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

Kengo Iwao, Hikaru Arita, Kenji Tahara

TL;DR

This work proposes a method for simultaneous articulated robot posture estimation and environmental mapping by integrating data from proximity sensors distributed over the whole body by extending the discrete-time model, typically used for state estimation, to the spatial direction of the articulated structure.

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.

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

TL;DR

This work proposes a method for simultaneous articulated robot posture estimation and environmental mapping by integrating data from proximity sensors distributed over the whole body by extending the discrete-time model, typically used for state estimation, to the spatial direction of the articulated structure.

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.
Paper Structure (17 sections, 15 equations, 7 figures, 1 table, 1 algorithm)

This paper contains 17 sections, 15 equations, 7 figures, 1 table, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. Proposed method
  3. Mathematical Notation
  4. Problem Statement
  5. Foundational method
  6. System Overview
  7. Articulated Structure Model
  8. Measurement Model
  9. Iterated Extended Kalman Filter
  10. Simulation
  11. Effects of Spatial Direction Estimation
  12. Robustness to Root Uncertainty
  13. Discussion
  14. Advantages of Estimation in the Spatial Direction
  15. Contribution to the Estimation of Roots
  16. ...and 2 more sections

Figures (7)

  • Figure 1: Overview of the method. $\theta$ is the angle obtained from the encoder. The red arrows represent the spatial direction, and the blue arrows represent the temporal direction.
  • Figure 2: Estimation process
  • Figure 3: A snapshot of the simulation. (a), (d) show the actual structure in the simulation environment; (b), (e) show the states and acquisition environment obtained via the proposed method; and (c), (f) show the states and acquisition environment obtained via the kinematic model. The position and orientation of the robot are represented in coordinate systems, where the origin of each system is located at the center of the adjacent joint on the root side of each link. In these coordinate systems, the x-axis is denoted by red, the y-axis by green, and the z-axis by blue.
  • Figure 4: Average absolute value of the error with respect to the true value of the position for each link.
  • Figure 5: Surrounding environment acquired from the entire structure.
  • ...and 2 more figures