State Estimation and Environment Recognition for Articulated Structures via Proximity Sensors Distributed over the Whole Body

TL;DR

This paper introduces a novel method for simultaneous state estimation and environment recognition in low-rigidity robots using distributed proximity sensors, improving accuracy in contact-rich scenarios.

Contribution

It extends existing state estimation models to incorporate spatial directions and sensor data from the entire body, enabling better environmental interaction understanding.

Findings

Significant reduction in estimation errors in simulations

Effective integration of proximity sensor data for environment mapping

Enhanced posture estimation accuracy for articulated structures

Abstract

For robots with low rigidity, determining the robot's state based solely on kinematics is challenging. This is particularly crucial for a robot whose entire body is in contact with the environment, as accurate state estimation is essential for environmental interaction. We propose a method for simultaneous articulated robot posture estimation and environmental mapping by integrating data from proximity sensors distributed over the whole body. Our method extends the discrete-time model, typically used for state estimation, to the spatial direction of the articulated structure. The simulations demonstrate that this approach significantly reduces estimation errors.