6.8. Problems

  1. Write a Python function to compute wheel angles in the Ackerman system given the desired vehicle turn angle and frame parameters. This the function should have arguments (theta, l1, l2) and function should return the two wheel angles (theta_l, theta_r).

  2. What are the motion equations for the Ackerman drive? [Meaning forward and angular velocity as a function of wheel speed.] Assume wheel radius is \(r\).

  3. A dual Ackerman drive would steer both front and rear wheels using an Ackerman steering approach. What would the pros and cons for this system compared to a single Ackerman drive?

  4. Assume that you have a rectangular Mechanum robot with \(L_1 = 0.30\)m, \(L_2 = 0.20\)m and \(r=0.08\)m. Find the path of the robot for the given wheel rotations: \(\dot{\phi}_1 = 0.75*\cos(t/3.0)\), \(\dot{\phi}_2 = 1.5*cos(t/3.0)\), \(\dot{\phi}_3 = -1.0\), \(\dot{\phi}_4 = 0.5\). Start with \(x, y, \theta = 0\) and set \(t=0\), \(\Delta t = 0.05\). Run the simulation for 200 iterations (or for 10 seconds). Keeping the x and y locations in an array is an easy way to generate a plot of the robot’s path. If x, y are arrays of x-y locations then try

    import pylab as plt
    plt.plot(x,y,'b.')
    plt.show()
    

    Showing the orientation takes a bit more work. Matplotlib provides a vector plotting method. You need to hand it the location of the vector and the vector to be plotted, \((x,y,u,v)\), where \((x,y)\) s the vector location and \((u,v)\) are the x and y components of the vector. You can extract those from \(\theta\) using \(u = s*\cos(\theta)\) and \(v = s*\sin(\theta)\) where \(s\) is a scale factor (to give a good length for the image, e.g. 0.075). The vector plot commands are then

    plt.quiver(u,v,c,s,scale=1.25,units='xy',color='g')
    plt.savefig('mecanumpath.pdf')
    plt.show()
    
  5. Real motion and measurement involves error and this problem will introduce the concepts. Assume that you have a differential drive robot with wheels that are 20cm in radius and L is 12cm. Using the differential drive code (forward kinematics) from the text, develop code to simulate the robot motion when the wheel velocities are \(\dot{\phi}_1 = 0.25t^2\), \(\dot{\phi}_2 = 0.5t\). The starting location is [0,0] with \(\theta = 0\).

    1. Plot the path of the robot on \(0\leq t \leq 5\). It should end up somewhere near [50,60].

    2. Assume that you have Gaussian noise added to the omegas each time you evaluate the velocity (each time step). Test with \(\mu = 0\) and \(\sigma = 0.3\). Write the final location (x,y) to a file and repeat for 100 simulations. Hint:

      mu, sigma = 0.0, 0.3
      xerr = np.random.normal(mu,sigma, NumP)
      yerr = np.random.normal(mu,sigma, NumP)
      
    3. Generate a plot that includes the noise free robot path and the final locations for the simulations with noise. Hint:

      import numpy as np
      import pylab as plt
      ...
      plt.plot(xpath,ypath, 'b-', x,y, 'r.')
      plt.xlim(-10, 90)
      plt.ylim(-20, 80)
      plt.show()
      
    4. Find the location means and 2x2 covariance matrix for this data set, and compute the eigenvalues and eigenvectors of the matrix. Find the ellipse that these generate. [The major and minor axes directions are given by the eigenvectors. Show the point cloud of final locations and the ellipse in a graphic (plot the data and the ellipse). Hint:

      from scipy import linalg
      from matplotlib.patches import Ellipse
      s = 2.447651936039926
      #  assume final locations are in x & y
      mat = np.array([x,y])
      #  find covariance matrix
      cmat = np.cov(mat)
      # compute eigenvals and eigenvects of covariance
      eval, evec = linalg.eigh(cmat)
      r1 = 2*s*sqrt(evals[0])
      r2 = 2*s*sqrt(evals[1])
      #  find ellipse rotation angle
      angle = 180*atan2(evec[0,1],evec[0,0])/np.pi
      # create ellipse
      ell = Ellipse((np.mean(x),np.mean(y)),r1,r2,angle)
      #  make the ellipse subplot
      a = plt.subplot(111, aspect='equal')
      ell.set_alpha(0.1)    #  make the ellipse lighter
      a.add_artist(ell)   #  add this to the plot
      
  6. Describe the different styles of Swedish wheel.

  7. Find the analytic wheel velocities and initial pose for a Mecanum robot tasked to follow (\(r=3\), \(L_1 = 10\), \(L_2=10\) all in cm) the given paths (path units in m). Plot the paths and compare to the actual functions to verify.

    1. \(y=(3/2)x + 5/2\)
    2. \(y = x^{2/3}\)
  8. What are the wheel velocity formulas for a four wheel Mechanum robot, (\(r=3\), \(L_1 = 10\), \(L_2=10\) all in cm) which drives in the circular path \((x-3)^2/16 + (y-2)^2/9 = 1\) and always faces the center of the circle.

  9. In Veranda, drive a Mecanum robot along a square with corners (0,0), (10,0), (10,10), (0,10), \(L_1 = 0.30\), \(L_2 = 0.20\) and \(r=0.08\). You should stop and “turn” at a corner, but keep the robot faced in the x-axis direction. Drive the edges at unit speed. Use a video screen capture program to record the results.

  10. In Veranda, drive the Mecanum robot in an infinity (\(\infty\)) shape. Use a video screen capture program to record the results.