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FLAGRED -- Fuzzy Logic-based Algorithm Generalizing Risk Estimation for Drones

Samuel Hovington, Louis Petit, Sophie Stratford, Philippe Hamelin, Alexis Lussier-Desbiens, Francois Ferland

TL;DR

The paper tackles real-time risk estimation for external disturbances affecting multirotor drones without relying on extra sensors, precise models, or large datasets. It introduces a modular framework comprising a fuzzy-logic risk estimator, a rule-learning module trained from simulations, and an elevated risk accumulator to forecast near-term risk, all grounded in motor command data and a motor-margin concept. Real-world tests on two distinct platforms demonstrate the approach can detect elevated risk more quickly and reliably than the ArduCopter wind estimator, including rapid gust events, highlighting its generality and practical applicability. The work suggests a viable path toward onboard, autonomous risk-aware flight control and operator awareness, with future work extending inputs to battery, sensor faults, and communication degradation.

Abstract

Accurately estimating risk in real-time is essential for ensuring the safety and efficiency of many applications involving autonomous robot systems. This paper presents a novel, generalizable algorithm for the real-time estimation of risks created by external disturbances on multirotors. Unlike conventional approaches, our method requires no additional sensors, accurate drone models, or large datasets. It employs motor command data in a fuzzy logic system, overcoming barriers to real-world implementation. Inherently adaptable, it utilizes fundamental drone characteristics, making it applicable to diverse drone models. The efficiency of the algorithm has been confirmed through comprehensive real-world testing on various platforms. It proficiently discerned between high and low-risk scenarios resulting from diverse wind disturbances and varying thrust-to-weight ratios. The algorithm surpassed the widely-recognized ArduCopter wind estimation algorithm in performance and demonstrated its capability to promptly detect brief gusts.

FLAGRED -- Fuzzy Logic-based Algorithm Generalizing Risk Estimation for Drones

TL;DR

The paper tackles real-time risk estimation for external disturbances affecting multirotor drones without relying on extra sensors, precise models, or large datasets. It introduces a modular framework comprising a fuzzy-logic risk estimator, a rule-learning module trained from simulations, and an elevated risk accumulator to forecast near-term risk, all grounded in motor command data and a motor-margin concept. Real-world tests on two distinct platforms demonstrate the approach can detect elevated risk more quickly and reliably than the ArduCopter wind estimator, including rapid gust events, highlighting its generality and practical applicability. The work suggests a viable path toward onboard, autonomous risk-aware flight control and operator awareness, with future work extending inputs to battery, sensor faults, and communication degradation.

Abstract

Accurately estimating risk in real-time is essential for ensuring the safety and efficiency of many applications involving autonomous robot systems. This paper presents a novel, generalizable algorithm for the real-time estimation of risks created by external disturbances on multirotors. Unlike conventional approaches, our method requires no additional sensors, accurate drone models, or large datasets. It employs motor command data in a fuzzy logic system, overcoming barriers to real-world implementation. Inherently adaptable, it utilizes fundamental drone characteristics, making it applicable to diverse drone models. The efficiency of the algorithm has been confirmed through comprehensive real-world testing on various platforms. It proficiently discerned between high and low-risk scenarios resulting from diverse wind disturbances and varying thrust-to-weight ratios. The algorithm surpassed the widely-recognized ArduCopter wind estimation algorithm in performance and demonstrated its capability to promptly detect brief gusts.
Paper Structure (9 sections, 8 equations, 9 figures, 2 tables)

This paper contains 9 sections, 8 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Fuzzy logic
  4. Methodology
  5. Fuzzy-logic estimator
  6. Inference rules learning process
  7. Elevated risk accumulator
  8. Experimental results
  9. Conclusion

Figures (9)

  • Figure 1: Fuzzy logic overall risk estimator
  • Figure 2: Membership functions used in the Fuzzy logic risk estimator
  • Figure 3: Simulation results to highlight the relation between motor margin and attitude error.
  • Figure 4: Top-down view of the decision map with highlighted extrapolation zone.
  • Figure 5: NADILE (Upper) and F450 (Lower)
  • ...and 4 more figures