Mass customized shoe production: a highly reconfigurable robotic device for handling limp material

This paper describes the EUROShoeE (extended user-oriented shoe enterprise) Project which aims to aid the shoe industry to transform its goods from being mass produced to being mass customized. This paper focuses on the results obtained in the critical field of the flexible handling of limp, synthetic and natural materials. An innovative device that enables a multipoint grasp, firm hold, and gentle transfer of limp items is presented. The design process used fully implemented digital mock-ups and ran extended virtual trims to accomplish the analysis of the prospective fixtures dynamic behavior as a stand-alone device and as an integrated end-effector of a robotic system. The prototype of the robotic device was created, and performance tests were started.     

Modeling, analysis, and performance evaluation of a robotic grippersystem for limp material handling

This paper presents an analytical model of a flat surfaced robotic gripper designed to automate the process of reliable, rapid and distortion-free limp material handling. The designed gripper prototype is integrated with an industrial robot manipulator. The gripper geometry and its grasp stability are justified. Performance of the overall system is experimentally tested, based on a set of industry dictated operational constraints. It is found that the gripper system has high reliability, grasp stability, and that it is capable of rapid rates of manipulation.     

Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult то control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.     

Intelligent robotic gripper with adaptive grasping force

The on-off control robot gripper is widely employed in pick-and-place operations in Cartesian space for handling hard objects between two positions. Without contact force monitoring, it can not be applied in fragile or soft objects handling. Although, an appropriate grasping force or gripper opening for each target could be searched by trial-and-error process, it needs expensive force/torque sensor or an accurate gripper position controller. It has too expensive and complex control strategy disadvantages for most of industrial applications. In addition, it can not overcome the target slip problem due to mass uncertainty and dynamic factor. Here, an intelligent gripper is designed with embedded distributed control structure for overcoming the uncertainty of object’s mass and soft/hard features. A communication signal is specified to integrate both robot arm and gripper control kernels for executing the robotic position control and gripper force control functions in sequence. An efficient model-free intelligent fuzzy sliding mode control strategy is employed to design the position and force controllers of gripper, respectively. Experimental results of pick-and-place soft and hard objects with grasping force auto-tuning and anti-slip control strategy are shown by pictures to verify the dynamic performance of this distributed control system. The position and force tracking errors are less than 1 mm and 0.1 N, respectively.

     

Proposal of a shape adaptive gripper for robotic assembly tasks

This paper proposes a novel robotic gripper used for assembly tasks that can adaptively grasp objects with different shapes. The proposed hand has a combined structure between two kinds of shape adaptive mechanisms where one is the granular jamming and the other is a multi-finger mechanism driven by a single wire. Due to the effect of the two shape adaptive mechanisms, the pose of a grasped object does not change during an assembly operation. The proposed hand has four fingers where two are the active ones and the other two are the passive ones. The pose of the grasped object can be uniquely determined since the passive fingers are used to orient an object placed on a table before the active fingers are closed to grasp it. Assembly experiments of some kinds of parts are shown to validate the effectiveness of our proposed gripper.     

Design fundamentals of a reconfigurable robotic gripper system

Discusses the design and modeling fundamentals of a multi-degree-of-freedom reconfigurable robotic gripper system (RGS), designed to automate the process of limp material handling, reliably and without distortion, deformation, and/or folding. The reconfigurable gripper design draws upon a previously reported flat surfaced, fixed-dimensions gripper system (Kolluru et al., 1998). The design consists of four arms in a cross-bar configuration, with a flat surfaced, fixed dimensions, suction-based gripper unit mounted on each of the arms. The kinematic and dynamic performance of the reconfigurable RGS is analyzed theoretically and then validated using Integrated Design Engineering Analysis Software (I-DEAS) simulation software     

Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces

It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult tо analyze with conventional analytical methods. Here, a novel design of an adaptive neuro fuzzy inference system (ANFIS) for estimation contact forces of a new adaptive gripper is presented. Since the conventional analytical methods is a very challenging task, fuzzy logic based systems are considered as potential candidates for such an application. The main points of this paper are in explanation of kinetostatic analyzing of the new gripper structure using rigid body model with added compliance in every single joint. The experimental results can be used as training data for ANFIS network for estimation of gripping forces. An adaptive neuro-fuzzy network is used to approximate correlation between contact point locations and contact forces magnitudes. The simulation results presented in this paper show the effectiveness of the developed method. This system is capable to find any change in ratio of positions of the gripper contacts and magnitudes of the contact forces and thus indicates state of both finger phalanges.     

Prediction of part orientation error tolerance of a robotic gripper

This paper presents a model which predicts the part orientation error tolerance of a three-fingered robotic gripper. The concept of “self-alignment” is introduced, where the gripper uses the grasping process to bring the workpiece into its final state of orientation. The gripper and part are represented mathematically, and initial contact locations upon grasp closure determined. This information is used to solve for the contact forces present, and criteria are developed to determine if beneficial part motion resulting in self-alignment is expected. The results are visualized via a boundary projected on a reference plane below the part. The model is validated experimentally with a number of part configurations with favorable results. This method presents a useful tool by which the mechanical designer can quantitatively predict the performance of an intuitively designed gripping system.     

A modular simulation approach for automated material handling systems

In this paper, the authors present a modular simulation approach for the evaluation of automated material handling systems (AMHSs). The approach involves the use of high level modularity so that alternative AMHS scenarios can be evaluated using identical or nearly identical source code. The approach is implemented using the AutoMod simulation platform, and the efficacy of the strategy is demonstrated through a case study with the United States Postal Service. Results indicate that the approach is viable and that it is of value for the evaluation of AMHS scenarios in general.     

A proactive material handling method for CPS enabled shop-floor

Cyber physical system (CPS) enables companies to keep high traceability and controllability in manufacturing for better quality and improved productivity. However, several challenges including excessively long waiting time and a serious waste of energy still exist on the shop-floor where limited buffer exists for each machine (e.g., shop-floor that manufactures large-size products). The production logistics tasks are released after work-in-processes (WIPs) are processed, and the machines will be occupied before trolleys arrival when using passive material handling strategy. To address this issue, a proactive material handling method for CPS enabled shop-floor (CPS-PMH) is proposed. Firstly, the manufacturing resources (machines and trolleys) are made smart by applying CPS technologies so that they are able to sense, act, interact and behave within a smart environment. Secondly, a shop-floor digital twin model is created, aiming to reflect their status just like real-life objects, and key production performance indicators can be analysed timely. Then, a time-weighted multiple linear regression method (TWMLR) is proposed to forecast the remaining processing time of WIPs. A proactive material handling model is designed to allocate smart trolleys optimally. Finally, a case study from Southern China is used to validate the proposed method and results show that the proposed CPS-PMH can largely reduce the total non-value-added energy consumption of manufacturing resources and optimize the routes of smart trolleys.     

A hybrid drive parallel arm for heavy material handling

This article discusses a hybrid drive parallel arm driven by cables and cylinders in order to design a compact handling arm, thus enlarging its workspace. Our major objective is to develop a dexterous arm capable of controlling six-degree-of-freedom (DOF) motion of heavy materials. The article includes a basic concept of parallel mechanisms with hybrid actuation, comparisons and evaluations of some types of hybrid mechanisms based on its kinematics and statics, and finally introduces a designed prototype arm     

A cooperative control model for multiagent-based material handling systems

This paper presents an Artificial Immune System (AIS)-based model for cooperative control of multiagent systems. This cooperative control model describes collective behaviors of autonomous agents, known as the AIS agents that are exemplified by the regulated activities performed by individual agents under the computation paradigm of Artificial Immune System. The regulations and emergence of agent behaviors are derived from the immune threshold measures that determine those activities performed by the AIS agents at an individual level. These threshold measures together with the collective behavioral model defined the cooperative control of the AIS-based control framework under which AIS agents behave and act strategically according to the changing environment. The cooperative control model is presented under the three domains, namely exploration, achievement and cooperation domains where AIS agents operate. In this research, we implemented the proposed cooperative control model with a case study of automated material handling with a group of AIS agents that cooperate to achieve the defined tasks.     

Automated stripping: a robotic handling cell for garmentmanufacture

The cutting room is currently the most highly automated part of the garment industry's manufacturing cycle. The next step, stripping the pieces from the cutting table and sorting them for assembly, is still highly labor intensive. At De Montfort University a robotic handling cell to perform the stripping operation has been developed and demonstrated     

Inference engine for material handling selection

In this paper we describe the inference engine of an expert system for material handling method and equipment selection. The system branches through a tree guided by collected data essential for solving the material handling equation: Material + Move → Method. The equation is solved using rules developed to handle relationships between elements and factors of the equation. While the engine in its current status is limited to a “ first cut” in the equipment selection process, it is of value to nonspecialist industrialist. Plans are for the final system to provide its user with access to vendors specialized in particular handing operations.     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

Robot handling of flat textile materials

The authors present a robotic system incorporating vision and force/torque sensing for handling flat textile materials and describe the results of experiments to measure the accuracy and reliability of the system for a variety of representative handling tasks for textile materials     

Applying vision guidance in robotic food handling

This paper presents the development of a visually guided robotic system for handling food products that are presented in an unstructured manner. Vision guided grasping is described in the context of vector manipulations. The grasp vector is formulated using both online visual information and off-line data. Problems due to the constraints imposed in food handling applications are addressed     

仓库物料运输系统多目标调度方法

为了提高仓库物料运输系统的效率,建立了考虑任务完成和AGV小车运行时间的多目标的调度模型,并提出了云模型影响下的出入库任务优先级分配策略。在此基础上,利用文化算法对问题进行了求解,讨论了算法的规则设计。最后,仿真结果表明,该算法是可行的,可以获得较好的效果,为AGV小车在仓库物料运输系统的应用提供了一种研究途径。