Development of a Rover Swarm Autonomous Driving System with Lateral Control Based on the L1 Controller

Gyeonghun Lim, Sungtae Moon

http://doi.org/10.5626/JOK.2025.52.4.276

With the increasing use of unmanned vehicles in various fields, services such as logistics, surveillance, and reconnaissance are being actively provided. In particular, swarm driving, which involves multiple unmanned vehicles, is gaining attention due to its advantages over single-vehicle operation, such as reduced task time, expanded operational range, and improved system reliability. However, previous studies have often neglected lateral control, resulting in reduced precision in swarm driving, and due to the complexity and high cost of development, most have been conducted in simulation environments. This paper implements precise and stable swarm control by utilizing the lightweight and easy-to-implement L1 controller for lateral control. Furthermore, the proposed swarm driving system was developed using the low-cost, open-source PX4-ROS2 platform, and a Data Distribution Service based communication method was employed for communication between unmanned vehicles. The system was validated in real-world environments, confirming its performance.

Dimensional Sentiment Analysis of Korean Text using Data Balancing

Taehee Jeon, Changhwan Kim

http://doi.org/10.5626/JOK.2021.48.7.790

Compared with most studies on categorical sentiment analysis which aims to represent emotional states as a small set of emotion categories, there have been fewer studies on dimensional sentiment analysis which treats sentiment analysis as a regression problem because of the shortage of data. Recently, the National Information Society Agency (NIA) released open data, Multimodal Video Data, through their web site, AI Hub. Using this data, we experimented with dimensional sentiment analysis of Korean text. For this purpose, we used CNN which is one of the conventional deep learning models in NLP. We also verified that data balancing could improve the performance of models. The results show that the model trained on Multimodal Video Data performs well enough to show that the data should be useful for dimensional sentiment analysis of Korean text and that with data balancing the model can perform better in spite of their fewer training data.

Implementation and Performance Analysis of Event Processing and Buffer Managing Techniques for DDS

http://doi.org/

Data Distribution Service (DDS) is a communication middleware that supports a flexible, scalable and real-time communication capability. This paper describes several techniques to improve the performance of DDS middleware. Detailed events for the internal behavior of the middleware are defined. A DDS message is disassembled into several submessages of independent, meaningful units for event-driven structuring in order to reduce the processing complexity. The proposed technique of history cache management is also described. It utilizes the fact that status access and random access to the history cache occur more frequently in the DDS. These methods have been implemented in the EchoDDS, the DDS implementation developed by our team, and it showed improved performance.

Learning Multiple Instance Support Vector Machine through Positive Data Distribution

Joong-Won Hwang, Seong-Bae Park, Sang-Jo Lee

http://doi.org/

This paper proposes a modified MI-SVM algorithm by considering data distribution. The previous MI-SVM algorithm seeks the margin by considering the “most positive” instance in a positive bag. Positive instances included in positive bags are located in a similar area in a feature space. In order to reflect this characteristic of positive instances, the proposed method selects the “most positive” instance by calculating the distance between each instance in the bag and a pivot point that is the intersection point of all positive instances. This paper suggests two ways to select the “most positive” pivot point in the training data. First, the algorithm seeks the “most positive” pivot point along the current predicted parameter, and then selects the nearest instance in the bag as a representative from the pivot point. Second, the algorithm finds the “most positive” pivot point by using a Diverse Density framework. Our experiments on 12 benchmark multi-instance data sets show that the proposed method results in higher performance than the previous MI-SVM algorithm.