• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.9
• /
• pp.909-915
• /
• 2008

Recently, the interests in the development and application of unmaned robots are increasing in various fields including surveillance and reconnaissance, planet exploration, and disaster relief. Unmaned robots are usually controlled from distance using radio communications but they should be equipped with an autonomous travelling function to cope with unexpected terrains and obstacles. This means that they should be able to evaluate terrain's characteristics quantitatively using mounted sensors so as to traverse harsh natural terrains autonomously. For this purpose, this paper presents a method for extracting terrain information, that is, slope and roughness from elevation maps as a prior step of traversability analysis. Slope is extracted using the curve fitting based on the least squares method and roughness using three metrics and their weighted average. The effectiveness of the proposed method is verified on both a fractal map and the world model map of a real terrain.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2008.04a
• /
• pp.233-236
• /
• 2008

Recently, the development and application of unmaned robots are increasing in various fields including surveillance and reconnaissance, planet exploration and disaster relief. Unmaned robots are usually controlled from distance using radio communications but they should be equipped with autonomous travelling function to cope with unexpected terrains and obstacles. This means that unmanned robots should be able to evaluate terrain's characteristics quantitatively using mounted sensors so as to traverse harsh natural terrains autonomously. For this purpose, this paper presents an algorithm for extracting terrain information from elevation maps as an early step of traversability analysis. Slope and roughness information are extracted from a world terrain map based on least squares method and fractal theory, respectively. The effectiveness of the proposed algorithm is verified on both fractal and real terrain maps.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.25 no.5
• /
• pp.321-328
• /
• 2016

Cranes are generally used to drop or lift equipment or materials. The present study focuses on equipment used for dropping and lifting the sonar system for undersea exploration. This study deals with a GA-based MATLAB$^{(R)}$ simulation for the design optimization of a new overboarding prototype with a two degree-of-freedom mechanism, including a parallelogram link, which is efficient in sonar system operation and maintenance. First, the strengths and weaknesses of the existing overboarding mechanisms are analyzed. The new mechanism to solve these problems is then suggested. For the proposed mechanism, the GA-based MATLAB$^{(R)}$ simulation technique is applied to the proposed mechanism to optimize the link lengths and the actuator lengths. By doing this, the mechanism cannot interfere in the hull's internal environment. Hence, the work range of motion (ROM) is satisfied, and good torque-angle properties are obtaind. The developed technology will be helpful in calculating the maximized output torque of the actuator for the application in practice using a similar type of the proposed mechanism.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.489-490
• /
• 2021

본 연구에서는 해양사고 처리 중 발생하는 인명사고 발생률을 감소시키고, 효율적으로 양식장을 관리하는 것을 전제로 카메라와 센서를 다기능 해양탐사 로봇에 적용하고자 한다. 현재의 양식장 관리 시스템은 수온 체크만 할 수 있게 되어있다. 이러한 시스템은 양식어에게 적합한 환경을 제공해주기 쉽지 않다. 본 논문은 이러한 문제점들을 개선하기 위해 기존의 해양 처리시스템과 양식 시스템 대신 카메라와 수온 센서, pH 농도 센서, 초음파 거리 센서, DC 모터, 블루투스 모듈을 적용한 다기능 해양탐사 로봇 기술을 제안한다. 기존의 시스템과는 다르게 안전하고 효율적으로 환경을 분석하고, 제어할 수 있다.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.3
• /
• pp.9-17
• /
• 2021

In this paper, an autonomous multi-agent path planning using reinforcement learning for monitoring of infrastructures and resources in a computationally distributed system was proposed. Reinforcement-learning-based multi-agent exploratory system in a distributed node enable to evaluate a cumulative reward every action and to provide the optimized knowledge for next available action repeatedly by learning process according to a learning policy. Here, the proposed methods were presented by (a) approach of dynamics-based motion constraints multi-agent path-planning to reduce smaller agent steps toward the given destination(goal), where these agents are able to geographically explore on the environment with initial random-trials versus optimal-trials, (b) approach using agent sub-goal selection to provide more efficient agent exploration(path-planning) to reach the final destination(goal), and (c) approach of reinforcement learning schemes by using the proposed autonomous and asynchronous triggering of agent exploratory phases.

• Journal of the Korean Society for Precision Engineering
• /
• v.30 no.1
• /
• pp.11-17
• /
• 2013

'Multi-scale mass-deployable cooperative robots' is a next generation robotics paradigm where a large number of robots that vary in size cooperate in a hierarchical fashion to collect information in various environments. While this paradigm can exhibit the effective solution for exploration of the wide area consisting of various types of terrain, its technical maturity is still in its infant state and many technical hurdles should be resolved to realize this paradigm. In this paper, we propose to develop new design and manufacturing methodologies for the multi-scale mass-deployable cooperative robots. In doing so, we present various fundamental technologies in four different research fields. (1) Adaptable design methods consist of compliant mechanisms and hierarchical structures which provide robots with a unified way to overcome various and irregular terrains. (2) Soft composite materials realize the compliancy in these structures. (3) Multi-scale integrative manufacturing techniques are convergence of traditional methods for producing various sized robots assembled by such materials. Finally, (4) the control and communication techniques for the massive swarm robot systems enable multiple functionally simple robots to accomplish the complex job by effective job distribution.

• The Journal of Information Systems
• /
• v.32 no.4
• /
• pp.211-227
• /
• 2023

Purpose In the contemporary landscape, marked by the enduring impact of COVID-19 and the recent disruptions stemming from the conflict in Ukraine, the purpose of this study is to navigate the era characterized by pervasive risk and uncertainty. Specifically, the study aims to dissect the impact of the COVID-19 outbreak on digital transformation, exploring the factors influencing this process and considering the multifaceted dynamics at play. The focus extends to the post-COVID-19 landscape, scrutinizing the implications and meanings of digital transformation both before and after the pandemic. Additionally, the study delves into future digital trends, with particular attention to climate and environmental issues, emphasizing corporate responsibilities in averting crises similar to COVID-19. The overarching goal is to provide a holistic perspective, shedding light on both positive and negative facets of digital transformation, and advocating for regulatory enhancements and legal frameworks conducive to a balanced and resilient digital future. Design/methodology/approach This study employs a comprehensive approach to analyze the impact of the COVID-19 outbreak on digital transformation. It considers various facets, such as smart devices reshaping daily routines, transformative changes in corporate ecosystems, and the adaptation of government institutions to the digital era within the broader context of the Fourth Industrial Revolution. The analysis extends to the post-COVID-19 landscape, examining the implications and meanings of digital transformation. Future digital trends, especially those related to climate and environmental issues, are prognosticated. The methodology involves a proactive exploration of challenges associated with digital transformation, aiming to advocate for regulatory enhancements and legal frameworks that contribute to a balanced and resilient digital future. Findings The findings of this study reveal that the digital economy has gained momentum, accelerated by the proliferation of non-face-to-face industries in response to social distancing imperatives during the COVID-19 pandemic. Digital transformation, both preceding and succeeding the onset of the pandemic, has precipitated noteworthy shifts in various aspects of daily life. However, challenges persist, and the study highlights factors that either bolster or hinder the transformative process. In the post-COVID-19 era, corporate responsibilities in averting crises, particularly those resembling the pandemic, take center stage. The study emphasizes the need for a holistic perspective, acknowledging both positive and negative facets of digital transformation. Additionally, it calls for proactive measures, including regulatory enhancements and legal frameworks, to ensure a balanced and resilient digital future.