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A novel metric for detecting quadrotor loss-of-control

Jasper van Beers, Prashant Solanki, Coen de Visser

TL;DR

A novel metric, based on actuator capabilities, is introduced to detect LOC in quadrotors and is able to detect LOC induced by actuator faults without explicit knowledge of the occurrence and nature of the failure.

Abstract

Unmanned aerial vehicles (UAVs) are becoming an integral part of both industry and society. In particular, the quadrotor is now invaluable across a plethora of fields and recent developments, such as the inclusion of aerial manipulators, only extends their versatility. As UAVs become more widespread, preventing loss-of-control (LOC) is an ever growing concern. Unfortunately, LOC is not clearly defined for quadrotors, or indeed, many other autonomous systems. Moreover, any existing definitions are often incomplete and restrictive. A novel metric, based on actuator capabilities, is introduced to detect LOC in quadrotors. The potential of this metric for LOC detection is demonstrated through both simulated and real quadrotor flight data. It is able to detect LOC induced by actuator faults without explicit knowledge of the occurrence and nature of the failure. The proposed metric is also sensitive enough to detect LOC in more nuanced cases, where the quadrotor remains undamaged but nevertheless losses control through an aggressive yawing manoeuvre. As the metric depends only on system and actuator models, it is sufficiently general to be applied to other systems.

A novel metric for detecting quadrotor loss-of-control

TL;DR

A novel metric, based on actuator capabilities, is introduced to detect LOC in quadrotors and is able to detect LOC induced by actuator faults without explicit knowledge of the occurrence and nature of the failure.

Abstract

Unmanned aerial vehicles (UAVs) are becoming an integral part of both industry and society. In particular, the quadrotor is now invaluable across a plethora of fields and recent developments, such as the inclusion of aerial manipulators, only extends their versatility. As UAVs become more widespread, preventing loss-of-control (LOC) is an ever growing concern. Unfortunately, LOC is not clearly defined for quadrotors, or indeed, many other autonomous systems. Moreover, any existing definitions are often incomplete and restrictive. A novel metric, based on actuator capabilities, is introduced to detect LOC in quadrotors. The potential of this metric for LOC detection is demonstrated through both simulated and real quadrotor flight data. It is able to detect LOC induced by actuator faults without explicit knowledge of the occurrence and nature of the failure. The proposed metric is also sensitive enough to detect LOC in more nuanced cases, where the quadrotor remains undamaged but nevertheless losses control through an aggressive yawing manoeuvre. As the metric depends only on system and actuator models, it is sufficiently general to be applied to other systems.
Paper Structure (12 sections, 15 equations, 5 figures, 2 tables)

This paper contains 12 sections, 15 equations, 5 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. Motivation
  3. Related Work
  4. Contributions
  5. Organization
  6. QUADROTOR MODEL
  7. DERIVATION OF FEASIBLY CONTROLLABLE METRIC
  8. RESULTS: SIMULATION AND FLIGHT DATA
  9. Simulation Overview
  10. Loss-of-control Detection in Simulation
  11. Quadrotor Loss-of-control Flight Data Overview
  12. Loss-of-control Detection on Real Flight Data

Figures (5)

  • Figure 1: Position tracking trajectory of the nominal quadrotor. Also shown are the standard controllability metric, $rank(C) = n$, and those proposed in this paper, $FCM[k]$ and $FCMW$.
  • Figure 2: Position tracking trajectory of the quadrotor with the complete failure of a single rotor at $t=5.2$s. Also shown are the standard controllability metric, $rank(C) = n$, and those proposed in this paper, $FCM[k]$ and $FCMW$.
  • Figure 3: Performance of the Windowed Feasibly Controllable Metric ($FCMW$), applied to the CineGo loss-of-control flight data set RNN_LOC_Altena, for various combinations of majority voting window, $MVW$, duration (in seconds) and low-pass filter cut-off frequency, $CF$ (in Hertz).
  • Figure 4: Comparison of the quadrotor loss-of-control (LOC) detection capabilities of the Windowed Feasibly Controllable Metric (FCMW) versus the attitude-based definition (Att) of RNN_LOC_Altena for yaw-induced LOC flights of the CineGo quadrotor. Shown are the detection distributions of each definition. Plotted in black is an example LOC detection of both definitions for one of the LOC flights. The hyper-parameters for the FCMW are: voting window $MVW = 1.0$s and cut-off frequency $CF = 30$Hz.
  • Figure 5: Variation in the time to loss-of-control detection of the Windowed Feasibly Controllable Metric ($FCMW$) as a function of the majority voting window ($MVW$) duration, in seconds. Also shown, in red, is the trivial case where the time-to-LOC detection = $MVW$. The two lowest $MVW$ (i.e. 0.02s and 0.05s) are not shown due to large variances and outliers.