Continuum Robot Localization using Distributed Time-of-Flight Sensors

2026-02-06

AI Summary

This work addresses the challenge of accurate localization and mapping for continuum robots in unstructured environments, where high-resolution sensing is often unavailable and the robot’s highly deformable structure complicates state estimation. To overcome these limitations, the authors propose a novel approach that fuses distributed low-resolution time-of-flight (ToF) sensors with prior knowledge of the robot’s shape—a combination not previously explored for continuum robot state estimation. By integrating kinematic modeling with multi-source sensor fusion, the method effectively mitigates performance degradation associated with individual sensor limitations. Evaluated on a 53 cm continuum robot, the system achieves an average position error of 2.5 cm and orientation error of 7.2°, demonstrating robustness and repeatability across diverse simulated and real-world scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Localization and mapping of an environment are crucial tasks for any robot operating in unstructured environments. Time-of-flight (ToF) sensors (e.g.,~lidar) have proven useful in mobile robotics, where high-resolution sensors can be used for simultaneous localization and mapping. In soft and continuum robotics, however, these high-resolution sensors are too large for practical use. This, combined with the deformable nature of such robots, has resulted in continuum robot (CR) localization and mapping in unstructured environments being a largely untouched area. In this work, we present a localization technique for CRs that relies on small, low-resolution ToF sensors distributed along the length of the robot. By fusing measurement information with a robot shape prior, we show that accurate localization is possible despite each sensor experiencing frequent degenerate scenarios. We achieve an average localization error of 2.5cm in position and 7.2{\deg} in rotation across all experimental conditions with a 53cm long robot. We demonstrate that the results are repeated across multiple environments, in both simulation and real-world experiments, and study robustness in the estimation to deviations in the prior map.

Problem

• Continuum Robot
• Localization
• Time-of-Flight Sensors
• Unstructured Environments
• Mapping

Innovation

• Continuum Robot
• Time-of-Flight Sensors
• Distributed Sensing
• Localization
• Shape Prior