Ceiling inspections present challenges due to limited accessibility and structural constraints. To ease the burden on security personnel, who would otherwise need to manually disassemble, inspect, and restore ceiling components, this study proposes a robotic system for detecting hazardous objects within ceiling environments. The proposed system features several key innovations: a hollow-structured track mechanism designed to reduce vibrations from jolting while traversing structural beams and to improve localization accuracy. We optimized the robot’s mass distribution and required drive torque through dynamic simulations to ensure stable mobility in confined ceiling spaces. For effective hazardous object detection, we developed a YOLOv8-Seg-based background learning algorithm that suppresses ceiling-structure patterns, allowing for the identification of unknown objects without prior class-specific training. Additionally, we introduced a frame-based filtering algorithm to enhance detection reliability by reducing false positives caused by motion blur during movement. The system's effectiveness was validated through experiments conducted in a ceiling-structured testbed, demonstrating its capability for accurate hazardous object detection under realistic operating conditions.

This paper presents a novel method of designing an efficient locomotion pattern generating algorithm for snake robots by a genetic algorithm (GA). In search and rescue operations in disaster areas, a snake robot requires multiple locomotion patterns. To overcome the complexity of snake robot control, we used a central pattern generator (CPG)-based control method which mimics the motion of a biological snake. GA was used to optimize CPG parameters to maximize locomotion performance. The locomotion performance according to the CPG parameters change was analyzed using the snake robot simulator. The proposed locomotion pattern generation algorithm evolved quickly for the target performance and obtained CPG parameters for the desired locomotion.