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System Identification and Adaptive Input Estimation on the Jaiabot Micro Autonomous Underwater Vehicle

Ioannis Faros, Herbert G. Tanner

TL;DR

This work addresses state estimation for a micro-AUV (Jaiabot) when actuation inputs are unknown. It develops separate linear surge and heading models from field data and couples them with an adaptive input estimation (AIE) framework based on RCIE to simultaneously estimate inputs and states, outperforming a standard Kalman filter under unknown inputs. Validation with Lake Allure data demonstrates convergence of the input-estimation coefficients and improved trajectory reconstruction, highlighting AIE's robustness to process and input uncertainties. The approach offers a practical pathway for real-time pose estimation of small autonomous surface vehicles used in environmental monitoring.

Abstract

This paper reports an attempt to model the system dynamics and estimate both the unknown internal control input and the state of a recently developed marine autonomous vehicle, the Jaiabot. Although the Jaiabot has shown promise in many applications, process and sensor noise necessitates state estimation and noise filtering. In this work, we present the first surge and heading linear dynamical model for Jaiabots derived from real data collected during field testing. An adaptive input estimation algorithm is implemented to accurately estimate the control input and hence the state. For validation, this approach is compared to the classical Kalman filter, highlighting its advantages in handling unknown control inputs.

System Identification and Adaptive Input Estimation on the Jaiabot Micro Autonomous Underwater Vehicle

TL;DR

This work addresses state estimation for a micro-AUV (Jaiabot) when actuation inputs are unknown. It develops separate linear surge and heading models from field data and couples them with an adaptive input estimation (AIE) framework based on RCIE to simultaneously estimate inputs and states, outperforming a standard Kalman filter under unknown inputs. Validation with Lake Allure data demonstrates convergence of the input-estimation coefficients and improved trajectory reconstruction, highlighting AIE's robustness to process and input uncertainties. The approach offers a practical pathway for real-time pose estimation of small autonomous surface vehicles used in environmental monitoring.

Abstract

This paper reports an attempt to model the system dynamics and estimate both the unknown internal control input and the state of a recently developed marine autonomous vehicle, the Jaiabot. Although the Jaiabot has shown promise in many applications, process and sensor noise necessitates state estimation and noise filtering. In this work, we present the first surge and heading linear dynamical model for Jaiabots derived from real data collected during field testing. An adaptive input estimation algorithm is implemented to accurately estimate the control input and hence the state. For validation, this approach is compared to the classical Kalman filter, highlighting its advantages in handling unknown control inputs.

Paper Structure

This paper contains 15 sections, 22 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Technical Approach
  3. System Identification
  4. Surge dynamics
  5. Heading dynamics
  6. Discrete-time Vehicle Dynamics
  7. Heading
  8. Surge
  9. Adaptive Input Estimation
  10. Input estimation subsystem
  11. Validation
  12. Results for Surge Dynamics
  13. Results for heading dynamics
  14. Trajectory reconstruction based on estimates
  15. Conclusion

Figures (8)

  • Figure 1: The first generation of the Jaiabot AUV.
  • Figure 2: Maneuvering experiments for motion dynamics system identification. (\ref{['figure:bootcamp-surge']}) Experimental data for surge dynamics were obtained from indoor tests in a long water tank. (\ref{['figure:Allure-lateral']}) Data for yaw dynamics were collected during outdoor tests in lake Allure, PA
  • Figure 3: Geometric relation between surge model output and sensor measurements.
  • Figure 4: Adaptive input estimation results for surge.
  • Figure 5: Comparison of the estimated state for surge against experimental data, for the Kalman filter and AIE.
  • ...and 3 more figures

Theorems & Definitions (3)

  • Remark 1
  • Remark 2
  • Remark 3