• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.278-284
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• 2019

Legged locomotion has high mobility on irregular surfaces by touching the ground at discrete points. Inspired by the creature's legged locomotion, legged robots have been developed to explore unstructured environments. In this paper, we propose a modular crawler that can easily adjust the number of legs for adapting the environment that the robot should move. One module has a pair of legs, so the number of legs can be adjusted by changing the number of modules. All legs are driven by a single driving motor for simple and compact design, so the driving axle of each module is connected by the universal joint. Universal joints between modules enable the body flexion for steering or overcoming higher obstacles. A prototype of crawler with three modules is built and the driving performance and the effect of module lifting on the ability to overcome obstacles are demonstrated by the experiments.

• Advances in robotics research
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• v.1 no.1
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• pp.101-126
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• 2014

In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions are achieved by a phase switching network (PSN) module. The combination of these modules generates various locomotion patterns and a reactive obstacle avoidance behavior. The behavior is driven by sensor inputs, to which additional neural preprocessing networks are applied. The complete neural circuitry is developed and tested using a physics simulation environment. This study verifies that the neural modules can serve a general purpose regardless of the robot's specific embodiment. We also believe that our neural modules can be important components for locomotion generation in other complex robotic systems or they can serve as useful modules for other module-based neural control applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.421-441
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• 2021

Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.6
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• pp.379-388
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• 2018

In this study, we propose a new approach to impliment autonomous travelling of mobile robot based on obstacle avoidance and voice command. Obstacle Avoidance technology of mobile robpot. It has been used in wide range of different robotics areas to minimize the risk of collisions. Obstacle avoidance of mobile robots are mostly applied in transportation systems such as aircraft traffic control, autonomous cars etc. Collision avoidance is a important requirement in mobile robot systems where they all featured some kind of obstacle detection techniques in order to avoid colliding. In this paper it was illustrated the reliability of voice command and obstacle avoidance for autonomous travelling of mobile robot with two wheels as the purpose of application to the manufacturing process by simulation and experiments.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.132-139
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• 2022

The development of the fourth industrial revolution and logistics 4.0 related technology, the growth of the e-commerce market, and the transition to a non-face to face society due to the pandemic are accelerating the growth of the logistics industry. Due to the growth of the logistics industry, various services are emerging to meet the requirements of the market, and research and technology development related to the parcel storage, which is an important element of the last mile service, is also underway. In the past, if it was difficult to deliver the goods directly to the recipient, the parcel storage installed near the delivery location was used, but the usability was not good and the storage of the goods was limited. In addition, the existing parcel storage has a lot of functional limitations compared to the advanced logistics technology, so it is necessary to develop a device that improves it. Therefore, this study conducted to secure safety for unmanned parcel storage devices with robot technology to improve usability and functionality in line with the advanced logistics industry. Based on ISO 10218, an industrial robot related standard, risk identification studies were conducted to derive results that contribute to the development of devices under development.

• Journal of the Korea Safety Management & Science
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• v.25 no.1
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• pp.15-21
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• 2023

In modern society, the delivery service market has grown explosively due to rapid changes in social structure and the recent COVID-19 pandemic. Therefore, various problems such as injury to workers and an increase in human accidents are occurring due to the loading and unloading of parcels. In order to solve this problem, domestic company n is developing a "robot-based cargo loading and unloading system". In developing a new technology system, quantitative reliability targets should be set for efficient operation and development. In this paper, reliability analysis was conducted through field data for the pneumatic gripper of the "robot-based cargo loading system". The reliability of the failure data was analyzed to estimate the distribution parameters and MTTF. Random data was derived for the probability of occurrence of a failure with the estimated value. By repeating the simulation to predict the number and year of failures according to the estimated parameters of the probability distribution, it was proposed as a method that reflects realistic probabilities rather than calculating with simple arithmetic using the average MTTF previously used in the field.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.121-138
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• 2024

Artificial intelligence is crucial to manufacturing productivity. Understanding the difficulties in producing disruptions, especially in linear feed robot systems, is essential for efficient operations. These mechanical tools, essential for linear movements within systems, are prone to damage and degradation, especially in the LM guide, due to repetitive motions. We examine how explainable artificial intelligence (XAI) may diagnose wafer linear robot linear rail clearance and ball screw clearance anomalies. XAI helps diagnose problems and explain anomalies, enriching management and operational strategies. By interpreting the reasons for anomaly detection through visualizations such as Class Activation Maps (CAMs) using technologies like Grad-CAM, FG-CAM, and FFT-CAM, and comparing 1D-CNN with 2D-CNN, we illustrates the potential of XAI in enhancing diagnostic accuracy. The use of datasets from accelerometer and torque sensors in our experiments validates the high accuracy of the proposed method in binary and ternary classifications. This study exemplifies how XAI can elucidate deep learning models trained on industrial signals, offering a practical approach to understanding and applying AI in maintaining the integrity of critical components such as LM guides in linear feed robots.

• The Journal of Korea Robotics Society
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• v.19 no.3
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• pp.287-292
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• 2024

The initial suppression of fires is critical to protecting human and material resources. In response to this, fire prevention and suppression systems using artificial intelligence and robot technology have recently been studied. In particular, an autonomous driving system that detects a fire using CNN is attracting attention. These systems respond quickly in the event of a fire, enabling initial fire suppression. However, since the conventional system is not equipped with a fire suppression function, direct intervention of firefighters is required. (1) To overcome these limitations, we propose an autonomous fire detection robot system equipped with a fire suppression function ROS-based firefighting system called 'ADEFS' (Autonomous-Detect & Extinguish-Fire Service). (2) The system performs three tasks to detect and extinguish. Tasks are to run the Ros-based SLAM Navigation, YOLO-CNN, and Four-degree freedom manipulator connected to the fire extinguishing pump. (3) Through this, early response in the event of a fire can minimize damage to life and property and can reduce labor costs, which can also be expected to reduce costs for companies.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.299-305
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• 2014

In this paper, a simple version of the hybrid navigation system using Kalman filter is proposed. The implemented hybrid navigation system is composed of a GPS to measure the position and the velocity, and a IMU(inertial measurement unit) to measure the acceleration and the posture of a mobile robot. A discrete Kalman filter is applied to provide the position of the robot by fusing both of the sensor data. When GPS signal is available, the navigation system estimates the position of the robot from the Kalman filter using position and velocity from GPS, and acceleration from IMU. During the interval until next GPS signal arrives, the system calculates the position of the robot using acceleration from IMU and velocity obtained at the previous step. Performance of the navigation system is verified by comparing the real path and the estimated path of the mobile robot. From experiments, we conclude that the navigation system is acceptable for the mobile robot.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2648-2653
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• 2003

This paper describes the development of a 3-DOF laparoscopic assistant robot system with motor-controlled bending and zooming mechanisms using the voice command motion control and auto-tracking control. The system is designed with two major criteria: safety and adaptability. To satisfy the safety criteria we designed the robot with optimized range of motion. For adaptability, the robot is designed with compact size to minimize interference with the staffs in the operating room. The required external motions were replaced by the bending mechanism within the abdomen using flexible laparoscope. The zooming of the robot is achieved through in and out motion at the port where the laparoscope is inserted. The robot is attachable to the bedside using a conventional laparoscope holder with multiple DOF joints and is compact enough for hand-carry. The voice-controlled command input and auto-tracking control is expected to enhance the overall performance of the system while reducing the control load imposed on the surgeon during a laparoscopic surgery. The proposed system is expected to have sufficient safety features and an easy-to-use interface to enhance the overall performance of current laparoscopy.