Autonomous Safe Locomotion System for Bipedal Robot Applying Vision and Sole Reaction Force to Footstep Planning Humanoid robots are expected to conduct tasks on behalf of humans in places such as a disaster scattered environment. Although humanoid robots have potentials to walk on uneven ground unlike wheeled robots, it is difficult to reach a given destination without falling down based on only visual information. In this paper, to reach the destination safely, we propose the autonomous safe locomotion system applying vision and sole reaction force to the footstep planning. Considering force information in addition to visual information, the robot can plan a path avoiding unstable footholds. The planned path is safer than a path which is planned based on only visual information. In our system, the robot checks if the foothold is safe or not by the foothold ascertainment motion. In addition to that, the robot saves the results of the motion to the database with the foothold label given by the visual classifier. To judge foothold safety, stiffness of the foothold is estimated from the reaction force and stepping amount. We propose the system considering these requirements for safe locomotion for bipedal robots and show experimental results using a real bipedal robot CHIDORI.

Avoiding Obstacles during Push Recovery Using RealTime Vision Feedback This paper introduces an obstacleavoiding algorithm for bipedal robots, especially in push recovery situations. Typically, There are many algorithms that plan footstep to avoid obstacles based on vision recognition data. However, if the robot is pushed, the planned footprint will change, and thus, there is no guarantee that it will avoid obstacles. Although modified stepping positions can be limited, the robotxe2x80x99s stability is not assured. Our proposed algorithm focuses on avoiding obstacles through vision recognition in push recovery situations and generating compensation actions for instability by restricting modified footsteps. We fuse vision feedback with our previous push recovery algorithm, which optimizes the ankle, hip, and stepping strategies. We build simple grid data using vision recognition and apply it to the inequality constraint of the stepping position. We validate the effectiveness of our algorithm using the bipedal platform GAZELLE with the Kinect V2 RGBD sensor.