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Design of a Health Monitoring System for a Planetary Exploration Rover

Sarah Swinton, Euan McGookin, Douglas Thomson

TL;DR

This work addresses safe autonomous operation of planetary exploration rovers by coupling health monitoring with adaptive-threshold fault detection. It defines four rover vitals—Forward Acceleration, Rate of Change of Distance to Target, Rate of Change of Heading, and Rate of Change of Commanded Voltage—and derives a rover health metric from their information entropy. A model-based fault-detection framework compares the rover against a non-faulty observer and uses health-informed adaptive thresholds to detect faults in sensors and actuators, validated via MATLAB simulations on straight-line and serpentine paths with gyroscope offset and motor failure faults. The approach reduces false positives and enables autonomous fault handling, contributing to extended mission lifespans and more robust planetary rovers.

Abstract

It is generally considered that a trustworthy autonomous planetary exploration rover must be able to operate safely and effectively within its environment. Central to trustworthy operation is the ability for the rover to recognise and diagnose abnormal behaviours during its operation. Failure to diagnose faulty behaviour could lead to degraded performance or an unplanned halt in operation. This work investigates a health monitoring method that can be used to improve the capabilities of a fault detection system for a planetary exploration rover. A suite of four metrics, named 'rover vitals', are evaluated as indicators of degradation in the rover's performance. These vitals are combined to give an overall estimate of the rover's 'health'. By comparing the behaviour of a faulty real system with a non-faulty observer, residuals are generated in terms of two high-level metrics: heading and velocity. Adaptive thresholds are applied to the residuals to enable the detection of faulty behaviour, where the adaptive thresholds are informed by the rover's perceived health. Simulation experiments carried out in MATLAB showed that the proposed health monitoring and fault detection methodology can detect high-risk faults in both the sensors and actuators of the rover.

Design of a Health Monitoring System for a Planetary Exploration Rover

TL;DR

This work addresses safe autonomous operation of planetary exploration rovers by coupling health monitoring with adaptive-threshold fault detection. It defines four rover vitals—Forward Acceleration, Rate of Change of Distance to Target, Rate of Change of Heading, and Rate of Change of Commanded Voltage—and derives a rover health metric from their information entropy. A model-based fault-detection framework compares the rover against a non-faulty observer and uses health-informed adaptive thresholds to detect faults in sensors and actuators, validated via MATLAB simulations on straight-line and serpentine paths with gyroscope offset and motor failure faults. The approach reduces false positives and enables autonomous fault handling, contributing to extended mission lifespans and more robust planetary rovers.

Abstract

It is generally considered that a trustworthy autonomous planetary exploration rover must be able to operate safely and effectively within its environment. Central to trustworthy operation is the ability for the rover to recognise and diagnose abnormal behaviours during its operation. Failure to diagnose faulty behaviour could lead to degraded performance or an unplanned halt in operation. This work investigates a health monitoring method that can be used to improve the capabilities of a fault detection system for a planetary exploration rover. A suite of four metrics, named 'rover vitals', are evaluated as indicators of degradation in the rover's performance. These vitals are combined to give an overall estimate of the rover's 'health'. By comparing the behaviour of a faulty real system with a non-faulty observer, residuals are generated in terms of two high-level metrics: heading and velocity. Adaptive thresholds are applied to the residuals to enable the detection of faulty behaviour, where the adaptive thresholds are informed by the rover's perceived health. Simulation experiments carried out in MATLAB showed that the proposed health monitoring and fault detection methodology can detect high-risk faults in both the sensors and actuators of the rover.
Paper Structure (21 sections, 10 equations, 5 figures)

This paper contains 21 sections, 10 equations, 5 figures.

Table of Contents

  1. Introduction
  2. System Modelling
  3. PER Model
  4. Guidance, Navigation and Control
  5. Fault Modelling
  6. Gyroscope Offset
  7. Motor Failure
  8. Rover Vitals
  9. Introduction
  10. Forward Acceleration
  11. Rate of Change of Distance to Target
  12. Rate of Change of Heading Angle
  13. Rate of Change of Commanded Voltage
  14. Rover Health
  15. Fault Detection
  16. ...and 6 more sections

Figures (5)

  • Figure 1: PER fault detection system architecture
  • Figure 2: Earth-fixed (XE, YE, ZE (blue)) and rover body-fixed axes (XB, YB, ZB (red)) for the modelled rover
  • Figure 3: Adaptive threshold block diagram
  • Figure 4: Rover path, health monitoring, and adaptive threshold generation for the straight line test scenario
  • Figure 5: Rover path, health monitoring, and adaptive threshold generation for the serpentine path test scenario