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Improving autonomous orchard vehicle trajectory tracking performance - - PowerPoint PPT Presentation

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Improving autonomous orchard vehicle trajectory tracking performance via slippage compensation Dr. Gokhan BAYAR Mechanical Engineering Department of Bulent Ecevit University Zonguldak, Turkey This study was conducted under the Supervision of

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Improving autonomous orchard vehicle trajectory tracking performance via slippage compensation

  • Dr. Gokhan BAYAR

Mechanical Engineering Department of Bulent Ecevit University Zonguldak, Turkey

This study was conducted under the Supervision of Dr. Marcel Bergerman in the Field Robotics Center of Robotics Institute of Carnegie Mellon University, Pittsburgh, PA, USA.

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Development of a slippage estimation procedure and performing a desired trajectory tracking control.

Objective of the Research

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a single set of controller parameters or a unique equation of motion to guarantee a desired performance and accuracy

Due to changing the characteristics Of wheel-ground interaction

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the simple assumptions which are generally used in the mobile robot / autonomous vehicle applications:

  • ideal transmission
  • ideal rolling
  • no slippage
  • no lost of traction control
  • no external wheel forces
  • no surface change behavior
  • no disturbance, etc.

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Surface Information <Mobile Robot> Unmanned Ground Vehicle

Desired task [f(x,y,t)] [f(x,y)]

Vehicle Model Controller Forward Velocity Steering Angle

x,y,θ,V,δ

Wheel-Ground Interaction

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Trajectory Tracking Control of an Autonomous Vehicle

f(X,Y,t)desired f(X,Y,t)actual Vehicledesired(t) XError(t)= |XDesired(t) - XActual(t)| YError(t)= |YDesired(t) - YActual(t)| θError(t)= |θDesired(t) - θActual(t)|

X Y

Vehicleactual(t)

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Dynamic approaches Kinematic/Car‐like robot approach Point mass model Dubins curves

Desired Trajectory Generator

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Car‐like robot model

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Desired trajectory tracking controller

Σ

Xdesired XPS

Σ

YPS Ydesired

Controller

Σ

θPS θdesired

Vehicle

Vc Xe Ye θe

+ + +

  • V

V x, y

Φc Φ Φ

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Lyapunov Functions

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trees

Working Environment of an Orchard Robot Vehicle

w1 w2

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  • 40
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20 40 60 80 100 120

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20 40 60 80 100 120

Reference Trajectory

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Turning Geometry

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Experimental Orchard

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  • 1. Experiments to test the behaviour
  • f the proposed model

Slippage information is not taken into consideration. RTK-GPS is used for position feedback.

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4 km autonomous drive achieved in the orchard

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Desired and actual steering angles for 4 km autonomous drive

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Video

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  • 2. Experiments to test the behaviour
  • f the proposed model.

Slippage information is not taken into consideration. Row Detection System (via Laser Scanning RangeFinder) is used.

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Experimental results obtained in the first row of the orchard

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Experimental results obtained in the first row. Width = 4.44 m, Length = 52.95 m. (a) Steering angles, (b) Lateral errors

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Video ‐ First Row Width = 4.44 m, Length = 52.95 m 0.5 m/s Forward Velocity

Forward Camera Front Camera

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  • 3. Experiments to test the behaviour
  • f the proposed model.

Slippage information is taken into consideration. RTK-GPS is used for position feedback.

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RTK-GPS

Odometer Steering System

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Car Like Robot Model Without Slippage Car Like Robot Model With Slippage

It is assumed that

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Slippage Experiments on Snow

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5 10 15 20

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2 4 6 X-Direction [m] Y-Direction [m] Desired Real 5 10 15 20

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2 4 6 X-Direction [m] Y-Direction [m] Desired Real

Reference Trajectory Tracking Control on Snow

Vehicle Control Without Slip Estimation Vehicle Control With Slip Estimation

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20 40 60 80 0.2 0.4 0.6 0.8 1 1.2 1.4 Time [s] Forward Speed [m/s] w/o Estimation w/ Estimation 20 40 60 80

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10 20 30 Time [s] Steering Angle [deg] w/o Estimation w/ Estimation

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  • 4. Orchard Experiments.

Slippage information is taken into consideration. Row Detection System (via Laser Scanning RangeFinder) is used.

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E1 results obtained by using RTK GPS feedback without using slippage estimation. E2 results obtained by using the slippage estimation procedure that uses RTK GPS feedback. E3 results obtained by using feedback information coming from dead reckoning algorithm. No slippage estimation procedure is adapted into the system model. E4 results obtained by using the slippage estimation process that uses the dead reckoning feedback information.

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Video ‐ Turning control without slippage estimation

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Video ‐ Turning control with slippage estimation

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1Field Robotics Center, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA 2Mechanical Engineering Department, Middle East Technical University, Ankara, Turkey

Special thanks to the co-authors of the paper: Gokhan Bayar*, Marcel Bergerman1, E. ilhan Konukseven2, A. Bugra Koku2, “Improving the trajectory tracking performance of autonomous orchard vehicles using wheel slip compensation”, Biosystems Engineering, vol. 146, pp. 149- 164, 2016.

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Thanks for your attention