Showing papers in "Robotics and Computer-integrated Manufacturing in 2013"

Absolute calibration of an ABB IRB 1600 robot using a laser tracker

Abstract: The absolute accuracy of an ABB IRB 1600 industrial robot is improved using a 29-parameter calibration model, developed after extensive experimentation. The error model takes into account all possible geometric errors (25 geometric error parameters to be identified through optimization, in addition to the pose parameters for the base and tool frames and four error parameters related to the compliance in joints 2, 3, 4 and 5). The least squares optimization technique is used to find the 29 error parameters that best fit the measures acquired with a laser tracker. Contrary to most other similar works, the validation of the robot's accuracy is performed with a very large number of measures (1,000) throughout the complete robot's joint space. After calibration, the mean/maximum position errors at any of eight different measurement points on the end-effector (all offset from axis 6 by approximately 120mm) are reduced from 0.968mm/2.158mm respectively, to 0.364mm/0.696mm. Highlights? A practical calibration procedure for industrial robots is presented in detail. ? The procedure is based on the use of a laser tracker and at least 3 measurement points. ? The method is validated on an ABB IRB 1600 robot in 1,000 arbitrary robot configurations. ? The mean/maximum position errors of this robot are reduced to 0.364mm/0.696mm.

RFID-enabled real-time manufacturing execution system for mass-customization production

Abstract: Mass-customization production (MCP) companies must fight with shop-floor uncertainty and complexity caused by wide variety of product components. The research is motivated by a typical MCP company that has experienced inefficient scheduling due to paper-based identification and manual data collection. This paper presents an RFID-enabled real-time manufacturing execution system (RT-MES). RFID devices are deployed systematically on the shop-floor to track and trace manufacturing objects and collect real-time production data. Disturbances are identified and controlled within RT-MES. Planning and scheduling decisions are more practically and precisely made and executed. Online facilities are provided to visualize and manage real-time dynamics of shop-floor WIP (work-in-progress) items. A case study is reported in a collaborating company which manufactures large-scale and heavy-duty machineries. The efficiency and effectiveness of the proposed RT-MES are evaluated with real-life industrial data for shop-floor production management in terms of workers, machines and materials.

Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

Abstract: The traditional production scheduling problem considers performance indicators such as processing time, cost, and quality as optimization objectives in manufacturing systems; however, it does not take energy consumption or environmental impacts completely into account. Therefore, this paper proposes an energy-efficient model for flexible flow shop scheduling (FFS). First, a mathematical model for a FFS problem, which is based on an energy-efficient mechanism, is described to solve multi-objective optimization. Since FFS is well known as a NP-hard problem, an improved, genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption to implement a feasible scheduling. Finally, a case study of a production scheduling problem for a metalworking workshop in a plant is simulated. The experimental results show that the relationship between the makespan and the energy consumption may be apparently conflicting. In addition, an energy-saving decision is performed in a feasible scheduling. Using the decision method, there could be significant potential for minimizing energy consumption.

An interoperable solution for Cloud manufacturing

Abstract: Cloud manufacturing is a new concept extending and adopting the concept of Cloud computing for manufacturing. The aim is to transform manufacturing businesses to a new paradigm in that manufacturing capabilities and resources are componentized, integrated and optimized globally. This study presents an interoperable manufacturing perspective based on Cloud manufacturing. A literature search has been undertaken regarding Cloud architecture and technologies that can assist Cloud manufacturing. Manufacturing resources and capabilities are discussed in terms of Cloud service. A service-oriented, interoperable Cloud manufacturing system is proposed. Service methodologies are developed to support two types of Cloud users, i.e., customer user and enterprise user, along with standardized data models describing Cloud service and relevant features. Two case studies are undertaken to evaluate the proposed system. Cloud technology brings into manufacturing industry with a number of benefits such as openness, cost-efficiency, resource sharing and production scalability.

Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints

Abstract: In this paper a high smooth trajectory planning method is presented to improve the practical performance of tracking control for robot manipulators. The strategy is designed as a combination of the planning with multi-degree splines in Cartesian space and multi-degree B-splines in joint space. Following implementation, under the premise of precisely passing the via-points required, the cubic spline is used in Cartesian space planning to make either the velocities or the accelerations at the initial and ending moments controllable for the end effector. While the septuple B-spline is applied in joint space planning to make the velocities, accelerations and jerks bounded and continuous, with the initial and ending values of them configurable. In the meantime, minimum-time optimization problem is also discussed. Experimental results show that, the proposed approach is an effective solution to trajectory planning, with ensuring a both smooth and efficiency tracking performance with fluent movement for the robot manipulators.

A collaborative and integrated platform to support distributed manufacturing system using a service-oriented approach based on cloud computing paradigm

Abstract: Today's manufacturing enterprises struggle to adopt cost-effective manufacturing systems. Overview of the recent manufacturing enterprises shows that successful global manufacturing enterprises have distributed their manufacturing capabilities over the globe. The successes of global manufacturing enterprises depend upon the entire worldwide integration of their product development processes and manufacturing operations that are distributed over the globe. Distributed manufacturing agents' collaboration and manufacturing data integrity play a major role in global manufacturing enterprises' success. There are number of works, conducted to enable the distributed manufacturing agents to collaborate with each other. To achieve the manufacturing data integrity through manufacturing processes, numbers of solutions have been proposed which one of the successful solutions is to use ISO 10303 (STEP) standard. However, adopting this standard one can recognize antonym effects of integration and collaboration approaches that weaken both integration and collaboration capabilities of manufacturing agents. In our latest work, we had developed an integrated and collaborative manufacturing platform named LAYMOD. Albeit the platform in question was through enough to be applied in various collaborative and integrated CAx systems, its embedded structure hampers its application for collaboration in distributed manufacturing systems. To achieve an integrated and collaborative platform for distributed manufacturing agents, this paper proposes a service-oriented approach. This approach is originated from cloud computing paradigm known as one of the technologies which enables a major transformation in manufacturing industry. Also, to maintain the product data integration based on the STEP standard, a new service-oriented approach is proposed. This approach is in parallel to the new capability of the STEP standard for supporting XML data structures. The result is a new platform named XMLAYMOD. XMLAYMOD is able to support distributed manufacturing collaboration and data integration based on the STEP standard. The different aspects of this platform to fulfill the requirements of distributed collaboration and also to overcome the lacks of the STEP standard are discussed through a brief case study. HighlightsÂ? Today's manufacturing enterprises struggle to adopt cost-effective manufacturing systems. Â? Distributed manufacturing collaboration and data integrity are essential in global manufacturing success. Â? One of the successful solutions for manufacturing data integrity is to use ISO 10303 (STEP) standard. Â? Adopting STEP standard, there are antonym effects of integration and collaboration capabilities. Â? This paper proposes an integrated and collaborative platform called XMLAYMOD for distributed manufacturing agents based on cloud computing paradigm.

Compound fabrication: A multi-functional robotic platform for digital design and fabrication

Abstract: Supporting various applications of digital fabrication and manufacturing, the industrial robot is typically assigned repetitive tasks for specific pre-programmed and singular applications We propose a novel approach for robotic fabrication and manufacturing entitled Compound Fabrication, supporting multi-functional and multi-material processes This approach combines the major manufacturing technologies including additive, formative and subtractive fabrication, as well as their parallel integration A 6-axis robotic arm, repurposed as an integrated 3D printing, milling and sculpting platform, enables shifting between fabrication modes and across scales using different end effectors Promoting an integrated approach to robotic fabrication, novel combination processes are demonstrated including 3D printing and milling fabrication composites In addition, novel robotic fabrication processes are developed and evaluated, such as multi-axis plastic 3D printing, direct recycling 3D printing, and embedded printing The benefits and limitations of the Compound Fabrication approach and its experimental platform are reviewed and discussed Finally, contemplation regarding the future of multi-functional robotic fabrication is offered, in the context of the experiments reviewed and demonstrated in this paper

Modeling of bead section profile and overlapping beads with experimental validation for robotic GMAW-based rapid manufacturing

Abstract: Robotic gas metal arc welding enables the capacity of fabricating fully dense components with low cost in rapid manufacturing During the layer additive manufacturing, the cross-sectional profile of a single weld bead as well as overlapping parameters is critical for improving the surface quality, dimensional accuracy and mechanical performance This paper highlights an experimental study carried out to determine the optimal model of the bead cross-section profile fitted with circular arc, parabola, and cosine function, by comparing the actual area of the bead section with the predicted areas of the three models A necessary condition for the overlapping of adjacent beads is proposed The results show that different models for the single bead section profile result in different center distances and surface qualities of adjacent beads The optimal model for the bead section profile has an important bearing on the ratio of wire feed rate to welding speed

An adaptive process planning approach of rapid prototyping and manufacturing

Abstract: This paper presents an adaptive approach to improve the process planning of Rapid Prototyping/Manufacturing (RP/M) for complex product models such as biomedical models. Non-Uniform Rational B-Spline (NURBS)-based curves were introduced to represent the boundary contours of the sliced layers in RP/M to maintain the geometrical accuracy of the original models. A mixed tool-path generation algorithm was then developed to generate contour tool-paths along the boundary and offset curves of each sliced layer to preserve geometrical accuracy, and zigzag tool-paths for the internal area of the layer to simplify computing processes and speed up fabrication. In addition, based on the developed build time and geometrical accuracy analysis models, adaptive algorithms were designed to generate an adaptive speed of the RP/M nozzle/print head for the contour tool-paths to address the geometrical characteristics of each layer, and to identify the best slope degree of the zigzag tool-paths towards achieving the minimum build time. Five case studies of complex biomedical models were used to verify and demonstrate the improved performance of the approach in terms of processing effectiveness and geometrical accuracy. Highlights? NURBS curves were introduced to maintain the accuracy of slicing layers. ? A mixed tool-path algorithm was developed to balance accuracy and build time. ? Adaptive algorithms were designed to identify the best slope degree of the zigzag tool-paths. ? Case studies of complex biomedical models were used to verify and demonstrate the approach

Critical success factors for ERP implementation in SMEs

Abstract: ERP implementation is regarded as complex, cumbersome and costly, and, very often, it exceeds the initial estimated resources. The process involves a thorough examination of the business processes in the organisation; selection of the best available software solution that matches the requirements of the enterprise; configuration of the selected systems;, training of staff; and customisation of the selected software solutions including development of required interfaces. Finally, the existing MIS of the organisation is replaced totally or partially by the new system. All the implementation processes should be carried out without affecting the daily operations across the whole enterprise. This can only be achieved by having an understanding of the key elements forming the infrastructure of the organisation, an effective plan for the implementation and an effective procedure to measure and evaluate the project throughout the implementation process. This paper presents the results of a study to identify and analyse the interrelationships of the critical issues involved in the implementation of ERP in small and medium sized enterprises (SMEs). Three basic research questions were addressed. First, what are the main critical success factors? Second, how do these factors interact throughout the implementation process? Third, which factors have their highest impact and in what stages? In order to answer these questions, over 50 relevant papers were critically reviewed to identify the main critical success factors (CSFs) for ERP implementation in large organisations. Then, the applicability of the identified CSFs to SMEs was investigated. Next, an industrial survey was also undertaken to identify which CSF has highest impact in what stages. The findings on relationships of the critical success factors have been utilised to develop a tool to monitor, and eventually improve, ERP implementations for SMEs. In the development of the tool, eight people from industry and academia with experience of ERP implementations were interviewed with the aim of validating the model being developed. The overall results provide useful pointers to the interplay of organisational and operational factors for the successful implementation of ERP.

Autonomous weld seam identification and localisation using eye-in-hand stereo vision for robotic arc welding

Abstract: One of the main difficulties in using robotic welding in low to medium volume manufacturing or repair work is the time taken to programme the robot to weld a new part. It is often cheaper and more efficient to weld the parts manually. This paper presents a method for the automatic identification and location of welding seams for robotic welding using computer vision. The use of computer vision in welding faces some difficult challenges such as poor contrast, textureless images, reflections and imperfections on the surface of the steel such as scratches. The methods developed in the paper enables the robust identification of narrow weld seams for ferrous materials combined with reliable image matching and triangulation through the use of 2D homography. The proposed algorithms are validated through experiments using an industrial welding robot in a workshop environment. The results show that this method can provide a 3D Cartesian accuracy of within ±1 mm which is acceptable in most robotic arc welding applications.

Online robot calibration based on vision measurement

Abstract: Robot calibration is a useful diagnostic method to improve positioning accuracy in robot production and maintenance. Unlike traditional calibration methods that require expensive equipment and complex steps, a vision-based online robot calibration method that only requires several reference images is presented in this paper. The method requires a camera that is rigidly attached to the robot end effector (EE), and a calibration board must be settled around the robot where the camera can see it. An efficient automatic approach to detect the corners from the images of the calibration board is proposed. The poses of the robot can be estimated from the detected corners. The kinematic parameters can be conducted automatically based on the known poses of the robot. Unlike in the existing self-calibration methods, the great advantage of this online self-calibration method is that the entire process of robot calibration is automatic and without any manual intervention, enabling the robot calibration to be completed online when the robot is working. Therefore, the proposed approach is particularly suitable for unknown environments, such as deep sea or outer space. In these high-temperature and/or high-pressure environments, the shapes of the robot links are easy to change. Thus, the robot kinematic parameters are changed by allowing the robot to grab objects with different qualities to verify the performance of the online robot calibration. Experimental studies on a GOOGOL GRB3016 robot show that the proposed method has high accuracy, convenience, and high efficiency.

An artificial neural network approach to the problem of wireless sensors network localization

Abstract: One of the imperative problems in the realm of wireless sensor networks is the problem of wireless sensors localization. Despite the fact that much research has been conducted in this area, many of the proposed approaches produce unsatisfactory results when exposed to the harsh, uncertain, noisy conditions of a manufacturing environment. In this study, we develop an artificial neural network approach to moderate the effect of the miscellaneous noise sources and harsh factory conditions on the localization of the wireless sensors. Special attention is given to investigate the effect of blockage and ambient conditions on the accuracy of mobile node localization. A simulator, simulating the noisy and dynamic shop conditions of manufacturing environments, is employed to examine the neural network proposed. The neural network performance is also validated through some actual experiments in real-world environment prone to different sources of noise and signal attenuation. The simulation and experimental results demonstrate the effectiveness and accuracy of the proposed methodology. Highlights? This research addresses the problem of mobile node tracking in wireless sensor networks. ? The significant factors impacting propagation of signals through media are studied. ? Neural based approaches are proposed to reduce the destructive effects of ambient factors. ? The proposed technique is examined through a simulation study and actual physical experiments. ? The results obtained corroborate the superior performance of the neural based technique proposed.

Optimization of process parameters for pulsed laser milling of micro-channels on AISI H13 tool steel

Abstract: This paper focuses on understanding the influence of laser milling process parameters on the final geometrical and surface quality of micro-channel features fabricated on AISI H13 steel. Optimal selection of process parameters is highly critical for successful material removal and high dimensional and surface quality for micro-sized die/mold applications. A set of designed experiments is carried out in a pulsed Nd:YAG laser milling system using AISI H13 hardened tool steel as work material. Arrays of micro-channels have been fabricated using a range of process parameters such as scanning speed (SS), pulse intensity (PI), and pulse frequency (PF). The relation between process parameters and quality characteristics has been studied with experimental modeling. Multi-criteria decision making for material and process parameter selection for desired surface quality and dimensional accuracy is investigated using an evolutionary computation method based on particle swarm optimization (PSO). Highlights? The performance of process parameters in laser milling technology. ? Methodology to select process parameters for laser milling process. ? Particle swarm optimization algorithm to optimize process parameters.

Selective disassembly planning for waste electrical and electronic equipment with case studies on liquid crystaldisplays

Abstract: Waste Electrical and Electronic Equipments (WEEEs) are one of the most significant waste streams in modern societies. In the past decade, disassembly of WEEE to support remanufacturing and recycling has been growingly adopted by industries. With the increasing customisation and diversity of Electrical and Electronic Equipment (EEE) and more complex assembly processes, full disassembly of WEEE is rarely an ideal solution due to high disassembly cost. Selective disassembly, which prioritises operations for partial disassembly according to the legislative and economic considerations of specific stakeholders, is becoming an important but still a challenging research topic in recent years. In order to address the issue effectively, in this paper, a Particle Swarm Optimisation (PSO)-based selective disassembly planning method embedded with customisable decision making models and a novel generic constraint handling algorithm has been developed. With multi-criteria and adaptive decision making models, the developed method is flexible to handle WEEE to meet the various requirements of stakeholders. Based on the generic constraint handling and intelligent optimisation algorithms, the developed research is capable to process complex constraints and achieve optimised selective disassembly plans. Industrial cases on Liquid Crystal Display (LCD) televisions have been used to verify and demonstrate the effectiveness and robustness of the research in different application scenarios.

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

Abstract: 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+/-0.015mm. 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 0.080mm within the entire robot's workspace of 600mmx600mm.

Off-line compensation of the tool path deviations on robotic machining: Application to incremental sheet forming

Abstract: In this paper, a coupling methodology is involved and improved to correct the tool path deviations induced by the compliance of industrial robots during an incremental sheet forming task. For that purpose, a robust and systematic method is first proposed to derive the elastic model of their structure and an efficient FE simulation of the process is then used to predict accurately the forming forces. Their values are then defined as the inputs of the proposed elastic model to calculate the robot TCP pose errors induced by the elastic deformations. This avoids thus a first step of measurement of the forces required to form a test part with a stiff machine. An intensive experimental investigation is performed by forming a classical frustum cone and a non-symmetrical twisted pyramid. It validates the robustness of both the FE analysis and the proposed elastic modeling allowing the final geometry of the formed parts to converge towards their nominal specifications in a context of prototyping applications.

Advanced CNC system with in-process feed-rate optimisation

Abstract: Tight quality requirements and stringent customer demands are the main thrust behind the development of new generation machine tool controllers that are more universal, adaptable and interoperable. The development of some international standards such as STEP and STEP-NC presents a vision for intelligent CNC machining. Implementation of STEP-NC enabled Machine Condition Monitoring (MCM) is presented in this paper. The system allows optimisation during machining in order to shorten machining time and increase product quality. In the system, an optiSTEP-NC, an AECopt controller and a Knowledge-Based Evaluation (KBE) module have been developed. The aim of the optiSTEP-NC system is to perform initial feed-rate optimisation based on STEP-NC data to assist process planners in assigning appropriate machining parameters. AECopt acts as a connector between the process planner and machining environment with the intention to provide adaptive and automatic in-process machining optimisation. KBE based-MTConnect is responsible for obtaining machining know-how. Optimisation is performed before, during or after machining operations, based on the data collected and monitored such as machining vibration, acceleration and jerk, cutting power and feed-rate.

A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects

Abstract: Time estimation in new product development (NPD) projects is often a complex problem due to its nonlinearity and the small quantity of data patterns. Support vector regression (SVR) based on statistical learning theory is introduced as a new neural network technique with maximum generalization ability. The SVR has been utilized to solve nonlinear regression problems successfully. However, the applicability of the SVR is highly affected due to the difficulty of selecting the SVR parameters appropriately. The imperialist competitive algorithm (ICA) as a socio-politically inspired optimization strategy is employed to solve the real world engineering problems. This optimization algorithm is inspired by competition mechanism among imperialists and colonies, in contrast to evolutionary algorithms. This paper presents a new model integrating the SVR and the ICA for time estimation in NPD projects, in which ICA is used to tune the parameters of the SVR. A real data set from a case study of an NPD project in a manufacturing industry is presented to demonstrate the performance of the proposed model. In addition, the comparison is provided between the proposed model and conventional techniques, namely nonlinear regression, back-propagation neural networks (BPNN), pure SVR and general regression neural networks (GRNN). The experimental results indicate that the presented model achieves high estimation accuracy and leads to effective prediction. Highlights? Proposing a new support vector model to capture data patterns of time intervals. ? Employing imperialist competitive algorithm to optimize the parameters of SVR. ? Presenting a real case study in a manufacturing industry in the NPD environment. ? Providing a comparison between the proposed model and conventional techniques.

Development of a decision support system for robot selection

Abstract: 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.

Uncertainty estimation in robot kinematic calibration

Abstract: Currently, the results of a robot calibration procedure are expressed generally in terms of the position and orientation error for a set of locations and orientations, which have been obtained from the previously identified kinematic parameters. In this work, a technique is presented to evaluate the calibration uncertainty for a robot arm calibrated using the circle point analysis method. The method developed is based on the probability distribution propagation calculation recommended by the Guide to the Expression of Uncertainty of Measurement and on the Monte Carlo method. This method makes it possible to calculate the uncertainty in the identification of each single robot parameter, and thus, to estimate the robot positioning uncertainty due to the calibration uncertainty, rather than based on a single set locations and orientations that are previously defined for a unique set of identified parameters. Additionally, this technique allows for the establishment of the best possible conditions for the data capture test, which identifies parameters and determines which of them have the least possible calibration uncertainty. This determination is based on the variables involved in the data capture process by propagating their influence up to the final robot accuracy.

Improving optimization of tool path planning in 5-axis flank milling using advanced PSO algorithms

Abstract: This paper studies optimization of tool path planning in 5-axis flank milling of ruled surfaces using advanced Particle Swarm Optimization (PSO) methods with machining error as an objective. We enlarge the solution space in the optimization by relaxing the constraint imposed by previous studies that the cutter must make contact with the boundary curves. Advanced Particle Swarm Optimization (APSO) and Fully Informed Particle Swarm Optimization (FIPS) algorithms are applied to improve the quality of optimal solutions and search efficiency. Test surfaces are constructed by systematic variations of three surface properties, cutter radius, and the number of cutter locations comprising a tool path. Test results show that FIPS is most effective in reducing the error in all the trials, while PSO performs best when the number of cutter locations is very low. This research improves tool path planning in 5-axis flank milling by producing smaller machining errors compared to past works. It also provides insightful findings in PSO based optimization of the tool path planning.

Point set augmentation through fitting for enhanced ICP registration of point clouds in multisensor coordinate metrology

Abstract: In multisensor coordinate metrology scenarios involving the fusion of homogenous data, specifically 3D point clouds like those originated by CMMs and structured light scanners, the problem of registration, i.e. the proper localization of the clouds in the same coordinate system, is of central importance. For fine registration, known variants of the Iterative Closest Point (ICP) algorithm are commonly adopted; however, no attempt seems to be done to tweak such algorithms to better suit the distinctive multisensor nature of the data. This work investigates an original approach that targets issues which are specific to multisensor coordinate metrology scenarios, such as coexistence of point sets with different densities, different spatial arrangements (e.g. sparse CMM points vs. gridded sets from light scanners), and different noise levels associated to the point sets depending on the metrological performances of the sensors involved. The proposed approach is based on combining known ICP variants with novel point set augmentation techniques, where new points are added to existing sets with the purpose of improving registration performance and robustness to measurement error. In particular, augmentation techniques based on advanced fitting solutions promote a paradigm shift for registration, which is not seen as a geometric problem consisting in moving point sets as close as possible to each other, but as a problem where it is not the original points, but the underlying geometries that must be brought together. In this work, promising combinations of ICP and point augmentation techniques are investigated through the application to virtual scenarios involving synthetic geometries and simulated measurements. Guidelines for approaching registration problems in industrial scenarios involving multisensor data fusion are also provided. Highlights? Point set registration in multisensor coordinate metrology is investigated. ? Iterative Closest Point (ICP) combined with point set augmentation is proposed. ? Point set augmentation improves ICP registration performance. ? Augmentation based on fitting makes ICP more robust to measurement error.

An active hybrid parallel robot for minimally invasive surgery

Abstract: During the last years, there has been an increase in research in the field of medical robots. This trend motivated the development of a new robotics field called ''robotic-assisted minimally invasive surgery''. The paper presents the kinematic and dynamic behavior of a parallel hybrid surgical robot PARASURG-9M. The robot consists of two subsystems: a surgical robotic arm, PARASURG 5M with five motors, and an active robotized surgical instrument PARASIM with four motors. The methodology for the robot kinematics is presented and the algorithm for robot workspace generation is described. PARASURG-9M inverse dynamic simulation is performed using MSC Adams and finally some numerical and simulation results of the developed experimental model with its system control are also described.

Structural design and kinematics of a new parallel reconfigurable robot

Abstract: Reconfigurable robots can be defined as a group of robots that can have different geometries, thus obtaining different structures derived from the basic one, having different degrees of freedom and workspaces. Thanks to the optimum dexterity they offer, the user can accomplish a large variety of industrial tasks, using a structurally optimized robot leading towards better energy control and efficiency especially in case of batch size production lines where the task (for the robot) may vary periodically. Reconfigurable systems are a challenge for numerous scientists, due to the advantage of dealing with changes and uncertainties on the ever-changing manufacturing market. One of the main problems of reconfigurable robots is the proper structural geometry determination, so that the resulting structure is able to perform a variety of tasks. This paper presents the structural design of an innovative parallel robot with six degrees of freedom and its proposed configurations with five, four, three and two degrees of freedom. The kinematic analysis and the workspace representations of all the presented configurations of the parallel robot, called Recrob, are also presented. Highlights? The importance of reconfigurable systems is explained. ? The structural design of a robot with up to six degrees of freedom is described. ? The kinematics of the reconfigurable robot is studied and a simulation is made. ? The reconfigurability of the parallel robot Recrob is explained. ? The workspace of all the configurations of the robot is studied.

Disassembly planning and sequencing for end-of-life products with RFID enriched information

Abstract: When a product reaches its end of lifecycle, components of the product can be reused, recycled, or disposed, depending on their conditions and recovery value. In order to make an optimal disassembly plan to efficiently retrieve the reusable and recyclable items inside a product, knowing the true condition of each component is essential. Practically, the recovery value of a used product is often estimated roughly via visual inspection, and the inaccurate estimates would lead to suboptimal disassembly plans. This paper proposes the use of radio-frequency identification (RFID) technology to support disassembly decisions for end-of-life products. RFID can track pertinent data throughout a product's lifecycle. With the enriched information, a fuzzy-based disassembly planning and sequencing model is proposed to maximize net profit. First, a Bayesian method translates the RFID data into a quality index of the components. Then, a fuzzy logic model, solved by genetic algorithm, synthesizes input variables (i.e., product usage, component usage, and component condition) into a solution of optimal disassembly sequence that maximizes profit considering recovery value and disassembly cost. This paper verifies the merits of using RFID to improve disassembly decisions that help reuse and recycle end-of-life products to reduce environmental impact.

Motion control analysis of a parallel robot assisted minimally invasive surgery/microsurgery system (PRAMiSS)

Abstract: This paper presents motion control architectures for a parallel robot assisted minimally invasive surgery/microsurgery system (PRAMiSS) that enable it to achieve milli/micro-manipulations under the constraint of moving through a fixed penetration point or so-called remote centre-of-motion (RCM) point without any mechanical constraint. Two control structures suitable for minimally invasive surgery operations with submillimeter accuracy and for minimally invasive microsurgery operations with the desired accuracy in micron range are proposed. The control algorithm also applies orientation constraints preventing the tip from orienting around the instrument axis due to the robot movements as well as a minimum displacement constraint to minimise the movements of the parallel micropositioning robot. Experiments were performed and the results are analysed in this paper to verify accuracy and effectiveness of the proposed control algorithm for both cases of minimally invasive surgery and microsurgery operations. The experimental results present good accuracy and performance of the control algorithm. The numerical modelling and graphical simulations were also carried out and the results are also provided that demonstrate the correlation between the experimental results and physical responses.

Compliance error compensation technique for parallel robots composed of non-perfect serial chains

Abstract: The paper presents the compliance errors compensation technique for over-constrained parallel manipulators under external and internal loadings. This technique is based on the non-linear stiffness modeling which is able to take into account the influence of non-perfect geometry of serial chains caused by manufacturing errors. Within the developed technique, the deviation compensation reduces to an adjustment of a target trajectory that is modified in the off-line mode. The advantages and practical significance of the proposed technique are illustrated by an example that deals with groove milling by the Orthoglide manipulator that considers different locations of the workpiece. It is also demonstrated that the impact of the compliance errors and the errors caused by inaccuracy in serial chains cannot be taken into account using the superposition principle.

Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics

Abstract: A novel neural network-based robust finite-time control strategy is proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties, in which the actuator dynamics is fully considered. The controller, which possesses finite-time convergence and strong robustness, consists of two parts, namely a neural network for approximating the nonlinear uncertainty function and a modified variable structure term for eliminating the approximate error and guaranteeing the finite-time convergence. According to the analysis based on the Lyapunov theory and the relative finite-time stability theory, the neural network is asymptotically convergent and the controlled robotic system is finite time stable. The proposed controller is then verified on a two-link robotic manipulator by simulations and experiments, with satisfactory control performance being obtained even in the presence of various uncertainties and external disturbances.

Dynamic modeling, stability and energy consumption analysis of a realistic six-legged walking robot

Abstract: This paper deals with a detailed analysis on kinematics, dynamics, stability and energy consumption of a realistic six-legged robot. The aim of this study is to extend a previous work of Roy et al. [1], in order to estimate optimal feet forces and joint torques of the six-legged robot generating wave-gaits with four different duty factors and deal with its stability issues. Two different approaches are developed to determine optimal feet forces. In the first approach, minimization of the norm of feet forces is carried out using a least square method, whereas minimization of the norm of joint torques is performed in the second approach. The second approach is found to be more energy efficient compared to the first one. The maximum values of feet forces and joint torques are seen to decrease with the increase of duty factor. The effects of walking parameters, namely velocity, stroke and duty factors have been studied on energy consumption and stability of the robot. The variations of average power consumption and specific energy consumption with the velocity and stroke are compared for four different duty factors. Wave gait with a low duty factor is found to be more energy-efficient compared to that with a high duty factor at the highest possible velocity.