North Korea has repeatedly provoked using unmanned aerial vehicles (UAVs), and the threat posed by UAVs continues to escalate, as evidenced by recent directives involving the use of waste-laden balloons and the development of suicide drones. North Korea’s small UAVs are difficult to detect due to their low radar cross-section (RCS) values, necessitating the efficient deployment and operation of assets for effective response. Against this backdrop, this study aims to predict the infiltration routes of enemy UAVs by considering their perspective, avoiding key facilities and obstacles, and propose deployment strategies to enable rapid detection and response during provocations. Utilizing the Markov Decision Process (MDP) based on previous studies, this research presents a model that reflects both UAV flight characteristics and regional environments. Unlike previous models that designate a single starting point, this study addresses the practical challenge of uncertainty in initial infiltration points by incorporating multiple starting points into the scenarios. By aggregating and integrating the probability maps derived from these variations into a unified map, the model predicts areas with a high likelihood of UAV infiltration over time. Furthermore, based on case studies in the capital region, this research proposes deployment strategies tailored to the specifications of currently known anti-drone integrated systems. These strategies are expected to support military decision-making by enabling the efficient operation of assets in areas with a high probability of UAV infiltration.

Obstacle avoidance is one of the most important parts of autonomous mobile robot. In this study, we proposed safe and efficient local path planning of robot for obstacle avoidance. The proposed method detects and tracks obstacles using the 3D depth information of an RGB-D sensor for path prediction. Based on the tracked information of obstacles, the paths of the obstacles are predicted with probability circle-based spatial search (PCSS) method and Gaussian modeling is performed to reduce uncertainty and to create the cost function of caution. The possibility of collision with the robot is considered through the predicted path of the obstacles, and a local path is generated. This enables safe and efficient navigation of the robot. The results in various experiments show that the proposed method enables robots to navigate safely and effectively.