def get_separation_heading(agent_name, neighbours): # initialise the heading vector that will hold the separation heading separation_heading_vector = [0, 0, 0] heading_vector = [0, 0, 0] # get the number of neighbouring agents in the neighbours list [find the length of the list] number_of_neighbours = len(neighbours) # IF we have 1 or more agents in the neighbours list if number_of_neighbours > 0: # get the position of the agent called agent_name agent_pos = agent_name.get_agent_position() # FOR every neighbour in the neighbours list for neighbour in neighbours: # get the position of the neighbour neighbour_pos = neighbour.get_agent_position() # calculate the heading vector from the neighbours position to the position of our agent heading_vector = vectorMath.get_vector_between_points(neighbour_pos, agent_pos) # normalise the heading vector to give it a size of 1 [needed so averaging the vector will work as expected heading_vector = vectorMath.vector_normalise(heading_vector) # add this heading vector to the separation_heading_vector separation_heading_vector = vectorMath.vector_add(separation_heading_vector, heading_vector) # calculate the averaged separation_heading_vector separation_heading_vector[0] = separation_heading_vector[0] / number_of_neighbours separation_heading_vector[1] = separation_heading_vector[1] / number_of_neighbours separation_heading_vector[2] = separation_heading_vector[2] / number_of_neighbours # normalise the separation_heading_vector separation_heading_vector = vectorMath.vector_normalise(separation_heading_vector) return separation_heading_vector
def get_separation_heading(agent_name, neighbours):
# initialise the heading vector that will hold the separation heading
separation_heading_vector = [0, 0, 0]
heading_vector = [0, 0, 0]
# get the number of neighbouring agents in the neighbours list [find the length of the list]
number_of_neighbours = len(neighbours)
# IF we have 1 or more agents in the neighbours list
if number_of_neighbours > 0:
# get the position of the agent called agent_name
agent_pos = agent_name.get_agent_position()
# FOR every neighbour in the neighbours list
for neighbour in neighbours:
# get the position of the neighbour
neighbour_pos = neighbour.get_agent_position()
# calculate the heading vector from the neighbours position to the position of our agent
heading_vector = vectorMath.get_vector_between_points(
neighbour_pos, agent_pos)
# normalise the heading vector to give it a size of 1 [needed so averaging the vector will work as expected
heading_vector = vectorMath.vector_normalise(heading_vector)
# add this heading vector to the separation_heading_vector
separation_heading_vector = vectorMath.vector_add(
separation_heading_vector, heading_vector)
# calculate the averaged separation_heading_vector
separation_heading_vector[
0] = separation_heading_vector[0] / number_of_neighbours
separation_heading_vector[
1] = separation_heading_vector[1] / number_of_neighbours
separation_heading_vector[
2] = separation_heading_vector[2] / number_of_neighbours
# normalise the separation_heading_vector
separation_heading_vector = vectorMath.vector_normalise(
separation_heading_vector)
return separation_heading_vector
def get_cohesion_heading(agent_name, neighbours):
# initialise the heading vector that will hold the separation heading
cohesion_heading_vector = [0, 0, 0]
# get the number of neighbouring agents in the neighbours list [find the length of the list]
number_of_neighbours = len(neighbours)
# IF we have 1 or more agents in the neighbours list
if number_of_neighbours > 0:
# get the position of the agent called agent_name
agent_pos = agent_name.get_agent_position()
# initialise a a vector to hold the average of all the neighbours positions to [0, 0, 0]
neighbours_average_vector = [0, 0, 0]
#FOR every neighbour in the neighbours list
for neighbour in neighbours:
# get the neighbours position
neighbour_pos = neighbour.get_agent_position()
# add it to the vector to hold the average positions
neighbours_average_vector = vectorMath.vector_add(
neighbours_average_vector, neighbour_pos)
# calculate the averaged neighbours position
neighbours_average_vector[
0] = neighbours_average_vector[0] / number_of_neighbours
neighbours_average_vector[
1] = neighbours_average_vector[1] / number_of_neighbours
neighbours_average_vector[
2] = neighbours_average_vector[2] / number_of_neighbours
# calculate the cohesion_heading_vector from the position of the agent called agent_name to the averaged neighbours position
cohesion_heading_vector = vectorMath.get_vector_between_points(
agent_pos, neighbours_average_vector)
# normalise the cohesion_heading_vector
cohesion_heading_vector = vectorMath.vector_normalise(
cohesion_heading_vector)
return cohesion_heading_vector
def get_alignment_heading(neighbours):
# initialise the heading vector that will hold the alignment heading
alignment_heading_vector = [0, 0, 0]
neighbour_vector = [0, 0, 0]
# get the number of neighbouring agents in the neighbours list [find the length of the list]
number_of_neighbours = len(neighbours)
# IF we have 1 or more agents in the neighbours list
if number_of_neighbours > 0:
# FOR every neighbour in the neighbours list
for neighbour in neighbours:
# get the heading of the neighbour as a vector
neighbour_vector = neighbour.get_agent_heading_vector()
# add this heading to the alignment_heading_vector
alignment_heading_vector = vectorMath.vector_add(
alignment_heading_vector, neighbour_vector)
# calculate the averaged alignment_heading_vector
alignment_heading_vector[
0] = alignment_heading_vector[0] / number_of_neighbours
alignment_heading_vector[
1] = alignment_heading_vector[1] / number_of_neighbours
alignment_heading_vector[
2] = alignment_heading_vector[2] / number_of_neighbours
# normalise alignment vector
alignment_heading_vector = vectorMath.vector_normalise(
alignment_heading_vector)
return alignment_heading_vector
def get_cohesion_heading(agent_name, neighbours): # initialise the heading vector that will hold the separation heading cohesion_heading_vector = [0, 0, 0] # get the number of neighbouring agents in the neighbours list [find the length of the list] number_of_neighbours = len(neighbours) # IF we have 1 or more agents in the neighbours list if number_of_neighbours > 0: # get the position of the agent called agent_name agent_pos = agent_name.get_agent_position() # initialise a a vector to hold the average of all the neighbours positions to [0, 0, 0] neighbours_average_vector = [0, 0, 0] #FOR every neighbour in the neighbours list for neighbour in neighbours: # get the neighbours position neighbour_pos = neighbour.get_agent_position() # add it to the vector to hold the average positions neighbours_average_vector = vectorMath.vector_add(neighbours_average_vector, neighbour_pos) # calculate the averaged neighbours position neighbours_average_vector[0] = neighbours_average_vector[0] / number_of_neighbours neighbours_average_vector[1] = neighbours_average_vector[1] / number_of_neighbours neighbours_average_vector[2] = neighbours_average_vector[2] / number_of_neighbours # calculate the cohesion_heading_vector from the position of the agent called agent_name to the averaged neighbours position cohesion_heading_vector = vectorMath.get_vector_between_points(agent_pos, neighbours_average_vector) # normalise the cohesion_heading_vector cohesion_heading_vector = vectorMath.vector_normalise(cohesion_heading_vector) return cohesion_heading_vector
def get_goal_heading(agent): # initialise the heading vector that will hold the goal heading goal_heading_vector = [0, 0, 0] # if we have reached the end of the path or otherwise require a new goal do it here if(pathfinding.need_new_goal(agent.get_goal(),agent.get_rounded_pos())): agent.new_goal() # check if we need to start heading towards the next step in the path agent.check_step() #get goal vector from our current position and the next spot in our path goal_heading_vector = vectorMath.get_vector_between_points(agent.get_agent_position(),agent.get_current_step()) # normalise goal vector goal_heading_vector = vectorMath.vector_normalise(goal_heading_vector) return goal_heading_vector
def get_goal_heading(agent):
# initialise the heading vector that will hold the goal heading
goal_heading_vector = [0, 0, 0]
# if we have reached the end of the path or otherwise require a new goal do it here
if (pathfinding.need_new_goal(agent.get_goal(), agent.get_rounded_pos())):
agent.new_goal()
# check if we need to start heading towards the next step in the path
agent.check_step()
#get goal vector from our current position and the next spot in our path
goal_heading_vector = vectorMath.get_vector_between_points(
agent.get_agent_position(), agent.get_current_step())
# normalise goal vector
goal_heading_vector = vectorMath.vector_normalise(goal_heading_vector)
return goal_heading_vector
def get_alignment_heading(neighbours): # initialise the heading vector that will hold the alignment heading alignment_heading_vector = [0, 0, 0] neighbour_vector = [0, 0, 0] # get the number of neighbouring agents in the neighbours list [find the length of the list] number_of_neighbours = len(neighbours) # IF we have 1 or more agents in the neighbours list if number_of_neighbours > 0: # FOR every neighbour in the neighbours list for neighbour in neighbours: # get the heading of the neighbour as a vector neighbour_vector = neighbour.get_agent_heading_vector() # add this heading to the alignment_heading_vector alignment_heading_vector = vectorMath.vector_add(alignment_heading_vector, neighbour_vector) # calculate the averaged alignment_heading_vector alignment_heading_vector[0] = alignment_heading_vector[0] / number_of_neighbours alignment_heading_vector[1] = alignment_heading_vector[1] / number_of_neighbours alignment_heading_vector[2] = alignment_heading_vector[2] / number_of_neighbours # normalise alignment vector alignment_heading_vector = vectorMath.vector_normalise(alignment_heading_vector) return alignment_heading_vector