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Mobile Robot Localization Using a Novel Whisker-Like Sensor

Prasanna K. Routray, Basak Sakcak, Steven M. LaValle, Manivannan M

TL;DR

This work addresses robust localization in confined environments by leveraging a single whisker-style tactile sensor. It introduces a virtual-sensor framework with multiple sensor models to map augmented robot states, including a beam-shape parameter, to observations, and uses preimages to quantify and reduce state uncertainty. By combining deterministic and possibilistic formulations, along with temporal and spatial filtering, the approach achieves contact-point estimation and localization with errors under $7\,\mathrm{mm}$ in both simulation and physical experiments; obstacle profiles can be reconstructed from contact histories. The results demonstrate that whisker-based sensing can complement vision-based navigation in cluttered or low-visibility settings and lay the groundwork for extending to 3D spaces and multimodal fusion in future work.

Abstract

Whisker-like touch sensors offer unique advantages for short-range perception in environments where visual and long-range sensing are unreliable, such as confined, cluttered, or low-visibility settings. This paper presents a framework for estimating contact points and robot localization in a known planar environment using a single whisker sensor. We develop a family of virtual sensor models. Each model maps robot configurations to sensor observations and enables structured reasoning through the concept of preimages - the set of robot states consistent with a given observation. The notion of virtual sensor models serves as an abstraction to reason about state uncertainty without dependence on physical implementation. By combining sensor observations with a motion model, we estimate the contact point. Iterative estimation then enables reconstruction of obstacle boundaries. Furthermore, intersecting states inferred from current observations with forward-projected states from previous steps allow accurate robot localization without relying on vision or external systems. The framework supports both deterministic and possibilistic formulations and is validated through simulation and physical experiments using a low-cost, 3D printed, Hall-effect-based whisker sensor. Results demonstrate accurate contact estimation and localization with errors under 7 mm, demonstrating the potential of whisker-based sensing as a lightweight, adaptable complement to vision-based navigation.

Mobile Robot Localization Using a Novel Whisker-Like Sensor

TL;DR

This work addresses robust localization in confined environments by leveraging a single whisker-style tactile sensor. It introduces a virtual-sensor framework with multiple sensor models to map augmented robot states, including a beam-shape parameter, to observations, and uses preimages to quantify and reduce state uncertainty. By combining deterministic and possibilistic formulations, along with temporal and spatial filtering, the approach achieves contact-point estimation and localization with errors under in both simulation and physical experiments; obstacle profiles can be reconstructed from contact histories. The results demonstrate that whisker-based sensing can complement vision-based navigation in cluttered or low-visibility settings and lay the groundwork for extending to 3D spaces and multimodal fusion in future work.

Abstract

Whisker-like touch sensors offer unique advantages for short-range perception in environments where visual and long-range sensing are unreliable, such as confined, cluttered, or low-visibility settings. This paper presents a framework for estimating contact points and robot localization in a known planar environment using a single whisker sensor. We develop a family of virtual sensor models. Each model maps robot configurations to sensor observations and enables structured reasoning through the concept of preimages - the set of robot states consistent with a given observation. The notion of virtual sensor models serves as an abstraction to reason about state uncertainty without dependence on physical implementation. By combining sensor observations with a motion model, we estimate the contact point. Iterative estimation then enables reconstruction of obstacle boundaries. Furthermore, intersecting states inferred from current observations with forward-projected states from previous steps allow accurate robot localization without relying on vision or external systems. The framework supports both deterministic and possibilistic formulations and is validated through simulation and physical experiments using a low-cost, 3D printed, Hall-effect-based whisker sensor. Results demonstrate accurate contact estimation and localization with errors under 7 mm, demonstrating the potential of whisker-based sensing as a lightweight, adaptable complement to vision-based navigation.
Paper Structure (33 sections, 2 theorems, 29 equations, 15 figures, 2 tables)

This paper contains 33 sections, 2 theorems, 29 equations, 15 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. Background and Related Work
  3. Functional Overview of Whisker Sensors
  4. Applications of Whisker Sensors
  5. Mathematical Modeling of Whisker Sensors
  6. Modeling Whisker Deformation
  7. Contact Estimation Techniques
  8. Whisker Mechanics and Contact Inference
  9. Brief Review of Beam Theory
  10. Contact Point Estimation via Bending Moment
  11. Virtual Whisker Sensor Models and Preimages
  12. Virtual Sensor Models
  13. Preimages and Uncertainty
  14. Preimages of a Load Sensor
  15. Preimages of a Contact Strip Sensor
  16. ...and 18 more sections

Key Result

Proposition 1

Let $\mathcal{P} \subset \mathbb{R}^2$ denote the set of contact points inferred from a virtual whisker sensor model. Suppose $x(s)$ and $y(s)$ are continuous functions representing the horizontal and vertical coordinates of the beam shape, parameterized by arc-length $s \in [0, L]$. Then the set is compact and connected; that is, $\mathcal{P}$ is bounded and connected.

Figures (15)

  • Figure 1: Generic representation: (a) A typical physical whisker sensor similar to a cantilever beam. (b) Large-angle deflection of the cantilever beam.
  • Figure 2: Reference frames for the robot $T_{rw}$ and whisker sensor in world reference frame: (a) Whisker sensor (blue) mounted on a robot and making a contact with the environment. (b) A commercial contact strip sensor available from TekScanTM can act like a whisker sensor.
  • Figure 3: Possible contact locations and corresponding whisker sensor shapes. The green curve represents the set of possible contact locations, while the red curves indicate the potential whisker shapes for each contact point: (a) Load sensor, (b) Contact strip sensor.
  • Figure 4: The environment where robot resides and corresponding preimage of $z\in Z$ where $z=F$: (a) A map that features straight, concave, convex, and symmetric boundary sections. The shaded region represents the area where the robot can be located under the assumption that the sensor and robot reference frame are coincident. (b) A single observation reduces the full state-space to the shaded region as the direction-dependent distance determines the width of the strip (shaded region). A particular distance in the set of distances is associated with an orientation of the robot.
  • Figure 5: Set of possible contact points (green) where the whisker sensor may interact with the obstacle. Corresponding flexible beam shapes for a discrete set of sensor measurements (red): (a) End-slope sensor. (b) Base bending moment sensor in small-angle deflection case.
  • ...and 10 more figures

Theorems & Definitions (4)

  • Proposition 1
  • proof
  • Proposition 2
  • proof