Hybrid low pass and de-trending filter for robust position estimation of quadcopters

Shatadal Mishra, Wenlong Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    In this paper, a hybrid low-pass and de-trending (HLPD) filtering technique is proposed to achieve robust position estimates using an optical flow based sensor which calculates velocity information at a rate of 400 Hz. In order to filter out the high-frequency oscillation in the velocity information, a standard low-pass filter is implemented. The low-pass filter successfully eliminates sudden jumps and missing data-points, which prevents unprecedented maneuvers and mid-air crashes. The integrated position estimate has the accumulated drift which occurs due to electrical signal and temperature fluctuations together with other environmental factors which affect the data acquisition from the optical flow sensor. A recursive linear least squares fit is performed for the drift model and de-trending is applied to the integrated position signal. The performance of the proposed estimator is validated by comparing with model-identification based weighted average (MI-WA) position estimator, which is commonly used in quadcopters for position estimation. Simulation and experimental flight tests are conducted and the results show that the flight performance of HLPD filter is better than the extensively used MI-WA position filter in hover and square pattern flight tests.

    Original languageEnglish (US)
    Title of host publicationMechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
    PublisherAmerican Society of Mechanical Engineers
    Pages98DUMMY
    Volume2
    ISBN (Electronic)9780791850701
    DOIs
    StatePublished - 2016
    EventASME 2016 Dynamic Systems and Control Conference, DSCC 2016 - Minneapolis, United States
    Duration: Oct 12 2016Oct 14 2016

    Other

    OtherASME 2016 Dynamic Systems and Control Conference, DSCC 2016
    CountryUnited States
    CityMinneapolis
    Period10/12/1610/14/16

    Fingerprint

    Optical flows
    Low pass filters
    Identification (control systems)
    Flight dynamics
    Sensors
    Data acquisition
    Air
    Temperature

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Industrial and Manufacturing Engineering
    • Mechanical Engineering

    Cite this

    Mishra, S., & Zhang, W. (2016). Hybrid low pass and de-trending filter for robust position estimation of quadcopters. In Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control (Vol. 2, pp. 98DUMMY). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2016-9921

    Hybrid low pass and de-trending filter for robust position estimation of quadcopters. / Mishra, Shatadal; Zhang, Wenlong.

    Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. Vol. 2 American Society of Mechanical Engineers, 2016. p. 98DUMMY.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Mishra, S & Zhang, W 2016, Hybrid low pass and de-trending filter for robust position estimation of quadcopters. in Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. vol. 2, American Society of Mechanical Engineers, pp. 98DUMMY, ASME 2016 Dynamic Systems and Control Conference, DSCC 2016, Minneapolis, United States, 10/12/16. https://doi.org/10.1115/DSCC2016-9921
    Mishra S, Zhang W. Hybrid low pass and de-trending filter for robust position estimation of quadcopters. In Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. Vol. 2. American Society of Mechanical Engineers. 2016. p. 98DUMMY https://doi.org/10.1115/DSCC2016-9921
    Mishra, Shatadal ; Zhang, Wenlong. / Hybrid low pass and de-trending filter for robust position estimation of quadcopters. Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control. Vol. 2 American Society of Mechanical Engineers, 2016. pp. 98DUMMY
    @inproceedings{57be444e13cd4667839e76daf550da6f,
    title = "Hybrid low pass and de-trending filter for robust position estimation of quadcopters",
    abstract = "In this paper, a hybrid low-pass and de-trending (HLPD) filtering technique is proposed to achieve robust position estimates using an optical flow based sensor which calculates velocity information at a rate of 400 Hz. In order to filter out the high-frequency oscillation in the velocity information, a standard low-pass filter is implemented. The low-pass filter successfully eliminates sudden jumps and missing data-points, which prevents unprecedented maneuvers and mid-air crashes. The integrated position estimate has the accumulated drift which occurs due to electrical signal and temperature fluctuations together with other environmental factors which affect the data acquisition from the optical flow sensor. A recursive linear least squares fit is performed for the drift model and de-trending is applied to the integrated position signal. The performance of the proposed estimator is validated by comparing with model-identification based weighted average (MI-WA) position estimator, which is commonly used in quadcopters for position estimation. Simulation and experimental flight tests are conducted and the results show that the flight performance of HLPD filter is better than the extensively used MI-WA position filter in hover and square pattern flight tests.",
    author = "Shatadal Mishra and Wenlong Zhang",
    year = "2016",
    doi = "10.1115/DSCC2016-9921",
    language = "English (US)",
    volume = "2",
    pages = "98DUMMY",
    booktitle = "Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control",
    publisher = "American Society of Mechanical Engineers",

    }

    TY - GEN

    T1 - Hybrid low pass and de-trending filter for robust position estimation of quadcopters

    AU - Mishra, Shatadal

    AU - Zhang, Wenlong

    PY - 2016

    Y1 - 2016

    N2 - In this paper, a hybrid low-pass and de-trending (HLPD) filtering technique is proposed to achieve robust position estimates using an optical flow based sensor which calculates velocity information at a rate of 400 Hz. In order to filter out the high-frequency oscillation in the velocity information, a standard low-pass filter is implemented. The low-pass filter successfully eliminates sudden jumps and missing data-points, which prevents unprecedented maneuvers and mid-air crashes. The integrated position estimate has the accumulated drift which occurs due to electrical signal and temperature fluctuations together with other environmental factors which affect the data acquisition from the optical flow sensor. A recursive linear least squares fit is performed for the drift model and de-trending is applied to the integrated position signal. The performance of the proposed estimator is validated by comparing with model-identification based weighted average (MI-WA) position estimator, which is commonly used in quadcopters for position estimation. Simulation and experimental flight tests are conducted and the results show that the flight performance of HLPD filter is better than the extensively used MI-WA position filter in hover and square pattern flight tests.

    AB - In this paper, a hybrid low-pass and de-trending (HLPD) filtering technique is proposed to achieve robust position estimates using an optical flow based sensor which calculates velocity information at a rate of 400 Hz. In order to filter out the high-frequency oscillation in the velocity information, a standard low-pass filter is implemented. The low-pass filter successfully eliminates sudden jumps and missing data-points, which prevents unprecedented maneuvers and mid-air crashes. The integrated position estimate has the accumulated drift which occurs due to electrical signal and temperature fluctuations together with other environmental factors which affect the data acquisition from the optical flow sensor. A recursive linear least squares fit is performed for the drift model and de-trending is applied to the integrated position signal. The performance of the proposed estimator is validated by comparing with model-identification based weighted average (MI-WA) position estimator, which is commonly used in quadcopters for position estimation. Simulation and experimental flight tests are conducted and the results show that the flight performance of HLPD filter is better than the extensively used MI-WA position filter in hover and square pattern flight tests.

    UR - http://www.scopus.com/inward/record.url?scp=85015659511&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85015659511&partnerID=8YFLogxK

    U2 - 10.1115/DSCC2016-9921

    DO - 10.1115/DSCC2016-9921

    M3 - Conference contribution

    AN - SCOPUS:85015659511

    VL - 2

    SP - 98DUMMY

    BT - Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control

    PB - American Society of Mechanical Engineers

    ER -