Exemple #1
0
  def get_ground_direction(self):
    ''' 
    In future classes, this function can be altered to return a preferred 
    direction,  but currently it only returns a random feasable direction if no
    direction is assigned for the robot (self.ground_direction)
    '''
    def random_direction():
      '''
      Returns a random, new location (direction)
      '''
      # obtain a random direction
      direction = (random.uniform(-1 * self.step, self.step), random.uniform(
        -1 * self.step, self.step), 0)

      # The they can't all be zero!
      if helpers.compare(helpers.length(direction),0):
        return random_direction()
      else:
        step = helpers.scale(self.step,helpers.make_unit(direction))
        predicted_location = helpers.sum_vectors(step, self.location)

        # Check the location
        if helpers.check_location(predicted_location):
          return direction
        else:
          return random_direction()

    # If we have a currently set direction, check to see if we will go out of 
    # bounds.
    if self.ground_direction != None:
      step = helpers.scale(self.step,helpers.make_unit(self.ground_direction))
      predicted_location = helpers.sum_vectors(step, self.location)

      # We are going out of bounds, so set the direction to none and call 
      # yourself again (to find a new location)
      if not helpers.check_location(predicted_location):
        self.ground_direction = None
        return self.get_ground_direction()

      # Here, we return the right direction
      else:
        assert self.ground_direction != None
        return self.ground_direction

    # We don't have a direction, so pick a random one (it is checked when we 
    # pick it)
    else:
      self.ground_direction = random_direction()
      return self.ground_direction
Exemple #2
0
    def random_direction():
      '''
      Returns a random, new location (direction)
      '''
      # obtain a random direction
      direction = (random.uniform(-1 * self.step, self.step), random.uniform(
        -1 * self.step, self.step), 0)

      # The they can't all be zero!
      if helpers.compare(helpers.length(direction),0):
        return random_direction()
      else:
        step = helpers.scale(self.step,helpers.make_unit(direction))
        predicted_location = helpers.sum_vectors(step, self.location)

        # Check the location
        if helpers.check_location(predicted_location):
          return direction
        else:
          return random_direction()