Design of a Mechatronic Interface with Compliant Manipulator for Robot Assisted Echocardiography

Abstract A compliant manipulator with a compound soft actuator is proposed for robot-assisted echocardiography. The target application is devoted to the TOE echo (Trans-oesophageal echocardiography), which is conventionally performed by medical practitioners. The manual manipulation of the echocardiography probe shows significant risks such as human errors, exposure to ionizing radiation, and multitasking complexity. Automation of TOE provides advantages in terms of control, safety, and workload of the operator. This paper proposes a teleoperated robotic system assisting the physician to perform TOE, to be used in cardiac catheterization laboratories as well as hybrid operation theatres. A system containing a holder with master-slave Dynamixel servos and a manipulator with soft actuators has been developed. To alleviate the major lack of the previous designs in conducting the insertion tube, a robotic arm with a soft structure is proposed that has not hazards of conventional robot manipulators. The fundamental equations and relations for quasi-static control of the system are developed in this paper

Analysis on the performance of a second-order and a third-order RLC circuit of PRBS generator

A pseudo-binary random signal (PRBS) has been widely utilized for system identification in complex signals to develop an experimental approach. PRBS generator is a circuit that generates pseudo-random numbers. This work aims to analyze the best fit value of the PRBS generator with second-order and third-order under-damped black-box RLC circuit of the estimated model. The procedures conducting here can be divided into three parts. First, to design two black boxes using the RLC circuit representing a critically under-damped second-order and third-order system. PRBS generated with maximum-length sequence (MLS) equals 127 bits by using seven shift registers. Second, simulate the PRBS generator using MATLAB software and validate the estimated model from the simulation using the System Identification Tool in MATLAB. Next, connecting hardware RLC circuit and reading input and output signals using an oscilloscope. Finally, 2500 samples of captured data were used for estimation. Then, analyze and compare the best fit of the simulation and experiment with second-order and third-order under-damped black-box RLC circuit. Furthermore, analyze and compare best fit using different sample time. The results showed that the best fit of the second-order model with under-damped black-box RLC circuit was autoregressive with the exogenous term (ARX) 211, where the best fit of the simulation was 99.88%, and the best fit of the experiment was 96.04%. And the results showed that the best fit of the third-order model with an under-damped black-box RLC circuit was ARX 331, where the best fit of the simulation was 99%, and the best fit of the experiment was 94.28%. It was concluded that the best fit value of the second-order was better than the third order. What’s more, the results showed that when the select range is the same, the bigger the sample time, the better the best fit

A review article: investigations on soft materials for soft robot manipulations

In recent years, exploratory research on soft materials and their mechanism has been gaining in popularity. The investigations on soft materials are mostly done for two reasons: (a) to develop an anthropomorphic/prosthetic hand or soft hand with human skin-like material to perform soft manipulations and (b) to develop soft actuators. This paper presented a comprehensive investigation into researches on soft materials for robotic applications. The primary interest of using soft materials is not to leave any marks or damage to objects during the manipulation. The other advantage would be stable grasping due to an area contact. Natural rubber, synthetic rubber, elastomer, polymer composite and nanoparticulated polymer composite are some existing soft materials. Extensive research is required to prepare a highstrength but lighter soft material for robotic soft manipulation. Human skin and its mechanical properties are initially discussed. In addition, the need of soft material for soft manipulations and observations from previous researches over the past few decades, modelling of non-linear hyperelastic/ viscoelastic materials and characterization are discussed. Finally, various soft materials including the polymer-matrix composites, available fillers and their advantages, processing methods and nanoparticulated polymer matrix and its significance in robotic application are presented

Hybrid extendable linear actuators: design and applications

There are many types of different actuators in the field of robotics. For applications where a linear displacement is required, a linear extendable actuator would be the method of choice. The hybrid class of extendable actuators possesses unique features making it suitable for many applications where the soft and rigid types fall short. However, there has not been a clear designation and classification of extendable linear hybrid actuators in the literature. This paper addresses this matter and provides the first overview of the hybrid class of extendable actuators. The paper performs a categorization and characterization of this class of extendable linear actuators based on their method of operation as well as the inherent unique features separating them from the rest. The paper contains five sections, and three sub-sections pertaining to the different categories of Hybrid actuators, and their applications. New research in this field continues to add features to this class of actuators through improvements and added capabilities

System Identification and Model Predictive Control using CVXGEN for Electro-Hydraulic Actuator

Hydraulics have been widely used in heavy industries for decades. The demand for intelligent hydraulic control system has been increasing as tough robotic researches are getting more popular. Despite the high power to weight ratio delivery, the hydraulic actuator suffers from nonlinearity properties that cause difficulties in applying precise position control.  In this paper we proposed Model Predictive Control (MPC) to control an Electro-Hydraulic Actuator (EHA) where its dynamic characteristics is obtained through system identification method.  Control signal generation optimisation and constraint handling are seldom included in the conventional control system design process. Therefore we introduce CVXGEN, a Code Generator for Embedded Convex Optimization that utilises the Quadratic Programming (QP) interior-point solver for MPC optimisation problem. Predictive Functional Control (PFC) is used to validate the CVXGEN-MPC and both algorithms are implemented in simulation and experiment of EHA position control to highlight the optimisation and constraint handling problem. Control performance, control effort, constraint handling and disturbance handling of both methods are discussed

Experimental Evaluation of A Cylinder Actuator Control Using McKibben Muscle

There has been an increased interest in applying pneumatic muscle actuator (PMA) in robotic systems because of its low weight and high compliant characteristics. On the other hand, pneumatic muscle actuator (PMA) is gaining attention in robotic applications because of its low weight and high compliant characteristics. It is known that the McKibben muscle is different from the fluidic cylinder actuator in that the cylinder was unstable in its position and in its velocity in an open-loop system unlike the McKibben that is stable in its position. The modeling and control of McKibben muscle as the actuator for the cylinder are crucial because it is known to have non-linear response, hysteresis and small stroke. In this project, a single acting cylinder model which would have uncontrolled extension to push direction by compressed air, is actuated and controlled using a PMA. The system is designed with two 1.3mm-diameter McKibben muscles attached to the cylinder. Open loop control was used and the result shows that the PMA is able to control the cylinder with good performance

Data analytics for traffic flow prediction in custom using Long Short Term Memory (LSTM) networks

This paper proposes data analysis for traffic flow prediction of customs to help the officer in Customs, Immigration, and Quarantine (CIQ) Complex to understand more about the traffic situation in CIQ. Currently in CIQ, the traffic behaviour for car is unpredictable; sometimes the traffic is very heavy while there are times where all the lanes are cleared. There is a plan to have installation of cameras for smart traffic management system in the future. Therefore, this research aims to have prediction of traffic flow based on time and visualize the trend of traffic data for the officer. The data consist of traffic flow and the respective timestamp. To analyse it with time-series data, Long Short-Term Memory (LSTM) Recurrent Network is used as deep learning approach for prediction. The data pre-processing and training of model would be done using Python. To organize the data, Tableau Prep Builder is used and integrate with Python to publish the data to Tableau Server for storage. An interactive dashboard would be designed on Tableau and made available online for the usage of the officer

Non-parametric identification techniques for intelligent pneumatic actuator

The aim of this paper is to present experimental, empirical and analytic identification techniques, known as non-parametric techniques. Poor dynamics and high nonlinearities are parts of the difficulties in the control of pneumatic actuator functions, which make the identification technique very challenging. Firstly, the step response experimental data is collected to obtain real-time force model of the intelligent pneumatic actuator (IPA). The IPA plant and Personal Computer (PC) communicate through Data Acquisition (DAQ) card over MATLAB software. The second method is approximating the process by curve reaction of a first-order plus delay process, and the third method uses the equivalent n order process with PTn model parameters. The obtained results have been compared with the previous study, achieved based on force system identification of IPA obtained by the (Auto-Regressive model with eXogenous) ARX model. The models developed using non-parameters identification techniques have good responses and their responses are close to the model identified using the ARX system identification model. The controller approved the success of the identification technique with good performance. This means the Non-Parametric techniques are strongly recommended, suitable, and feasible to use to analyze and design the force controller of IPA system. The techniques are thus very suitable to identify the real IPA plant and achieve widespread industrial acceptance

Modeling and fuzzy FOPID controller tuned by PSO for pneumatic positioning system

A pneumatic cylinder system is believed to be extremely nonlinear and sensitive to nonlinearities, which makes it challenging to establish precise position control of the actuator. The current research is aimed at reducing the overshoot in the response of a double-acting pneumatic actuator, namely, the IPA positioning system’s reaction time. The pneumatic system was modeled using an autoregressive with exogenous input (ARX) model structure, and the control strategy was implemented using a fuzzy fractional order proportional integral derivative (fuzzy FOPID) employing the particle swarm optimization (PSO) algorithm. This approach was used to determine the optimal controller parameters. A comparison study has been conducted to prove the advantages of utilizing a PSO fuzzy FOPID controller over PSO fuzzy PID. The controller tuning algorithm was validated and tested using a pneumatic actuator system in both simulation and real environments. From the standpoint of time-domain performance metrics, such as rising time (tr), settling time (ts), and overshoot (OS%), the PSO fuzzy FOPID controller outperforms the PSO Fuzzy PID controller in terms of dynamic performance