Forthcoming and Online First Articles

International Journal of Mechatronics and Automation

International Journal of Mechatronics and Automation (IJMA)

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International Journal of Mechatronics and Automation (8 papers in press)

Regular Issues

  • A real-time embedded drive-by-wire control module for self-driving cars with ROS2   Order a copy of this article
    by Hang Cui, Jiaming Zhang, William R. Norris 
    Abstract: The objective for attaining safety and reliability for an autonomous driving vehicle has provided numerous challenges for researchers. The widely used robot operating system (ROS) is centralised and wastes computing resources due to innate features of the architecture. This paper proposes a safety-critical Simulink compatible real-time operating system (RTOS)-based high-low level controller framework with real-time online parameter tuning for self-driving cars. In addition, a method for leveraging the new features of robot operating system 2 (ROS2), an enhanced safe and reliable self-driving platform, was designed and built. Two geometric path trackers, namely the pure pursuit controller and the Stanley controller were implemented on this high-low level controller framework. Both controllers were evaluated using both indoor and outdoor experiments with reliable performance. This is the first application of ROS2 on a small-scale Ackermann steering platform, and in the use of Simulink modelling for rapid control system prototyping.
    Keywords: safety critical; real-time drive-by-wire; self-driving; robot operating system 2; ROS2.
    DOI: 10.1504/IJMA.2021.10043905
  • Tableware tidying-up robot for self-service restaurant - robot system design   Order a copy of this article
    by Deheng Zhu, Hiroaki Seki, Tokuo Tsuji, Tatsuhiro Hiramitsu 
    Abstract: This study developed an automated tableware tidying-up robot mechanism to tidy up the tableware in a self-service restaurant with a large number of tableware. This study focused on the process of sorting and collecting tableware that were placed on trays by the guest and flowed to robot with belts conveyor. The separated tableware was placed in a washing machine. Garbage and leftover food were also treated with this robot mechanism. The parallel arm and robot hand mechanism were designed to realise the advantages of low cost, high processing speed, and space saving. It was proved that the prototype robot can move and grab tableware smoothly with the high-speed position control. The finger and hand rotation mechanism can throw away leftover food by the leftover food throwing experiment. Additionally, the system can sort the tableware by multiple robots was verified using the tableware sorting experiment.
    Keywords: parallel arm; leftover food treatment; tableware; tidying-up robot; air drive; self-service restaurant; robot design; air cylinder; high speed position control; encoder.
    DOI: 10.1504/IJMA.2022.10044163
  • Estimating future forceps movement using deep learning for robotic camera control in laparoscopic surgery   Order a copy of this article
    by Yamato Umetani, Masahiko Minamoto, Shigeki Hori, Tetsuro Miyazaki, Kenji Kawashima 
    Abstract: In laparoscopic surgery, an assistant surgeon must hold the laparoscope. Autonomous control of the holder helps the surgeon focus on the surgery. Estimating the future movement of the forceps can improve the control performance of a holder robot. We have previously proposed a method for estimating the position of the forceps 0.1 seconds ahead in a 2D image by using deep learning, based on segmented forceps in the camera image. In this study, we extend the prediction time to 0.1-3 seconds ahead, and investigate the accuracy of the estimation when the number of past position inputs to the convolutional neural network changes. We confirm that the forceps position in a 2D image 0.8 seconds ahead can be estimated online using only the past three positions in the suturing task within an error of 30 pixels, which is acceptable for laparoscope holder control.
    Keywords: laparoscopic surgery; camera holder; motion estimation; semantic segmentation; deep learning; neural network; forceps; mean intersection over union; MIoU; pre-training; learning rate.
    DOI: 10.1504/IJMA.2022.10044512
  • Algebraic models based on trigonometric and Cramer's rules for computing inverse kinematics of robotic arm   Order a copy of this article
    by Khairul Annuar Abdullah, Zuriati Yusof, Raja Mohd. Tariqi Raja Lope Ahmad, Muhammad Fairuz Abd. Rauf, Zuraidy Adnan, Wan Azlan Wan Hassan, Riza Sulaiman 
    Abstract: This study postulates applicable and high-performing solutions for the inverse kinematic (IK) problem of two-segmented robotic arm using algebraic models. The former section of models is acquired from the manipulation of trigonometric rules, specifically the sum and Pythagorean identities to solve the second joint angles. The latter part of models is drawn by exploiting a matrix mechanics called Cramer's rule for the results of first joint angle. For verification, the precision of solutions yielded are cross-checked with the manipulator's direct kinematics and tested with the statistical measure of minimum square error while tracking cubic Hermite spline, cubic Bezier, cubic B-spline, ellipse and circle curves. For validation, a spreadsheet-based IK application utilising built-in front-end capabilities including Visual Basic for applications, Math and Trig function library, name manager, data validation, ActiveX controls, and charts is developed to accommodate these models and simulate the feasible joint angles and orientations of robotic arm.
    Keywords: algebraic model; Cramer's rule; curved path tracking; inverse kinematics; mean square error; motion control; robotic arm; trigonometric rule.
    DOI: 10.1504/IJMA.2022.10044162
  • Recognising and predicting gait cycle states for weight-reducing exoskeleton robots using deep learning   Order a copy of this article
    by Hanqing Zhao, Hidetaka Nambo 
    Abstract: In this study, we propose to use plantar pressure distribution data. Using a combined approach of deep learning and ensemble learning. A proposal for implementing weight-reducing exoskeleton robot in walking gait recognition and temporal prediction. The challenge points of this problem is system architecture design, transformation of data features and morphology, plantar pressure data acquisition and gait data recognition and prediction in real-time. In this paper, we design the system design and implementation for real-time plantar pressure data collection using IoT approach, and real-time gait recognition and prediction using CNN + RNN + ensemble learning model. We designed a plantar pressure measurement device and obtained walking gait datasets through the device. The model is trained using the dataset to obtain a gait recognition and prediction model. Our proposed system solution was implemented for both non-actual walking and actual walking experiments. It is shown experimentally that the gait recognition and gait prediction results of the integrated learning approach in the non-actual walking experiments are 96% and 37%. The gait recognition and gait prediction results of the integrated learning approach in the actual walking experiments are 69% and 42% results.
    Keywords: robotic exoskeleton systems; deep learning; IoT; man-machine intelligent system; walk assist.
    DOI: 10.1504/IJMA.2021.10043413
  • A robotic wheel locally transforming its diameters and the reinforcement learning for robust locomotion   Order a copy of this article
    by Naoki Moriya, Hiroki Shigemune, Hideyuki Sawada 
    Abstract: The implementation of the neural network has been paid attention in the autonomous operation of robots. In particular, it is efficient for a robot itself to learn the locomoting method to get over different obstacles on rough terrains. We are developing a robotic wheel that can locomote stably even on rough terrain, and introduce the reinforcement learning for the ability to robustly get over an obstacle. Our robot is able to locomote by utilising the extension and returning of the diameters by moving its centre of gravity. We study its mobility through four experiments, which are the testing of the locomotion on flat ground, the climbing over a step, controlling the robotic wheel by IMU, and the braking performance. After the learning, we verify the performance of getting over a step of 10 cm and 20 cm, which are equivalent to 25% and 50% of the wheel diameter, respectively.
    Keywords: robotic wheel; variable diameter; climbing over obstacles; reinforcement learning.
    DOI: 10.1504/IJMA.2022.10044310
  • Prototype design and performance analysis of genetic algorithm-based SLAM for indoor navigation using TETRIX Prizm mobile robot   Order a copy of this article
    by Abhilasha Singh, V. Kalaichelvi, R. Karthikeyan 
    Abstract: For any robot to navigate on its own, it is important to create a map of its surroundings and localise itself to detect the obstacles. The present paper focuses on building a universal robotic description format model for the TETRIX robot followed by optimisation of 2D simultaneous localisation and mapping parameters using genetic algorithm in ROS-based environment to make robot navigation efficient. The GMapping and adaptive Monte Carlo localisation parameters were optimised using multiobjective GA. Furthermore, map quality is validated by using no reference blind/referenceless image spatial quality evaluator factor. The statistical analysis based on the mean and standard deviation of particle filters was analysed to measure the spread the particles filters during localisation. The test environment was created in ROS, Gazebo and MATLAB and with optimal parameters good quality high-resolution map with accurate and efficient localisation was achieved.
    Keywords: GMapping; AMCL; TETRIX; genetic algorithm; autonomous navigation; BRISQUE.
    DOI: 10.1504/IJMA.2021.10043373
  • Design and development of a 3D-printed balloon type actuator for a hybrid force-display glove   Order a copy of this article
    by Ken'ichi Koyanagi, Daisuke Takata, Takumi Tamamoto, Kentaro Noda, Takuya Tsukagoshi, Toru Oshima 
    Abstract: A balloon actuator is a type of pneumatic actuator developed for a virtual reality-based hand rehabilitation device, which is a hybrid force-display glove that combines an actuator and passive element. A balloon actuator typically generates a strong force and is safe for hand rehabilitation; however, the actuator has slow response characteristics for force display applications that use virtual reality techniques. Herein, we demonstrate an improved balloon actuator that features a 3D-printed inner construction optimised for glove type force display device with a soft material, and the basic and force display characteristics of the 3D-printed balloon actuator. The 3D-printed balloon actuator shows the force display characteristics by representing VR objects at an acceptable level.
    Keywords: pneumatic actuator; balloon type actuator; soft actuator; 3D printing; design optimisation; rehabilitation; haptics; force display; virtual reality; hybrid system; glove type device; mechatronics.
    DOI: 10.1504/IJMA.2021.10043906