• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.2
• /
• pp.180-188
• /
• 1999

The objective of this paper is, based upon the principles of artificial life, to induce emergent behaviors of multiple autonomous mobile robots which complex global intelligence form from simple local interactions. Here, we propose an architecture of neural network learning with reinforcement signals which perceives the neighborhood information and decides the direction and the velocity of movement as mobile robots navigate in a group. As the results of the simulations, the optimum weight is obtained in real time, which not only prevent the collisions between agents and obstacles in the dynamic environment, but also have the mobile robots move and keep in various patterns.

• International Journal of Advanced Culture Technology
• /
• v.11 no.1
• /
• pp.165-170
• /
• 2023

This study focuses on the development of intelligent combat robot systems for future warfare. The research is structured as follows: First, the introduction presents the rationale for researching intelligent combat robots and their potential to become game changers in future warfare. Second, in the context of the intelligent robot paradigm, this study proposes the need for military organizations to innovate their combat concepts and weapon systems through the effective utilization of Artificial Intelligence, Cognitive, Biometric, and Mechanical technologies. This forms the theoretical background of the study. Third, the analysis of intelligent robot systems considers five examples: humanoid robots, jumping robots, wheeled and quadrupedal pack robots, and tank robots. Finally, the discussion and conclusion propose that intelligent combat robots should be selected as game changers in military organizations for future warfare, and suggest further research in this area.

• International journal of advanced smart convergence
• /
• v.12 no.2
• /
• pp.108-116
• /
• 2023

Smart home and artificial intelligence technologies are developing rapidly, and various smart home systems associated with artificial intelligence (AI) improved the quality of living for people. In the present research, we examine the role of individuals' motivational system in their responses to the application of AI in smart homes. In particular, this research focuses on individuals' prevention motivational system and investigates whether individuals' attitudes toward the application of AI in smart homes differ according to their level of prevention motivation. Specifically, it is hypothesized that individuals with strong (vs. weak) prevention motivation will have more favorable attitudes toward the application of AI in smart homes. Consistent with the hypothesis, the results reveal that the respondents in the strong (vs. weak) prevention motivation reported significantly more favorable attitudes toward the six types of AI-based application in smart homes (e.g., AIbased AR/VR games, AI pet care system, AI robots, etc.). Our findings suggest that individuals' prevention motivational system may be an effective market segmentation tool in facilitating their positive responses to the application of AI in smart homes.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.8
• /
• pp.471-481
• /
• 2003

The acts of biological immune system are similar to the navigation for autonomous mobile robots under dynamically changing environments. In recent years, many researchers have studied navigation algorithms using artificial immune networks. Conventional artificial immune algorithms consist of an obstacle-avoidance behavior and a goal-reaching behavior. To select a proper action, the navigation algorithm should combine the obstacle-avoidance behavior with the goal-reaching behavior. In this paper, the neural network is employed to combine the behaviors. The neural network is trained with the surrounding information. the outputs of the neural network are proper combinational weights of the behaviors in real-time. Also, a velocity control algorithm is constructed with the artificial immune network. Through a simulation study and experimental results for a autonomous mobile robot, we have shown the validity of the proposed navigation algorithm.

• Genomics & Informatics
• /
• v.17 no.4
• /
• pp.44.1-44.3
• /
• 2019

Artificial intelligence (AI), big data, and ubiquitous robotic companions -the three most notable technologies of the 4th Industrial Revolution-are receiving renewed attention each day. Technologies that can be experienced in daily life, such as autonomous navigation, real-time translators, and voice recognition services, are already being commercialized in the field of information technology. In the biosciences field in Korea, such technologies have become known to the local public with the introduction of the AI doctor Watson in large number of hospitals. Additionally, AlphaFold, a technology resembling the AI AlphaGo for the game Go, has surpassed the limit on protein folding predictions-the most challenging problems in the field of protein biology. This report discusses the significance of AI technology and big data on the bioscience field. The introduction of automated robots in this field is not just only for the purpose of convenience but a prerequisite for the real sense of AI and the consequent accumulation of basic scientific knowledge.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.4
• /
• pp.633-640
• /
• 2022

As service robots are applied to various fields, interest in an image processing processor that can perform an image processing algorithm quickly and accurately suitable for each task is increasing. This paper introduces an image processing processor design method applicable to robots. The proposed processor consists of an AGX board, FPGA board, LiDAR-Vision board, and Backplane board. It enables the operation of CPU, GPU, and FPGA. The proposed method is verified through simulation experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.52-54
• /
• 2021

With the advent of the era of artificial intelligence, robot education and teaching under its empowerment have been widely concerned and applied worldwide. The purpose of this study: systematically evaluate the application effect of AI educational robots in student education and teaching; the method of this study: use the computer to search for relevant education in the search tools such as "Web of Science", "CNKI", "ERIC", "IEEE" A comparative study of the effects of robot teaching and traditional teaching. The retrieval time is from January 2000 to January 2020. Comprehensive MetaAnalysis 2.0 was used for Meta analysis. The results of this study: A quantitative analysis of the 31 valid research literatures included, and an objective evaluation of the effect of the meta-analysis on AI educational robots. The analysis results show that the combined effect of AI educational robots on student learning effects is 0.465 This indicates that educational robots have a moderately positive effect on students 'learning effectiveness. The conclusion of this study: The application effect of AI educational robots in student education and teaching is better than traditional education methods, which can better promote student learning.

• International Journal of Computer Science & Network Security
• /
• v.22 no.11
• /
• pp.294-302
• /
• 2022

The article explores the prospects and trends for the transformation of some basic concepts of law associated with the development of artificial intelligence systems and the problems of liability for harm caused by a robot. The prospects, conditions and consequences of vesting robots with partial (quasi) or full legal personality are explored. This process should lead to a revision of the concepts of will, subjective side and legal responsibility in the direction of their greater universalization. The legally significant signs of will, legal personality, legal liability in relation to robots, artificial intelligence systems and other complex automated information systems are clarified. The author identifies the following essential factors of legal qualification of an act committed by a robot: goals, reasons for setting goals, connections between the planned result and the action taken, the actual result, the reasons for the difference between the actual result and the planned one. The article pays special attention to the preventive function of legal liability, which, when applied to robot subjects, can be expressed in the following basic procedures. 1. Accounting for legal requirements in the behavior of the robot. 2. Timely adaptation of the robot to changes in legislation and other regulatory legal acts that affect its behavior. 3. Accounting for incidents. 4. Destruction of a series of robots whose actions lead to unacceptable consequences.

• Journal of IKEEE
• /
• v.28 no.2
• /
• pp.168-173
• /
• 2024

Speaker recognition refers to a technology that analyzes voice frequencies that are different for each individual and compares them with pre-stored voices to determine the identity of the person. Deep learning-based speaker recognition is being applied to many fields, and pet robots are one of them. However, the hardware performance of pet robots is very limited in terms of the large memory space and calculations of deep learning technology. This is an important problem that pet robots must solve in real-time interaction with users. Lightening deep learning models has become an important way to solve the above problems, and a lot of research is being done recently. In this paper, we describe the results of research on lightweight speaker recognition for pet robots by constructing a voice data set for pet robots, which is a specific command type, and comparing the results of models using residuals. In the conclusion, we present the results of the proposed method and Future research plans are described.

• The Journal of Korea Robotics Society
• /
• v.1 no.2
• /
• pp.163-171
• /
• 2006

This paper introduces the research progress on the artificial brain in the Telerobotics and Control Laboratory at KAIST. This series of studies is based on the assumption that it will be possible to develop an artificial intelligence by copying the mechanisms of the animal brain. Two important brain mechanisms are considered: spike-timing dependent plasticity and dopaminergic plasticity. Each mechanism is implemented in two coding paradigms: spike-codes and rate-codes. Spike-timing dependent plasticity is essential for self-organization in the brain. Dopamine neurons deliver reward signals and modify the synaptic efficacies in order to maximize the predicted reward. This paper addresses how artificial intelligence can emerge by the synergy between self-organization and reinforcement learning. For implementation issues, the rate codes of the brain mechanisms are developed to calculate the neuron dynamics efficiently.