In this study, a module combining various types of sensors was developed to increase search efficiency inside collapsed buildings. It was designed to be less than 70 mm in diameter so that it can be put into narrow spaces, and is equipped with a small & high-performance processor to process multiple sensor data. To increase sensor data processing efficiency, multi thread based software was configured, and the images were combined and transmitted to ensure time synchronization of multi-channel video data. A human detection function based on sound source detection using two microphones was implemented. The developed multi-sensor module was tested for operation by mounting it on a snake-type robot in a test bed simulating a disaster site. It was confirmed that the visible range of the robot to which the multi-sensor module was applied was expanded, and the ability to detect human and low-light human detect was secured.

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.