Trajectory Optimization

MIT 6.832: Underactuated Robotics

Spring 2022, Lecture 11

Follow live at https://slides.com/d/jgUEwKM/live

(or later at https://slides.com/russtedrake/spring22-lec11)

Image credit: Boston Dynamics

https://en.wikipedia.org/wiki/Stall_(fluid_dynamics)

Dimensional Analysis

  • Bird or plane...
    • with mass \(m\), wing area \(S\), operating in a fluid with density \(\rho\)
    • which requires a distance \(x\) to slow from \(V_0\) to \(V_f\)
  • Distance-averaged drag coefficient: 

 

\langle C_D \rangle = \frac{2m}{\rho S x} \ln \left( \frac{V_0}{V_f}\right)
Vehicle Average C_D
Boeing 747 0.16
X-31 0.3
Cornell Perching Plane 0.25
Common pigeon 10
  • A few (very rough) reference points:

Experiment Design

  • Glider (no propellor)
  • Flat plate wings
  • Dihedral (passive roll stability)
  • Offboard sensing and control

System Identification

  • Nonlinear rigid-body vehicle model
  • Linear actuator model (+ saturations, delay)
  • Real flight data (no wind tunnel)

Lift Coefficient

Drag Coefficient

Dynamic Model

  • Planar dynamics

 

  • Aerodynamics fit from data

 

  • State: \( {\bf x} = [x, y, \theta, \phi, \dot{x}, \dot{y}, \dot\theta] \)

 

  • Control: \( {\bf u} = \dot\phi \)
  • Enters motion capture @ 6m/s
  • Perch in < 3.5m away
  • Entire trajectory < 1s
Vehicle Average C_D
Boeing 747 0.16
X-31 0.3
Cornell Perching Plane 0.25
Common pigeon 10
Our glider 1.1
Cobra maneuver (Mig) 0.9

Lecture 11: Trajectory Optimization

By russtedrake

Lecture 11: Trajectory Optimization

MIT Underactuated Robotics Spring 2021 http://underactuated.csail.mit.edu

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