https://scholar.dgist.ac.kr/handle/20.500.11750/103 2026-04-04T10:16:28Z 2026-04-04T10:16:28Z Kim, Sangjun Lee, Sanghoon Park, Kyung-Joon https://scholar.dgist.ac.kr/handle/20.500.11750/59335 2026-01-12T12:40:13Z 2025-10-31T15:00:00Z

Title: DIDoS: Disturbance-induced Denial-of-service Attack in Networked Cyber-physical Systems Author(s): Kim, Sangjun; Lee, Sanghoon; Park, Kyung-Joon Abstract: Event-triggered control (ETC) in a cyber-physical system (CPS) is an efficient aperiodic control strategy that generates control packets only when a control-triggering condition is satisfied. However, the CPS with ETC introduces a new point of vulnerability. A well-designed disturbance signal can unnecessarily trigger control events and the resulting excessive packet exchanges can destabilize the physical systems due to network saturation. In this paper, we propose a novel CPS attack vector entitled the disturbance-induced denial of service (DIDoS) attack, which has the following key characteristics: DIDoS cannot be mitigated by a conventional network security method such as a firewall. Unlike most cyber-physical attacks, DIDoS does not require knowledge of physical system dynamics. Under DIDoS, a disturbance signal into a single physical system can saturate the whole network and destabilize all the physical systems connected to the network. We study the relationship between the network delay and the stability of physical systems under DIDoS. We derive a stability condition under a time-varying network delay and quantitatively describe network saturation under DIDoS with an IEEE 802.11 wireless network model. Our simulation results show that DIDoS can saturate the network and destabilize all the physical systems.

2025-10-31T15:00:00Z Chae, Jiyeong Seo, Hyunkyo Lee, Sanghoon Park, Kyung-Joon https://scholar.dgist.ac.kr/handle/20.500.11750/59333 2026-02-03T10:40:18Z 2025-11-30T15:00:00Z

Title: Time-aware Costmap for Smoother and Less Disruptive AMR Navigation With ROS 2 Author(s): Chae, Jiyeong; Seo, Hyunkyo; Lee, Sanghoon; Park, Kyung-Joon Abstract: Autonomous mobile robots (AMRs) are increasingly deployed in industrial settings, where they perform various tasks that replace human labor, thereby boosting operational efficiency. In such smart manufacturing environments where multiple types of robots coexist, effective management of dynamic obstacles during AMR navigation is crucial. However, the standard ROS 2 navigation stack lacks built-in mechanisms specifically to handle dynamic obstacles. Many existing studies on dynamic obstacle avoidance are limited to local path planning, focusing primarily on real-time recognition and evasion based on sensor data. In this paper, we propose a time-aware costmap framework that leverages information about areas frequently occupied by dynamic obstacles, integrating it into the global costmap to enable smoother and less-disruptive navigation. The framework not only enhances AMR stability but also increases productivity in industrial environments. The time-aware costmap framework offers three key advantages: First, it enables the global planner to select routes that are smooth and minimally disruptive. Second, it integrates a custom layer featuring time-bound dynamic obstacles into the global costmap as a plugin, enabling seamless adaptation within existing navigation frameworks. Finally, it works effectively using only a 2D LiDAR sensor, minimizing hardware and software overhead. We validate the performance of the proposed framework in a Gazebo simulation environment modeled after a milk-run distribution system. The results demonstrate that the framework substantially improves safety and stability by lowering curvature, jerk, and collision risks. While this comes with a trade-off of about 20% lower throughput due to smoother detours, the framework’s emphasis on smoothness and safety offers greater practical value for reliable navigation in industrial environments.

2025-11-30T15:00:00Z Chae, Jiyeong Lee, Sanghoon Seo, Hyunkyo Park, Kyung-Joon https://scholar.dgist.ac.kr/handle/20.500.11750/59139 2025-11-03T01:10:10Z 2025-10-31T15:00:00Z

Title: From issues to routes: A cooperative costmap with lifelong learning for Multi-AMR navigation Author(s): Chae, Jiyeong; Lee, Sanghoon; Seo, Hyunkyo; Park, Kyung-Joon Abstract: In large-scale industrial environments where multi-AMR (Autonomous Mobile Robot) systems are deployed, the unpredictable occurrence of obstacles can significantly disrupt AMR navigation, hindering task execution. To overcome such disruptions, AMRs must frequently replan their routes in real time, often resulting in suboptimal trajectories. This paper proposes a multi-AMR path planning framework based on a Cooperative Costmap with Lifelong Learning, designed to enable efficient navigation even in environments where obstacle patterns are not known a priori. Inspired by issue-propagation models in social-network theory - which describe how public attention rises and fades over time within a network - the proposed approach models the temporal influence of encountered obstacles, allowing predictive path planning that adapts to changing obstacle patterns. The framework incorporates a lifelong learning mechanism to incrementally refine the influence parameter over time, thus ensuring adaptability in dynamic industrial settings. Simulation experiments demonstrate that the proposed approach increases task throughput by up to 18.0% and reduces average travel time by up to 30.1% compared to the standard ROS 2 navigation stack.

2025-10-31T15:00:00Z Lee, Sanghoon Chae, Jiyeong Jeon, Haewon Kim, Taehyun Hong, Yeong-Gi Um, Doo-Sik Kim, Taewoo Park, Kyung-Joon https://scholar.dgist.ac.kr/handle/20.500.11750/58503 2025-12-11T18:01:08Z 2025-03-31T15:00:00Z

Title: Cyber-Physical AI: Systematic Research Domain for Integrating AI and Cyber-Physical Systems Author(s): Lee, Sanghoon; Chae, Jiyeong; Jeon, Haewon; Kim, Taehyun; Hong, Yeong-Gi; Um, Doo-Sik; Kim, Taewoo; Park, Kyung-Joon Abstract: The integration of Cyber-Physical Systems (CPS) and AI presents both opportunities and challenges. AI operates on the principle that good things happen probabilistically,while CPS adheres to the principle that all bad things must not happen,requiring uncertainty-awareness. Furthermore, the difference between AI's resource accessibility assumption and CPS's resource limitations highlights the need for resource-awareness. We introduce Cyber-Physical AI (CPAI), an interdisciplinary sub-field of AI and CPS research, to address these constraints. To the best of our knowledge, CPAI is the first research domain on CPS-AI integration. We propose a 3D classification schema of CPAI: Constraint (C), Purpose (P), and Approach (A). We also systematize the CPS-AI integration process into three phases and nine steps. By analyzing 104 studies, we highlight nine key challenges and insights from a CPAI perspective. CPAI aims to unify fragmented studies and provide guidance for reliable and resource-efficient integration of AI as a component of CPS. © 2025 Copyright held by the owner/author(s).

2025-03-31T15:00:00Z