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The purpose of this paper is to investigate the potential, both analytically and practically, of understanding research methods as bridging devices. Methods can bridge theory and empirics, but it is argued that they can perform several bridging functions: between theory and praxis, between analysis and strategy and between past and future. The focus is on those forms of bridging relevant for understanding and effectuating change in governance, at community level and at the scale of organizations.

The paper develops a perspective on methods as bridging devices. It uses the newly minted methods of governance path and context mapping as a case study. These methods conceptually derive from evolutionary governance theory (EGT) and were developed and tested in Canadian empirical research. The case helps to develop insight in features, forms and limitations of methods as bridging devices in governance research and practice. The authors then use the case to further develop the initial concept of bridging more generally, emphasizing the shifting balance between methods as bridging and creating boundaries.

Both the case study and the theoretical analysis underline the necessary imperfection of any method as bridging device. The authors affirm the potential of method to perform different bridging functions at the same time, while revealing clear tradeoffs in each role. Tradeoffs occur with adapted versions of the method producing new strengths and weaknesses in new contexts. In each of the forms of bridging involved neither side can be reduced to the other, so a gap always remains. It is demonstrated that the practice of bridging through method in governance is greatly helped when methods are flexibly deployed in ongoing processes of bricolage, nesting and modification. Governance enables the continuous production of new framing devices and other methods.

The idea of methods as bridging devices is new, and can assist the development of a broader understanding of the various forms and functions of research methods. Moreover, it helps to discern roles of research methods in the functioning of governance. The context of governance helps to recognize the multi-functionality of research methods, and their transformation in a context of pressured decision-making. Moreover, this approach contributes to the understanding of governance as adumbrated by EGT.

The goal of this work is to clarify seven useful DMAIC Analyze phase options for developing process improvement opportunities required for successful projects.

Using a scientific method problem solving structure, IO possibilities are shown to be predicted by rejecting a conceptual testable hypothesis.

Seven analysis paths are identified that enable learners to develop multiple IO discovery strategies and to narrow tool selection options. Four benefit areas for identifying analysis paths are given: improved training, continuous improvement foundation, leadership support and framework clarification.

Any starting list of analysis paths for developing IOs would be incomplete. The diversity of application experiences and tools will add to the current list.

Learners participating in LSS activities are aware of management's expectation that they will develop IOs to justify the LSS investment. Tool-focused training may leave some learners unclear about the multiple possible sources for IOs. Identifying useful analysis paths with associated tools for IO discovery will address any learner's Analyze phase uncertainty and facilitate expanded opportunities.

Any successful LSS project must discover IOs to develop improvement actions. Clarifying IO discovery alternatives will encourage team brainstorming on Analyze phase investigative options. This framework identifying LSS improvement paths will assist practitioners in training and communicating with leadership and learners the range of approaches for developing improvement actions.

The study aims to fabricate large metal components with overhangs built on cylindrical or conical surfaces with a high dimensional precision. It proposes methods to address the problems of generating tool-paths on cylindrical or conical surfaces simply and precisely, and planning the welding process on these developable surfaces.

The paper presents the algorithm of tool-paths planning on conical surfaces using a parametric slicing equation and a spatial mapping method and deduces the algorithm of five-axis transformation by addressing the rotating question of two sequential points. The welding process is investigated with a regression fitting model on a flat surface, and experimented on a conical surface, which can be flattened onto a flat surface.

The paper provides slicing and path-mapping expressions for cylindrical and conical surfaces and a curvature-speed-width (CSW) model for wire and arc additive manufacturing to improve the surface appearances. The path-planning method and CSW model can be applied in the five-axis fabrication of the prototype of an underwater thruster. The CSW model has a confidence coefficient of 98.02% and root mean squared error of 0.2777 mm. The reverse measuring of the finished blades shows the residual deformation: an average positive deformation of about 0.5546 mm on one side of the blades and an average negative deformation of about −0.4718 mm on the other side.

Because of the chosen research approach, the research results may lack generalizability for the fabrication based on arbitrary surfaces.

This paper presented an integrated slicing, tool-path planning and welding process planning method for five-axis wire and arc additive manufacturing.

This study aims to modify a nature-based numerical method named the invasive weed optimization (IWO) method for mobile robot path planning in various complex environments.

The existing IWO method is quick in converging to a feasible solution but in a complex environment; it takes more time as well as computational resources. So, in this paper, the computational part of this artificial intelligence technique is modified with the help of recently developed evolution algorithms like particle swarm optimization, genetic algorithm, etc. Some conditional logic statements were used while doing sensor-based mapping for exploring complex paths. Implementation of sensor-based exploration, mathematical IWO method and prioritizing them for better efficiency made this modified IWO method take complex dynamic decisions.

The proposed modified IWO is better for dynamic obstacle avoidance and navigating a long complex map. The deviation of results in simulation and experiments is less than 5.5%, which validates a good agreement between simulation and real-time testing platforms.

As per a deep literature review, it has found that the proposed approach has not been implemented on the Khepera-III robot for smooth motion planning. Here a dynamic obstacle mapping feature is implemented. A method to selectively distribute seeds instead of a random normal distribution is also implemented in this work. The modified version of IWO is coded in MATLAB and simulated through V-Rep simulation software. The integration of sensors was done through logical conditioning. The simulation results are validated using real-time experiments.

Unmanned aerial/ground vehicles (UAV/UGV) collaboration systems are increasingly being used to perform reconnaissance and rescue missions autonomously, especially in disaster areas. The paper aims to discuss this issue.

To improve visibility, this study proposes a path-planning algorithm based on map matching. Continuous ground images are first collected aerially using the UAV vision system. Subsequently, a global map of the ground environment is created by processing the collected images using the methods of image correction, image mosaic and obstacle recognition. The local map of the ground environment is obtained using the 2D laser radar sensor of the UGV. A set of features for both global and local maps is established. Unknown values during map matching are determined via the least squares method. Based on the matched mapping, the traditional A* algorithm is used for the planning of global path in the global map, and the dynamic window method is used for adjustment of the local map.

Simulation experiments were carried out to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm can construct a global map of the wide environment and effectively bypass the obstacles missed by the UAV.

Prior to map matching, there is a need to extract the edge of obstacles in the global map.

This paper proposed a path planning algorithm based on map matching, yielding insights into the application of the UAV/UGV collaboration systems in disaster areas.

VirtuNav is a haptic-, audio-enabled virtual reality simulator that facilitates persons with visual impairment to explore a 3D computer model of a real-life indoor location, such as a room or building. The purpose of this paper is to aid in pre-planning and spatial awareness, for a user to become more familiar with the environment prior to experiencing it in reality.

The system offers two unique interfaces: a free-roam interface where the user can navigate, and an edit mode where the administrator can manage test users, maps and retrieve test data.

System testing reveals that spatial awareness and memory mapping improve with user iterations within VirtuNav.

VirtuNav is a research tool for investigation of user familiarity developed after repeated exposure to the simulator, to determine the extent to which haptic and/or sound cues improve a visually impaired user’s ability to navigate a room or building with or without occlusion.

The application may prove useful for greater real world engagement: to build confidence in real world experiences, enabling persons with sight impairment to more comfortably and readily explore and interact with environments formerly unfamiliar or unattainable to them.

VirtuNav is developed as a practical application offering several unique features including map design, semi-automatic 3D map reconstruction and object classification from 2D map data. Visual and haptic rendering of real-time 3D map navigation are provided as well as automated administrative functions for shortest path determination, actual path comparison, and performance indicator assessment: exploration time taken and collision data.

This paper seeks to adopt FRBRoo as an ontological approach to integrate heterogeneous metadata, and transform human-understandable format into machine-understandable format for semantic query.

Two cases of use with museum artefacts and literary works were exploited to illustrate how FRBRoo can be used to re-contextualize the semantics of elements and the semantic relationships embedded in those elements. The shared ontology was then RDFized and examples were explored to examine the feasibility of the proposed approach.

FRBRoo can play a role as inter lingua aligning museum and library metadata to achieve heterogeneous metadata integration and semantic query without changing either of the original approaches to fit the other.

Exploration of more diverse use cases is required to further align the different approaches of museums and libraries using FRBRoo and make revisions.

Solid evidence is provided for the use of FRBRoo in heterogeneous metadata integration and semantic query.

This is the first study to elaborate how FRBRoo can play a role as a shared ontology to integrate the heterogeneous metadata generated by museums and libraries. This paper also shows how the proposed approach is distinct from the Dublin Core format crosswalk in re-contextualizing semantic meanings and their relationships, and further provides four new sub-types for mapping description language.

Accurate perception of the environment using range sensors such as laser scanner, SONAR, infrared, vision, etc., for the application, such as path planning, localization, autonomous navigation, simultaneously localization and mapping, is a highly challenging area. The reliability of the perception by range sensors relies on the sensor accuracy, precision, sensor model, sensor registration, resolution, etc. Laser scanner is even though accurate and precise but still the efficient and consistent mapping of the environment is yet to be attained because laser scanner gives error as the extrinsic and intrinsic parameters varied which cause specular reflection, refraction, absorption, etc., of the laser beam. The paper aims to discuss this issue.

This paper presents an error analysis in sensory information of laser scanner due to the effect of varying the scanning angle with respect to the optical axis and surface reflectivity or refractive index of the targets. Uncertainties caused by these parameters are reduced by proposing a new technique, tilt mounting system (TMS) with designed filters of tilting the angular position of a laser scanner with the best possible selection of range and scanning angle for the robust occupancy grid mapping. Various experiments are performed in different indoor environments, and the results are validated after the implementation of the TMS approach with designed filters.

After the implementation of the proposed TMS approach with filters, the errors in the laser grid map are reduced by 15.6 percent, which results in 62.5 percent reduction in the collision of a mobile robot during autonomous navigation in the laser grid map.

The TMS approach with designed filter reduces the effect of variation in intrinsic and extrinsic parameters to generate efficient laser occupancy grid map to achieve collision-free autonomous navigation.

Modern service robots are designed to work in a complex indoor environment, in which the robot has to interact with the objects in different ambient light intensities (day light, tube light, halogen light and dark ambiance). The variations in sudden ambient light intensities often cause an error in the sensory information of optical sensors like laser scanner, which reduce the reliability of the sensor in applications such as mapping, path planning and object detection of a mobile robot. Laser scanner is an optical sensor, so sensory information depends upon parameters like surface reflectivity, ambient light condition, texture of the targets, etc. The purposes of this research are to investigate and remove the effect of variation in ambient light conditions on the laser scanner to achieve robust autonomous mobile robot navigation.

The objective of this study is to analyze the effect of ambient light condition (dark ambiance, tube light and halogen bulb) on the accuracy of the laser scanner for the robust autonomous navigation of mobile robot in diverse illumination environments. A proposed AIFA (Adaptive Intensity Filter Algorithm) approach is designed in robot operating system (ROS) and implemented on a mobile robot fitted with laser scanner to reduce the effect of high-intensity ambiance illumination of the environment.

It has been experimentally found that the variation in the measured distance in dark is more consistent and accurate as compared to the sensory information taken in high-intensity tube light/halogen bulbs and in sunlight. The proposed AIFA approach is implement on a laser scanner fitted on a mobile robot which navigates in the high-intensity ambiance-illuminating complex environment. During autonomous navigation of mobile robot, while implementing the AIFA filter, the proportion of cession with the obstacles is reduce to 23 per cent lesser as compared to conventional approaches.

The proposed AIFA approach reduced the effect of the varying ambient light conditions in the sensory information of laser scanner for the applications such as autonomous navigation, path planning, mapping, etc. in diverse ambiance environment.

This paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission requires the rover to automatically localize itself with high accuracy.

A three-dimensional (3D) feature detection method is first proposed to extract salient features from the observed point cloud, after that, the salient features are employed as the candidate destinations for re-visiting under SLAM structure, followed by a path planning algorithm integrated with SLAM, wherein the path length and map utility are leveraged to reduce the growth rate of state estimation uncertainty.

The proposed approach is able to extract distinguishable 3D landmarks for feature re-visiting, and can be naturally integrated with any SLAM algorithms in an efficient manner to improve the navigation accuracy.

This paper proposes a novel active-SLAM structure for planetary rover exploration mission, the salient feature extraction method and active revisit patch planning method are validated to improve the accuracy of pose estimation.