Multi-sensor Fusion for Stiffness Estimation to Assist Legged Robot Control in Unstructured Environment

Abstract

Legged robot is designed for more flexibility when navigating in complex unstructured environment. When the end-effectors of the robot contacting non-rigid ground, the robot sinks due to different stiffness of the ground. This presents a challenge for accurate and robust control of the upper platform. In this paper, a real-time muti-sensor fusion method Dual Parallelizable Particle Filter (DPPF) is proposed to estimate ground stiffness. DPPF utilized RGB-D camera, IMU and 3-DoF force sensors. Meanwhile, we established a ground material database and trained a real-time ground segmentation network to assist the stiffness estimation of the ground. Then the information of ground material is utilized as a prior distribution for DPPF to achieve faster stiffness estimation. The experiments on synthetic data and on six-legged robot show that DPPF has faster computing speed, fewer convergent steps than previous state estimation methods. The estimated stiffness can be utilized for legged robot impedance control, posture control and trajectory planning.