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from CBAA import CBAA_agent

import numpy as np

import matplotlib.pyplot as plt

task_num = 10

robot_num = 10

task = np.random.uniform(low=0,high=1,size=(task_num,2))

robot_list = [CBAA_agent(id=i, task=task) for i in range(robot_num)]

fig, ax = plt.subplots()

ax.set_xlim((-0.1,1.1))

ax.set_ylim((-0.1,1.1))

ax.plot(task[:,0],task[:,1],'rx')

robot_pos = np.array([r.state[0].tolist() for r in robot_list])

ax.plot(robot_pos[:,0],robot_pos[:,1],'b^')

# Network Initialize

G = np.ones((robot_num, robot_num)) # Fully connected network

# for i in range(robot_num-1):

# for j in range(i+1,robot_num):

# ax.plot([robot_pos[i][0],robot_pos[j][0]],[robot_pos[i][1],robot_pos[j][1]],'g--',linewidth=1)

t = 0 # Iteration number

assign_plots = []

while True:

print("==Iteration {}==".format(t))

## Phase 1: Auction Process

print("Auction Process")

for robot_id, robot in enumerate(robot_list):

# select task by local information

robot.select_task()

# print(robot.x)

if t == 0:

assign_line, = ax.plot([robot.state[0][0],task[robot.J,0]],[robot.state[0][1],task[robot.J,1]],'k-',linewidth=1)

assign_plots.append(assign_line)

else:

assign_plots[robot_id].set_data([robot.state[0][0],task[robot.J,0]],[robot.state[0][1],task[robot.J,1]])

plt.pause(0.5)

## Phase 2: Consensus Process

print("Consensus Process")

# Send winning bid list to neighbors (depend on env)

message_pool = [robot.send_message() for robot in robot_list]

for robot_id, robot in enumerate(robot_list):

# Recieve winning bidlist from neighbors

g = G[robot_id]

connected, = np.where(g==1)

Y = {neighbor_id:message_pool[neighbor_id] for neighbor_id in connected}

# Update local information and decision

robot.update_task(Y)

# print(robot.x)

if (robot.x).any():

assign_plots[robot_id].set_data([robot.state[0][0],task[robot.J,0]],[robot.state[0][1],task[robot.J,1]])

else:

assign_plots[robot_id].set_data([robot.state[0][0],robot.state[0][0]],[robot.state[0][1],robot.state[0][1]])

plt.pause(0.5)

t += 1