def main(num_agents):
world, starts, goals = util.generate_problem(num_agents, 16, 16, 0.2)
# Create agents
agents = [Agent(world, starts[i], goals[i]) for i in range(num_agents)]
start_time = timeit.default_timer()
paths = dimpp(agents, None, None)
end_time = timeit.default_timer()
print(f'elapsed time: {(end_time - start_time) * 1000:5.3f}ms')
print('Making visualisation')
vis = visualisation.Visualisation(world, num_agents, scale=20)
vis.draw_paths('midpp.mkv', paths)
def main(agents):
world, starts, goals = util.generate_problem(agents, 16, 16, 0.2)
print(starts, goals)
start_time = timeit.default_timer()
paths = standard_algorithm(agents, world, starts, goals,
start_time=start_time, max_time=15)
end_time = timeit.default_timer()
print(f'elapsed time: {(end_time - start_time) * 1000:5.3f}ms')
print('Writing visualisations')
vis = visualisation.Visualisation(world, agents, scale=20)
frames = vis.draw_paths('sa.mkv', paths)
def main(agents):
world, starts, goals = util.generate_problem(agents, 16, 16, 0.2)
print('starts:', starts)
print('goals: ', goals)
start_time = timeit.default_timer()
paths = odid2(agents,
world,
starts,
goals,
start_time=start_time,
max_time=5)
print('Writing visualisations')
vis = visualisation.Visualisation(world, agents, scale=20)
frames = vis.draw_paths('odid.mkv', paths)
def main(num_agents):
world, starts, goals = util.generate_problem(num_agents, 16, 16, 0.2)
# Create agents
agents = [Agent(world, starts[i], goals[i]) for i in range(num_agents)]
start_time = timeit.default_timer()
if '--simple' in sys.argv or '-s' in sys.argv:
paths = simple_poc(agents)
else:
paths = poc(agents, start_time=start_time, max_time=5)
end_time = timeit.default_timer()
print(f'elapsed time: {(end_time - start_time) * 1000:5.3f}ms')
print('Making visualisation')
vis = visualisation.Visualisation(world, num_agents, scale=20)
vis.draw_paths('poc.mkv', paths)
def version1(agents, start_time, max_time, visualize=False):
paths = []
for agent in agents:
agent.plan(start_time=start_time, max_time=max_time)
paths.append(agent.path)
if visualize:
vis = visualisation.Visualisation(agents[0].world,
len(agents),
scale=20)
count = 0
conflicts = util.paths_conflict(paths)
init_conflicts = len(convert_conflicts(agents, conflicts))
while conflicts:
time = timeit.default_timer()
if start_time != None and (time - start_time) > max_time:
raise TimeExceeded()
if visualize:
print('Exporting conflicts')
im = vis.draw_paths_with_conflicts(paths, conflicts)
im.save(f'conflict_{count:05}.png')
count += 1
#print(f'Conflicts found: {len(conflicts)}')
#pprint(conflicts)
conflict_objs = convert_conflicts(agents, conflicts)
#pprint(conflict_objs)
# Add conflicts to agents
for agent in agents:
agent.conflicts.clear()
for conflict in conflict_objs.values():
for agent in conflict.agents:
agent.conflicts.add(conflict)
# Get the agents to resove the conflicts
for agent in agents:
try:
conflict = agent.conflicts[0]
except IndexError:
continue # Agent has no conflicts
conflict.resolve(agents, start_time, max_time)
# Update the list of conflicts
paths = [agent.path for agent in agents]
conflicts = util.paths_conflict(paths)
#print() # Just a new line to break up iterations
# Final visualisation
if visualize:
print('Exporting final conflicts')
im = vis.draw_paths_with_conflicts(paths, conflicts)
im.save(f'conflict_{count:05}.png')
# Find number of conflicts solved
conflicts = set()
for agent in agents:
conflicts.update(agent.resolved_conflicts)
# Find the size of conflicts
sizes = (len(c.agents) for c in conflicts)
return {'paths': paths,
'initial': init_conflicts,
'solved': len(conflicts),
'sizes': sizes,
}