def planner_comparison(seed):
params = get_data_random(seed + 1,
map_res=8,
map_fill_perc=30,
agent_n=5,
job_n=5,
idle_goals_n=0)
agent_pos, grid, idle_goals, jobs = params
mapstr = get_map_str(grid)
print(mapstr)
maphash = str(hashlib.md5(mapstr.encode('utf-8')).hexdigest())[:8]
print(maphash)
fname = "planner/eval/cache/" + str(
maphash) + '.pkl' # unique filename based on map
pre_calc_paths(jobs, idle_goals, grid, fname)
config_opt = generate_config()
config_opt['params'] = params
config_opt['filename_pathsave'] = fname
config_milp = config_opt.copy()
config_milp['milp'] = 1
config_cobra = config_opt.copy()
config_cobra['cobra'] = 1
config_greedy = config_opt.copy()
config_greedy['greedy'] = 1
config_nn = config_opt.copy()
config_nn['number_nearest'] = 2
config_col = config_nn.copy()
config_col['all_collisions'] = True
if is_cch():
print("Configs: [config_opt, config_nn, config_milp]")
configs = [config_opt, config_nn,
config_milp] #, config_cobra, config_greedy, config_col]
sizes = [1, 2]
else:
print(
"Configs: [config_opt, config_nn, config_milp, config_cobra, config_greedy]"
)
configs = [
config_opt, config_nn, config_milp, config_cobra, config_greedy
]
sizes = [2, 3, 4]
ts, ress = benchmark(one_planner, [configs, sizes],
samples=1,
timeout=1000)
return ts, ress
def eval(_map, agent_pos, jobs, fname, display=False, finished_blocking=True):
from planner.milp.milp import plan_milp
grid = np.repeat(_map[:, ::2, np.newaxis], 100, axis=2)
config = generate_config()
config['filename_pathsave'] = fname
config['finished_agents_block'] = finished_blocking
print("Problem:")
print(str(jobs))
print(str(agent_pos))
mapstr = get_map_str(grid)
print(mapstr)
print("TCBS")
res_agent_job, res_agent_idle, res_paths = plan(agent_pos,
jobs, [], [],
grid,
config,
plot=False)
print("agent_job: " + str(res_agent_job))
print("paths: " + str(res_paths))
costs_opt = get_costs(res_paths, jobs, res_agent_job, display)
print("MINLP")
minlp_res_agent_job, minlp_res_paths = plan_milp(agent_pos,
jobs,
grid,
config,
plot=False)
print("agent_job: " + str(minlp_res_agent_job))
print("paths: " + str(minlp_res_paths))
costs_minlp = get_costs(minlp_res_paths, jobs, minlp_res_agent_job,
display)
# MINLP VS PLAN
if display:
fig = plt.figure()
ax1 = fig.add_subplot(121, projection='3d')
ax1.set_title("Solution Optimal")
ax2 = fig.add_subplot(122, projection='3d')
plot_results(ax1, [], res_paths, res_agent_job, agent_pos, grid, [],
jobs, "Optimal")
plot_results(ax2, [], minlp_res_paths, minlp_res_agent_job, agent_pos,
grid, [], jobs, "Separate")
plt.show()
print("TCBS (results again for comparison)")
print("agent_job: " + str(res_agent_job))
print("paths: " + str(res_paths))
costs_tcbs = get_costs(res_paths, jobs, res_agent_job, display)
return costs_tcbs, costs_minlp
from planner.tcbs.plan import plan, generate_config
from planner.milp.milp import plan_milp
import matplotlib.pyplot as plt
from tools import get_map_str
params = get_data_random(330,
map_res=8,
map_fill_perc=30,
agent_n=4,
job_n=4,
idle_goals_n=0)
agent_pos, grid, idle_goals, jobs = params
mapstr = get_map_str(grid)
maphash = str(hashlib.md5(mapstr.encode('utf-8')).hexdigest())[:8]
fname = "planner/eval/cache/" + str(maphash) + '.pkl' # unique filename based on map
config = generate_config()
config['finished_agents_block'] = True
config['filename_pathsave'] = fname
tcbs_agent_job, tcbs_agent_idle, tcbs_paths = plan(agent_pos, jobs, [], [], grid, config, plot=False)
f = plt.figure()
ax0= f.add_subplot(111)
plot_results(ax0, tcbs_agent_idle, tcbs_paths, tcbs_agent_job, agent_pos, grid, [], jobs, "tcbs")
milp_agent_job, milp_agent_idle, milp_paths = plan_milp(agent_pos, jobs, [], [], grid, config, plot=False)
f1 = plt.figure()
ax1= f1.add_subplot(111)