A closed-loop approach for tracking a humanoid robot using particle filtering and depth data

Abstract

Humanoid robots introduce instabilities during biped march that complicate the process of estimating their position and orientation along time. Tracking humanoid robots may be useful not only in typical applications such as navigation, but in tasks that require benchmarking the multiple processes that involve registering measures about the performance of the humanoid during walking. Small robots represent an additional challenge due to their size and mechanic limitations which may generate unstable swinging while walking. This paper presents a strategy for the active localization of a humanoid robot in environments that are monitored by external devices. The problem is faced using a particle filter method over depth images captured by an RGB-D sensor in order to effectively track the position and orientation of the robot during its march. The tracking stage is coupled with a locomotion system controlling the stepping of the robot towards a given oriented target. We present an integral communication framework between the tracking * The first two authors contributed equally. and the locomotion control of the robot based on the Robot Operating System (ROS), which is capable to achieve real time locomotion tasks using a NAO humanoid robot.

Robotic Leg Prosthesis: A Survey From Dynamic Model to Adaptive Control for Gait Coordination

“…In the probability framework, Kalman filtering that follows statistical properties has emerged under practical problems, such as estimating the center-of-mass motion [202], the instabilities of humanoid bipedal robots [203], and the motion prediction [204]. In addition, extensive studies on filter-based control of prosthetic legs [66][67], i.e., extended Kalman filter (EKF) [205][206], are carried out, thus providing the state estimation for prosthetic control.…”