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.

This paper introduces PongBot, a quadruped robot developed for preparation in the Dronebot Challenge held in Jangseonggun, Jeollanam-Do, South Korea in November 2020. The Dronebot Challenge, hosted by the Army Headquarters, is a competition to demonstrate that drones and robots can be useful for military purposes. In 2020, this competition consisted of a total of 8 events and we participated in the ‘Traveling on rough terrain’ event, which consisted of various terrains, such as, slopes, unpaved roads, and streams. PongBot is a quadruped robot that uses an electric motor and can walk for more than an hour on various terrains. Also, according to the rules of the competition, the robot had a system which could be remotely controlled from a ground control station. In addition, by applying the SLAM algorithm, the robot operator received information about its surrounding environment, thereby deriving records to facilitate the operation. The performance of this robot system and SLAM algorithm was verified through this competition.

The purpose of this study was to suggest the method for automated locomotion modes (Level Walking, Stair Ascent, Stair Descent) detection based on the Radial Basis Function Support Vector Machine (RBF-SVM) for the hip gait assist robot. The universal hip gait assist robot had a limit in detection of the walking intention of users because of the limited sensors’ quantity. Through the offline training, using MATLAB, we trained the collected gait data of users wearing the hip gait assist robot and obtained the parameter of the RBF-SVM model. In the online test, using LabVIEW, we developed the algorithm for the locomotion modes decision of individuals using the optimized parameter of the RBF-SVM. Finally, we executed the gait test for three terrains through the walking environment’s test platform. As a result, the locomotion modes decision rate for three terrains was 98.5%, 99%, and 98% respectively. And the decision delay time of algorithm was 0.03 s, 0.03 s, and 0.06 s respectively.