The purpose of this thesis is to investigate the possibilities of automating a gluing process in the optical manufacturing industry. Aimpoint, a Swedish company that manufactures red-dot sights was the industrial partner in this thesis. Since the optical quality of their products is of the highest importance and the lenses and protective glass are for the most part glued using traditional, manual methods, the company aims to find improvements and methods to ensure the qualityof their products by automating this process. A number of requests were submitted, including the ability of such an improved process to be able to glue lenses of more complex geometries than the mostly round lenses currently used. To allow precise adhesive dispensing around the lens of a sight with a complex geometry, Aimpoint has two Cartesian tabletop dispensing robots. This kind of robot servesas a baseline for the comparisons in this thesis. A Franka Emika articulating robot arm was used to test the possibilities of au-tomating the process using such a robot system. Firstly, a motion was generatedbased on the CAD model of the product. The motion generation and manipulation was performed in MATLAB where the Peter Corke robotics toolbox was used for initial simulations. Unfortunately, the robot model in the toolbox was not able to take joint limitations into account and a migration to ROS was in order. Since there was no implementation for Cartesian control in the simulation software for Franka Emika, a decision was made to start physical tests using the robot pre-maturely. As for the adhesive dispensing, tests were conducted to investigate the parameters that affect the termination of a glue joint such as the retract setup and timings. A repeatability test was also conducted to test the performance of dis-pensing using a direct pressure fed adhesives syringe versus using a dispensingvalve. With the knowledge and data from these tests, the experimental setup was built at the robot lab in Lund where a computer ran the controller implementation, controlling the robot and an Arduino was utilized to actuate the dispenser. Several details were glued on this setup, both with vertical and angled nozzle. Asa reference for gluing, a detail was glued using the Cartesian dispensing robotat Aimpoint with the same limited path as previously described. To analyse the performance of the glued joint, a push-out test was conducted at Aimpoint usinga tensile strength machine. The results from this test showed that a glue joint glued with an angled nozzle was stronger and a higher force was required to deform the joint compared to one glued with a vertical nozzle. On average, the joint glued with an angled nozzle required 721 N and the vertical required 395 N. It is concluded that keeping the nozzle angled during the dispensing operation yield a stronger glue joint and makes the system less sensitive to positionand orientation errors. As for the impedance controller used in this thesis, it is concluded to not be suitable for the considered application and its requirements. Regarding the robotic solution investigated in this thesis, it was observed to bebeneficial to utilize a high number of axes when generating the motion for the gluing operation. However, case-consistent inverse kinematics is a requirementto further investigate this claim.

Technology today has progressed to the point that the true potential of robotics is beginning to be realized. However, programming robots to be robust across varied environments and objectives, in a way that is accessible and intuitive to most users, is still a difficult task. There remain a number of unmet needs. For example, many existing solutions today are proprietary, which makes widespread adoption of a single solution difficult to achieve. Also, most approaches are highly targeted to a specific implementation. But it is not clear that these approaches will generalize to a wider range of problems and applications. To address these issues, we define the Interaction Space, or the space created by the interaction between robots and humans. This space is used to classify relevant existing work, and to conceptualize these unmet needs. GTax, a knowledge transfer framework, is presented as a solution that is able to span the Interaction Space. The framework is based on SysML, a standard used in many different systems, which provides a formalized representation and verification. Through this work, we demonstrate that by generalizing across the Interaction Space, we can simplify robot programming and enable knowledge transfer between processes, systems and application domains.

<p dir="ltr">The research presents the structure and a prototype an innovative parallel robotic structure using 3 mobile bases for actuation and hybrid motion. A system identification was performed to verify the model of the robot.</p>

<p>Robot grasping is emerging as an active area of research in robotics as the interest in human-robot interaction is gaining worldwide because of diverse industrial settings for sharing tasks and workplaces. It mainly focuses on the quality of generated grasps for object manipulation. However, despite advancements, these methods need to consider the human-robot collaboration settings where robots and humans will have to grasp the same objects concurrently. Therefore, generating robot grasps compatible with human preferences of simultaneously holding an object is necessary to ensure a safe and natural collaboration experience. In this work, we propose a novel, deep neural network-based method called CoGrasp that generates human-aware robot grasps by contextualizing human preference models of object grasping into the robot grasp selection process. We validate our approach against existing state-of-the-art robot grasping methods through simulated and real-robot experiments and user studies. In real robot experiments, our method achieves about 88% success rate in producing stable grasps that allow humans to interact and grasp objects simultaneously in a socially compliant manner. Furthermore, our user study with 10 independent participants indicated our approach enables a safe, natural, and socially aware human-robot objects' co-grasping experience compared to a standard robot grasping technique.</p> <p>To facilitate the grasping process, we also introduce a vision-language model that works as a pre-processing system before the grasping action takes place. In most settings, the robots are equipped with sensors that allow them to capture the scene, on which the vision model is used to do a detection task and objectify the visible contents in the environment. The language model is used to program the robot to make it possible for them to understand and execute the required sequence of tasks. Using the process of object detection, we build a set of object queries from the sensor image and allow the user to provide an input query for a task to be performed. We then perform a similarity score among these queries to localize the object that needs attention, and once identified, we can use a grasping process for the task at hand.</p>

<div>Polymer nanocomposites (PNC) have an excellent potential for in-situ strain sensing applications in static and dynamic loading scenarios. These PNCs have a polymer matrix of polyvinylidene fluoride (PVDF) with a conductive filler of multi-walled carbon nanotubes (MWCNT) and have both piezoelectric and piezoresistive characteristics. Generally, this composite would accurately measure either low-frequency dynamic strain using piezoresistive characteristic or high-frequency dynamic strains using piezoelectric characteristics of the MWCNT/PVDF film sensor. Thus, the frequency bands of the strain sensor are limited to either piezoresistive or piezoelectric ranges. In this study, a novel weighted fusion technique, called Piezoresistive/Piezoelectric Fusion (PPF), is proposed to combine both piezoresistive and piezoelectric characteristics to capture the wide frequency bands of strain measurements in real-time. This fuzzy logic (FL)-based method combines the salient features (i.e., piezoresistive and piezoelectric) of the nanocomposite sensor via reasonably accurate models to extend the frequency range over a wider band. The FL determines the weight of each signal based on the error between the estimated measurements and the actual measurements. These weights indicate the contribution of each signal to the final fused measurement. The Fuzzy Inference System (FIS) was developed using both optimization and data clustering techniques. In addition, a type-2 FIS was utilized to overcome the model’s uncertainty limitations. The developed PPF methods were verified with experimental data at different dynamic frequencies that were obtained from existing literature. The fused measurements of the MWCNT/PVDF were found to correlate very well with the actual strain, and a high degree of accuracy was achieved by the subtractive clustering PPF’s FISs algorithm. <br></div><div><br></div><div>3D force sensors have proven their effectiveness and relevance for robotics applications. They have also been used in medical and physical therapy applications such as surgical robots and Instrument Assisted Soft Tissue Manipulation (IASTM). The 3D force sensors have been utilized in robot-assisted surgeries and modern physical therapy devices to monitor the 3D forces for improved performances. The 3D force sensor performance and specifications depend on different design parameters, such as the structural configuration, placement of the sensing elements, and load criterion. In this work, different bioinspired structure configurations have been investigated and analyzed to obtain the optimal 3D force sensor configuration in terms of structural integrity, compactness, the safety factor, and strain sensitivity. A Finite Element Analysis (FEA) simulation was used for the analysis to minimize the time of the development cycle.</div><div><br></div><div><br></div><div>A tree branch design was used as the 3D force sensor’s elastic structure. The structure was made of aluminum with a laser-cutting fabrication process. The PVDF/MWCNT films contained piezoresistive and piezoelectric characteristics that allowed for static/low strain measurements and dynamic strain measurements, respectively. Two compositions with 0.1 wt.% and 2 wt.% PVDF/MWCNT sensing elements were selected for piezoelectric and piezoresistive strain measurements, respectively. These characteristic measurements were investigated under different vibration rates in a supported beam experiment. The 3D force sensor was tested under dynamic excitation in the Z-direction and the X-direction. A Direct Piezoresistive/Piezoelectric Fusion (DPPF) method was developed by fusing the piezoresistive and piezoelectric measurements at a given frequency that overcomes the limited frequency ranges of each of the strain sensor characteristics. The DPPF method is based on a fuzzy inference system (FIS) which is constructed and tuned using the subtractive clustering technique. Different nonlinear Hammerstein-Wiener (nlhw) models were used to estimate the actual strain from piezoresistive and piezoelectric measurements at the 3D force sensor. The DPPF method was tested and validated for different strain signal types using presumed Triangle and Square signal waves data. The DPPF has proven its effectiveness in fusing piezoresistive and piezoelectric measurements with different types of signals. In addition, an Extended Direct Piezoresistive/Piezoelectric Fusion (EPPF) is introduced to enhance the DPPF method and perform the fusion in a range of frequencies instead of a particular one. The DPPF and EPPF methods were implemented on the 3D force sensor data, and the developed fusion algorithms were tested on the proposed 3D force sensor experimental data. The simulation results show that the proposed fusion methods have been effective in achieving lower Root Mean Square Error (RMSE) than those obtained from the tuned nlhw models at different operating frequencies.</div>

<p>The miniaturization and thinning of wearable, soft robotics and medical devices are soon to require higher performance modeling as the physical flexibility causes direct impacts on the electrical characteristics of the circuit – changing its behavior. As a representative flexible electronic component, the organic field effect transistor (OFET) has attracted much attention in its manufacturing as well as applications. However, as the strain and stress effects are integrated into multiphysics modelers with deeper interactions, the computational complexity and accuracy of OFET modeling is resurfacing as a limiting bottleneck.</p><p>The dissertation was organized into three interrelated studies. In the first study, the Mass-Spring-Damper (MSD) model for an inverted staggered thin film transistor (TFT) was proposed to investigate the TFT’s internal stress/strain fields, and the strain effects on the overall characteristics of the TFT. A comparison study with the finite element analysis (FEA) model shows that the MSD model can reduce memory usage and raises the computational convergence speed for rendering the same results as the FEA. The second study developed the generalized solid-state model by incorporating the density of trap states in the band structure of organic semiconductors (OSCs). The introduction of trap states allows the generalized solid-state model to describe the electrical characteristics of both inorganic TFTs and organic field-effect transistors (OFETs). It is revealed through experimental verification that the generalized solid-state model can accurately characterize the bending induced electrical properties of an OFET in the linear and saturation regimes. The third study aims to model the transient and steady-state dynamics of an arbitrary organic semiconductor device under mechanical strain. In this study, the fractional drift-diffusion (Fr-DD) model and its computational scheme with high accuracy and high convergence rate were proposed. Based on simulation and experimental validation, the transconductance and output characteristics of a bendable OFET were found to be well determined by the Fr-DD model not only in the linear and saturation regimes, but also in the subthreshold regime.</p>

<p>Stringent regulatory requirements and modern diesel engine technologies have engaged automotive manufacturers and researchers in accurately predicting and controlling diesel engine-out emissions. As a result, engine control systems have become more complex and opaquer, increasing the development time and costs. To address this challenge, Model-based control methods are an effective way to deal with the criticality of the system study and controls. And physics-based combustion engine modeling is a key to achieve it. This thesis focuses on development and validation of a physics-based model for both engine and emissions using model-based design tools from MATLAB & Simulink. Engine model equipped with exhaust gas circulation and variable geometry turbine is adopted from the previously done work which was then integrated with the combustion and emission model that predicts the heat release rates and NO_xemission from engine. Combustion model is designed based on the mass fraction burnt from CA10 to CA90 and then NO_xpredicted using the extended Zeldovich mechanism. The engine models are tuned for both steady state and dynamics test points to account for engine operating range from the performance data. Various engine and combustion parameters are estimated using parameter estimation toolbox from MATLAB and Simulink by applying least squared solver to minimize the error between measured and estimated variables. This model is validated against the virtual engine model developed in GT-power for Cummins 6.7L turbo diesel engine. To account the harmonization of the testing cycles to save engine development time globally, a world harmonized stationary cycle (WHSC) is used for the validation. Sub-systems are validated individually as well as in loop with a complete model for WHSC. Engine model validation showed promising accuracy of more than 88.4 percent in average for the desired parameters required for the NO_xprediction. NO_x estimation is accurate for the cycle except warm up and cool down phase. However, NO_xprediction during these phases is limited due to actual NO_xmeasured data for tuning the model for real time NO_xestimation. Results are summarized at the end to compare the trend of NO_xestimation from the developed combustion and emission model to show the accuracy of in-cylinder parameters and required for the NO_x estimation. </p>

<p>The purpose of this research experiment was to explore and implement a cost-effective automation solution into a low volume production line for loading parts onto a coordinate measuring machine (CMM) for dimensional inspection. Quality control practices have historically been separated from production process by inspection routines being performed in a controlled lab. The system demonstrated the possibilities of an in-process automation of the quality control process that was feasible to be implemented for small and mid-sized manufacturing companies. The process involved an APSX horizontal injection mold machine dispensing parts onto the conveyor belt. The conveyor belt was controlled by a Phoenix Contact PLC and two line sensors that provided two stopping point for cooldown before inspection. A MyCobot 320-M5 robotic arm was used to select the part off the line and places it into a fixture on a Hexagon coordinate measuring machine (CMM).</p>