IJCAS

Abstract : This special issue is dedicated to the papers that are extended from those originally submitted to the 38th ICROS Annual Conference (ICROS 2023), held in Sol Beach Hotel & Resort, Gangwon, Korea, from June 21 to 23, 2023. The objective is to establish a connection between the conference and IJCAS so that strong results from the annual conference can be published in IJCAS in a timely manner. Prof. Hyo-Sung Ahn, the Editor-in-Chief of IJCAS brought this idea to the organizing committee of ICROS.
Motivated by Prof. Ahn’s idea, the organizing committee developed detailed guidelines. Call for paper for this special issue was announced in April 2023 and the interested authors are asked to contact the guest editor before the submission deadline to the conference. The authors are informed that their papers will undergo the standard review process of IJCAS and that they have four weeks to prepare the revision. The guest editors received 20 applications, and 15 papers have been received before the conference took place. The papers were handled by three associate editors who committed to make the review process fast without sacrificing the high standard of the journal. At the end of review process, the following seven papers were chosen for publication:
1. “Compensated Motion and Position Estimation of a Cable-driven Parallel Robot Based on Deep Reinforcement Learning” by Huaishu Chen, Min-Cheol Kim, Yeongoh Ko, and Chang-Sei Kim*.
2. “Design of Humanoid Robot Foot to Absorb Ground Reaction Force by Mimicking Longitudinal Arch and Transverse Arch of Human Foot” by Jindeok Lee and Hyun-Min Joe*.
3. “Enhancing Low-light Images for Monocular Visual Odometry in Challenging Lighting Conditions” by Donggil You, Jihoon Jung, and Junghyun Oh*.
4. “Safe Trajectory Path Planning Algorithm Based on RRT* While Maintaining Moderate Margin From Obstacles” by Subin Lim and Sangrok Jin*.
5. “Connection Loss Detection Algorithm of Parallel-connected Cells Based on Change of Battery SOC” by Byeonggwan Jang, Hyoseo Choi, Wooyong Kim*, and Kyung-Soo Kim*.
6. “A Current Sensor Fault-detecting Method for Onboard Battery Management Systems of Electric Vehicles Based on Disturbance Observer and Normalized Residuals” by Wooyong Kim, Kunwoo Na, and Kyunghwan Choi*.
7. “Design and Verification of Early Unstable Stage Control Scheme for High-speed Underwater Launched AUV” by Chul Hyun.
The guest editors would like to recommend that the editorial board of IJCAS continues to offer authors the opportunity for increased exposure by linking the upcoming annual conferences of ICROS with IJCAS.

Abstract : Unlike conventional rigid-link parallel robots, cable-driven parallel robots (CDPRs) have distinct advantages, including lower inertia, higher payload-to-weight ratio, cost-efficiency, and larger workspaces. However, because of the complexity of the cable configuration and redundant actuation, model-based forward kinematics and motion control necessitate high effort and computation. This study overcomes these challenges by introducing deep reinforcement learning (DRL) into the cable robot and achieves compensated motion control by estimating the actual position of the end-effector. We used a random behavior strategy on a CDPR to explore the environment, collect data, and train neural networks. We then apply the trained network to the CDPR and verify its efficacy. We also addressed the problem of asynchronous state observation and action execution by delaying the action execution time in one cycle and adding this action to be executed to match the motion control command. Finally, we implemented the proposed control method to a high payload cable robot system and verified the feasibility through simulations and experiments. The results demonstrate that the end-effector position estimation accuracy can be improved compared with the numerical model-based forward kinematics solution and the position control error can be reduced compared with the conventional open-loop control and the open-loop control with tension distribution form.

Abstract : In this paper, we describe a double arched robotic Foot-1 (DARFT-1) for a humanoid robot. The feet of many humanoid robots are equipped with force/torque (F/T) sensors for various purposes of walking control, including the calculation of zero-moment-point (ZMP), contact detection, and contact force control. However, there are cases where unexpectedly large ground reaction force (GRF) is applied to the F/T sensor when the humanoid robot walks on uneven ground, causing the F/T sensor to break easily. To protect the F/T sensor and achieve the mechanical filter effect, various robot feet are being studied. We propose a robot foot that mimics the longitudinal arch and transverse arch of a human foot to absorb GRF effectively. Each arch of the proposed foot consists of passive joints and springs and is designed with a 2-degrees-of-freedom (DoF) structure. Furthermore, DARFT-1 is designed to prevent external obstacles from entering the sole of the foot, while also being designed for shape adaptation to uneven ground. To verify the effectiveness of the designed foot, GRF measurement experiments were conducted by mounting the DARFT-1 on the humanoid robot DRC-HUBO+. Through the experiments, the DARFT1 reduced GRF by an average of 9.8% and 10.02% in three trials when placing the obstacle on the front and side of the foot, respectively, compared to the previous foot. In addition, the proposed foot performed as a mechanical filter by reducing the rate of change in the GRF. Furthermore, the reduced GRF decreased the ZMP, improving the stability of the humanoid robot’s walk.

Abstract : Visual odometry (VO) estimates the robot’s current position based on feature matching or brightness variation between images, making it primarily suitable for well-lit environments with good image quality. Consequently, existing visual odometry methods exhibit degraded performance in low-light or highly dynamic environments, limiting their operational efficiency in outdoor settings. To overcome these challenges, research has been conducted to enhance low-light images to improve odometry performance. Recent advancements in deep learning have facilitated extensive research on image enhancement, including low-light conditions. Utilizing generative adversarial networks (GANs) and techniques like CycleGAN, researchers have achieved robust improvements in various lighting conditions and enhanced odometry performance in low-light environments. However, these methods are typically trained on single images, compromising the structural consistency between consecutive images. In this paper, we propose learning-based low-light image enhancement and the preservation of structural consistency between consecutive images for monocular visual odometry. The proposed model utilizes the CycleGAN approach for domain transformation between different illumination levels, effectively avoiding the failure of visual odometry in low-light environments. To handle diverse lighting conditions within images, a local discriminator is employed to enhance local brightness. Additionally, a structure loss is introduced using sequence images to ensure structural consistency between the original and generated images. This method simultaneously improves low-light conditions and preserves structural consistency, leading to enhanced visual odometry performance in low-light environments.

Abstract : This paper presents Ex-RRT*, a modification of the rapidly-exploring random tree star (RRT*) algorithm that allows the robot to avoid obstacles with a margin. RRT* generates the shortest path to a destination while avoiding obstacles. However, if the robot’s embedded trajectory generation algorithm interpolates the waypoints generated by the RRT* to make a motion, collisions may occur with the edges or overhang of obstacles. This algorithm adds a cost function for the distance from each node to the nearest obstacle to ensure that the waypoints generated by the path planner have an appropriate margin for obstacles. It is designed to provide safer control from collisions when each robot’s embedded trajectory generation algorithm operates by interpolating waypoints derived by path planning. Through simulation, we compare the proposed Ex-RRT* and conventional RRT* with performance indices such as total distance traveled and collision avoidance norm. Experiments are conducted on the task of moving an object inside a box with a commercial robot to validate the proposed algorithm. The proposed algorithm generates paths with improved safety and can be applied to various robotic arms and mobile platforms.

Abstract : The battery pack consists of parallel-connected cells to satisfy the power and mileage per charge of the eco-friendly vehicles. The vehicle specifications determine the number of battery cells connected in parallel by the type of battery. In driving conditions, such as sharp bumps and rough roads, the welding used for the interconnection between the cells may become loose, potentially causing the cells to detach from the battery module. The detachment leads to a reduction in the capacity of the battery pack and increases cell-to-cell variation among the parallel-connected battery cells. The detection algorithm for identifying disconnections among parallel-connected cells in a module is essential for ensuring the safe operation of the battery pack. This paper introduces a novel method for detecting contact loss among parallel-connected cells by utilizing the state of charge (SOC) change rate of the cells. The algorithm utilizes the estimated internal resistance and the variation in the slope of the estimated SOC change to detect connection losses within a battery module. The proposed method is verified with the simulation using Matlab/Simulink. The performance of the proposed algorithm is validated in various cases with some scenarios.

Abstract : This study presents a current sensor fault-detecting method for an electric vehicle battery management system. The proposed current sensor fault detector comprises the nonlinear battery cell model, the Luenbergertype state estimator, and a disturbance observer-based current residual generator. The features of this study are summarized as follows: 1) A nonlinear state space representation of the battery cell model is derived so that the disturbance observer considering the engaged current as an external disturbance can be applied, 2) a nonlinear model-based state observer and disturbance observer are combined to deal with the state of charge estimation as well as the unknown current estimation and 3) the concept of the normalized residual is introduced for current sensor fault detection criteria. Because the proposed method can estimate the engaged current whether the current measurement is available or not, the residual between the estimated current and measured current can capture the current sensor fault. Additionally, the normalization process ensures the current sensor fault diagnosis can be realized regardless of the magnitude of the engaged current. The performance of the proposed current sensor fault algorithm was experimentally verified under several magnitudes of engaged current scenarios using a single battery cell.

Abstract : In this paper, the author proposes a noble robust control scheme for an underwater-launched high-speed autonomous underwater vehicle (AUV). The proposed controller can rapidly stabilize the AUV during the initial stage of instability after launch. AUVs are typically not stable after launch due to large acceleration, deceleration, and roll torque. By adding a suitable over-define control constraint to the control command, we can obtain simple yet robust control performance using a conventional controller that is designed for normal conditions. Preventing the control effort from being monopolized by a single axis is possible by distributing it along the axes in a predetermined ratio. In addition, by over-defining the total control capacity, it was able to achieve the effect of applying the maximum possible control effort when necessary. Simulation and sea test results show that the proposed control scheme is suitable for controlling an AUV after an underwater launch.

Abstract : Functional observers are the major alternative to many practical estimation problems where full-order observers cannot be used. This paper introduces a generalized approach to design H∞ functional observers for a class of Lipschitz nonlinear systems with multiple time delays. Moreover, the considered system extends from previously published work in that it presents nonlinearity, multiple delay and external disturbance. Their main findings come from the development of a generalized augmented Lyapunov function that uses both the extended reciprocal convex combination and the Wirtinger inequality. The stability of the observer is therefore guaranteed by an LMI optimization problem. Finally, the steps of the design procedure were condensed and proffered for the two numerical examples to test the recommended design approach.

Abstract : This paper proposes a new stability condition for a fractional order calculus (FOC)-based for the disturbance observer (DO) and sliding mode control (SMC) of a secure communication system (SCS). First, the mathematics of the chaotic system was remodeled into the Takagi-Sugeno fuzzy (TSF) with the aim of softening the calculation of control design and stability analysis. Second, the synchronization of two nonidentical chaotic systems was adopted by using a newly proposed Lyapunov-based stability condition. Third, a new disturbance observer was proposed based on the suggested stability condition together with the inversed model-based concept. To show the correction of the proposed method, the stability analysis was adopted to obtain the goal. Finally, the simulation by using MATLAB software was used to conduct the effectiveness of the proposed methods. The achievements are small tracking errors, stable steady-states, and all tested disturbances and uncertainties were mostly rejected.