Month: March 2018

Multi-objective parameter optimization design of a magnetically actuated intravitreal injection device

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Abstract
Aiming at intravitreal injection procedures for eye diseases, needless injectors are emerging to puncture complications, save time and improve the safety of the process. In this paper, an injection device based on electromagnetic E-core actuation is selected for its better position control and improved controllability over current solenoid designs. The multi-objective optimization model of the E-core device is derived. Then, an integrated NSGA-II and TOPSIS based on combinatorial weighting approach is proposed for the parameter optimization design of the device and the selection of a final compromise solution or optimal solution. The combination weighting method combines the advantages of the objective and subjective weighting method, making the results more reasonable.
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Simultaneous Hand–Eye, Tool–Flange, and Robot–Robot Calibration for Comanipulation by Solving the Problem

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Abstract
Multirobot comanipulation shows great potential in surpassing the limitations of single-robot manipulation in complicated tasks such as robotic surgeries. However, a dynamic multirobot setup in unstructured environments poses great uncertainties in robot configurations. Therefore, the coordination relationships between the end-effectors and other devices, such as cameras (hand–eye calibration) and tools (tool–flange calibration), as well as the relationships among the base frames (robot–robot calibration) have to be determined timely to enable accurate robotic cooperation for the constantly changing configuration of the systems. We formulated the problem of hand–eye, tool–flange, and robot–robot calibration to a matrix equation AXB=YCZ. A series of generic geometric properties and lemmas were presented, leading to the derivation of the final simultaneous algorithm. In addition to the accurate iterative solution, a closed-form solution was also introduced based on quaternions to give an initial value. To show the feasibility and superiority of the simultaneous method, two nonsimultaneous methods were compared through thorough simulations under various robot movements and noise levels. Comprehensive experiments on real robots were also performed to further validate the proposed methods. The comparison results from both simulations and experiments demonstrated the superior accuracy and efficiency of the proposed simultaneous calibration method.
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Self-Triggered Output Feedback Control for Consensus of Multi-Agent Systems

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Abstract
This paper studies the self-triggered control consensus problem of general linear multi-agent systems(MASs). A novel self-triggered control strategy based on output feedback is proposed for centralized and distributed cases, respectively. In consideration of the states of agents are not available, a state observer is adopted. A dynamic observer-based control law is employed to improve the transient response. Under this triggering strategy, both the estimated states of MASs and the states of controller are updated at triggering time. The next triggering time is predetermined at the last triggering instant. Moreover, the asymptotic consensus of MASs can be guaranteed. Finally, the effectiveness of the proposed control strategy is illustrated by a numerical example.
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Low-Cost Pyrometry System With Nonlinear Multisense Partial Least Squares

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Abstract:

Accurate high-temperature measurement is very important for process monitoring of an industrial system. Infrared thermometers usually can handle no more than 1000 °C and should use some expensive accessories for higher temperature measurements. This paper proposes a low-cost pyrometry system with nonlinear multisense partial least squares (NMSPLS). The ordinary camera with different filters is designed to collect the images of hot object at different wavelengths, and the NMSPLS is presented for predicting the temperature of the hot object from the obtained images. For the proposed method, the obtained images are represented by the multisense tensor, where red, green, and blue are regarded as three different dimensions in a sense of the tensor, respectively. The proposed method integrates an outer model and a nonlinear inner model. For the outer model, the independent variables and the dependent variables are projected into a low-dimensional common latent subspace. The weight matrices are calculated from the independent variables by the tucker decomposition, and the single value decomposition is adopted for extracting the latent variables (Lvs) based on the covariance between the independent variables and the dependent variables. For the nonlinear inner model, the neural network is adopted and the extracted Lvs are used as the input and the output of the neural network, respectively. Two real experiments are performed for estimating the proposed method. The experimental results verify that the proposed method can be applied for pyrometry and have higher effectiveness.

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Electromagnetically Enhanced Soft & Flexible Bend Sensor: A Quantitative Analysis with Different Cores

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Abstract

Advantages of soft, flexible materials with developments in refined magnetic actuation can be intertwined for a promising platform to work on a resilient, adaptable manipulator aimed to meet ever-increasing demands in safe regulated medical environments. Taking advantages of these soft magnetic polymers, we propose a novel, soft-squishy and flexible bend sensor by determining the relationship between inductance changes with bending angle. This bend sensor employs flexible wire embedded in a silicone elastomer with the different permeable core. The principle notion is to have a comprehensive analysis of the change in morphology of the sensor with bending angle which can be translated to inductance generated therein. The performance of the sensor is evaluated with various experimental trials while analytical modelling elucidates that the bend angle is linearly proportional to the sensor signal citing R-square value up to 0.9204. The proposed sensor produces the desired output in the EM frequency range of 8 MHz – 10 MHz with a tunable sensitivity of 0.418 mV/rad. The sensor is robust enough to stretch up to twice of its original length. The main advantage of this bend sensor is its simple fabrication technique, flexibility, robustness and economical. Conclusively, this work on induction based tactile bending sensor is proved to produce robust output and can be extrapolated to sense bending angle using induction principle for the rehabilitative device, wearable robots and related biomedical applications requiring low cost, soft and flexible operations.

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