation of an unmanned system requires a robot communication channel between the host and the controller; the controller must also respond to a variety of instructions transmitted from the host. Additional controller design requirements are imposed by economics. The proposed controller can be used today, and can develop in an evolutionary manner to meet the needs of future manufacturing systems. The evolutionary development is made possible by modular design, organized in a hierarchical manner.     

Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey

A manufacturing system able to perform a high variety of tasks requires different types of resources. Fully automated systems using robots possess high speed, accuracy, tirelessness, and force, but they are expensive. On the other hand, human workers are intelligent, creative, flexible, and able to work with different tools in different situations. A combination of these resources forms a human-machine/robot (hybrid) system, where humans and robots perform a variety of tasks (manual, automated, and hybrid tasks) in a shared workspace. Contrarily to the existing surveys, this study is dedicated to operations management problems (focusing on the applications and features) for human and machine/robot collaborative systems in manufacturing. This research is divided into two types of interactions between human and automated components in manufacturing and assembly systems: dual resource constrained (DRC) and human-robot collaboration (HRC) optimization problems. Moreover, different characteristics of the workforce and machines/robots such as heterogeneity, homogeneity, ergonomics, and flexibility are introduced. Finally, this paper identifies the optimization challenges and problems for hybrid systems. The existing literature on HRC focuses mainly on the robotic point of view and not on the operations management and optimization aspects. Therefore, the future research directions include the design of models and methods to optimize HRC systems in terms of ergonomics, safety, and throughput. In addition, studying flexibility and reconfigurability in hybrid systems is one of the main research avenues for future research.     

基于蒙特卡洛模拟的机器人工作站规划

机器人装配工作站是机器人柔性装配系统的主要组成部分。本文采用蒙特卡罗原理研究作业系统误差、机器人位姿误差补偿和机器人柔顺性对机器人装配过程的影响，从而确定上料装置和定位装置的最优位姿，本文所提出的工作站规划方法对设计机器人装配工作站具有实用价值。     

A support-design framework for Cooperative Robots systems in labor-intensive manufacturing processes

Manufacturing processes and industrial systems gradually change their traditional layouts and configurations, preparing to introduce novel integrated human-robot technologies as collaborative robots and exoskeletons. Whether mass customization of lot size and the production mix discourages the adoption of capital-intensive automation, collaborative robots become affordable and effective and a hotspot of the debate on manufacturing systems. This paper provides a novel support-design framework for the cooperative robot system in labor-intensive manufacturing processes to aid layout and task scheduling design. Through an iterative closed-loop methodology, this framework explores the impact of a cooperative robot in a labour-intensive manufacturing system like the production facility of a food service company. The framework leads the designer through the re-layout of the end-of-line, the economic and technical feasibility analyses, using simulation to estimate payback and ergonomics benefits for workers. Within the proposed layout, we state that adopting a cooperative cobot for the end-of-line is affordable and ergonomically convenient without representing a safety threat for workers. The testbed confirms the framework as an enabling tool for human-robot technologies integration in current manufacturing systems under budget and workers-driven constraints.