# Vision-Guided Six-Legged Walking of Little Crabster Using a Kinect Sensor
## 1 Introduction  
The 2011 nuclear power plant explosion in Fukushima, Japan, was the result of a powerful earthquake and massive tsunami. Because it was impossible for humans to perform clean-up and dismantling tasks in the nuclear power plant, military robots, T-hawks, and Packbots from the United States were dispatched to the power plant. However, they were not able to complete their missions due to uneven terrain, including slopes, stairs, and ladders. After this failure, the Defense Advanced Research Project Agency (DARPA) in 2012 launched a robotics challenge project requesting robots to perform tasks specified particularly for disaster circumstances, and Team ViGIR realized those tasks through computer simulations in 2013 [1]. [PERSON] et al. generated walking paths considering the center of mass (CoM) and performed simulations and experiments of biped walking and ladder climbing using the humanoid robot ATLAS (Boston Dynamics Co., Boston, MA, USA) [2]. In 2014, Team KAIST implemented dynamic and static walking on the DRC-HUBO for rough terrain similar to that of disaster environments while applying a vision system to the DRC-HUBO for object recognition and gripping [3]. [PERSON] et al. generated a walking path and achieved stable walking on uneven terrain for a small six-legged robot with stereo cameras [4]. Likewise, numerous studies on locomotion and tasks in hazardous environments were actively performed. Because most disaster environments include rough terrain, locomotion capability on rough terrain is essential for successful movement. [PERSON] et al. achieved stable walking on inclined slopes for aiRobot-V by measuring ZMP error and trunk inclination using foot force sensors and an accelerometer [5]. They proposed a dynamic balance controller that includes a Kalman filter and a fuzzy motion controller. [PERSON] et al. proposed a biped-walking trajectory generator based on the three-mass angular momentum modelusing model predictive control. As a result, they were able to reduce both the modeling error and zero moment point horizon and modify walking patterns against unexpected obstacles [6]. In addition to biped walking robots, [PERSON] al. developed six-legged walking of the LAURON V on uneven terrain by applying sensor data including currents and ground contacts to stance and swing behaviors [7]. As far as we have been able to investigate, most legged robots have used blind-walking methods, which modify foot landing position based on the measured foot force, without using a vision system. While blind-walking methods enable legged robots to walk on rough terrain, blind-walking is somewhat slow and leads to a high risk of the robot falling down when confronted with excessive height or slope changes. Moreover, if there is an obstacle higher than the foot swing height, the foot may collide with the obstacle, leading to a fatal malfunction for robot systems. In order to solve these problems of blind-walking, studies on obstacle avoidance using vision sensors during walking have been gradually conducted since 2000. [PERSON] et al. obtained locations of a robot and a target object from landmarks attached to specific locations and multiple cameras attached to the external environment within the moving range [8]. They used the Monte Carlo algorithm to create a map and realize the location of the robot. [PERSON] et al. proposed a vision-guided walking method that can calculate locations of obstacles for the humanoid robot ASIMO and generate a walking path while avoiding obstacles in real-time [9]. These studies have an advantage since their robots can easily obtain accurate obstacle locations by installing vision systems in the external environment and not directly onto the robot body. On the other hand, there is a disadvantage in that the locomotion range of the robot is restricted due to the limitations in the captured image of the area with the environment-fixed vision systems.