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pursuit_game_pp_1vs1.py
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pursuit_game_pp_1vs1.py
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'''
Author: Qin Yang
05/08/2021
'''
#Import Robotarium Utilities
import rps.robotarium as robotarium
from rps.utilities.transformations import *
from rps.utilities.graph import *
from rps.utilities.barrier_certificates import *
from rps.utilities.misc import *
from rps.utilities.controllers import *
import numpy as np
from fractions import Fraction
import time
import random
import os
def alien_detector(numExplorer, x, id, sensing_distance):
detector = False
for i in range(numExplorer):
if np.linalg.norm(x[:2,[i]] - x[:2,[id]]) < sensing_distance:
detector = True
return detector
def pursuit_game_pp():
N = 2
initial_conditions = np.array([[-1.4, 1.333],[-0.8, 0.830],[0, 1]])
r = robotarium.Robotarium(number_of_robots=N, show_figure=True, initial_conditions=initial_conditions, sim_in_real_time=True)
# How many iterations do we want (about N*0.033 seconds)
iterations = 10000
# sensing distance between explorer and alien
sensing_distance = 0.3
numActiveAlien = 0
# initial agent's energy and hp
agent_energy_level = []
pursuer_evader_distance = []
for i in range(N):
if i == 1:
agent_energy_level.append(150)
else:
agent_energy_level.append(100)
pursuer_evader_distance.append(0)
#Max_simum linear speed of robot specified by motors
magnitude_limit = 0.1
# We're working in single-integrator dynamics, and we don't want the robots
# to collide or drive off the testbed. Thus, we're going to use barrier certificates
si_barrier_cert = create_single_integrator_barrier_certificate_with_boundary()
# Create SI to UNI dynamics tranformation
si_to_uni_dyn, uni_to_si_states = create_si_to_uni_mapping()
# Generated a connected graph Laplacian (for a cylce graph).
L = cycle_GL(N)
si_velocities = np.zeros((2, N))
CM1 = np.random.rand(N,3)
CM2 = np.random.rand(N,3)
CM3 = np.random.rand(N,3)
marker_size_goal = determine_marker_size(r,0.2)
robot_marker_size_m = 0.35
font_size_m = 0.1
font_size = determine_font_size(r,font_size_m)
font_size_m1 = 0.06
font_size1 = determine_font_size(r,font_size_m1)
font_size_m2 = 0.04
font_size2 = determine_font_size(r,font_size_m2)
marker_size_robot = determine_marker_size(r, robot_marker_size_m)
line_width = 5
# Plot Graph Connections
x = r.get_poses() # Need robot positions to do this.
old_x = []
for i in range(N):
old_x.append(initial_conditions[:2, [i]])
pursuer_label = r.axes.text(x[0,0],x[1,0]+0.25,"pursuer",fontsize=font_size1, color='b',fontweight='bold',horizontalalignment='center',verticalalignment='center',zorder=0)
pursuer_energy_label = r.axes.text(x[0,0],x[1,0]+0.2,"NRG: ",fontsize=font_size2, color='c',fontweight='bold',horizontalalignment='center',verticalalignment='center',zorder=0)
pursuer_hp_label = r.axes.text(x[0,0],x[1,0]+0.15,"Dist: ",fontsize=font_size2, color='m',fontweight='bold',horizontalalignment='center',verticalalignment='center',zorder=0)
evader_label = r.axes.text(x[0,1],x[1,1]+0.25,"evader",fontsize=font_size1, color='r',fontweight='bold',horizontalalignment='center',verticalalignment='center',zorder=0)
evader_energy_label = r.axes.text(x[0,1],x[1,1]+0.2,"NRG: ",fontsize=font_size2, color='c',fontweight='bold',horizontalalignment='center',verticalalignment='center',zorder=0)
evader_hp_label = r.axes.text(x[0,1],x[1,1]+0.15,"Dist: ",fontsize=font_size2, color='m',fontweight='bold',horizontalalignment='center',verticalalignment='center',zorder=0)
r.step()
for k in range(iterations):
# Get the poses of the robots and convert to single-integrator poses
x = r.get_poses()
x_si = uni_to_si_states(x)
waypoints = np.array([[random.uniform(-1.4, 1.4), random.uniform(-1.4, 1.4), random.uniform(-1.4, 1.4)], [random.uniform(-1.4, 1.4), random.uniform(-1.4, 1.4), random.uniform(-1.4, 1.4)]])
pursuer_label.set_position([x_si[0,0],x_si[1,0]+0.25])
pursuer_label.set_fontsize(determine_font_size(r,font_size_m1))
pursuer_energy_label.set_position([x_si[0,0],x_si[1,0]+0.2])
pursuer_energy_label.set_fontsize(determine_font_size(r,font_size_m2))
pursuer_energy_label.set_text("NRG: " + str(round(agent_energy_level[0], 2)))
pursuer_hp_label.set_position([x_si[0,0],x_si[1,0]+0.15])
pursuer_hp_label.set_fontsize(determine_font_size(r,font_size_m2))
pursuer_hp_label.set_text("Dist: " + str(round(pursuer_evader_distance[0], 2)))
evader_label.set_position([x_si[0,1],x_si[1,1]+0.25])
evader_label.set_fontsize(determine_font_size(r,font_size_m1))
evader_energy_label.set_position([x_si[0,1],x_si[1,1]+0.2])
evader_energy_label.set_fontsize(determine_font_size(r,font_size_m2))
evader_energy_label.set_text("NRG: " + str(round(agent_energy_level[1], 2)))
evader_hp_label.set_position([x_si[0,1],x_si[1,1]+0.15])
evader_hp_label.set_fontsize(determine_font_size(r,font_size_m2))
evader_hp_label.set_text("Dist: " + str(round(pursuer_evader_distance[1], 2)))
# Initialize the single-integrator control inputs
#si_velocities = np.zeros((2, N))
# For each robot...
for i in range(N):
# Get the neighbors of robot 'i' (encoded in the graph Laplacian)
j = topological_neighbors(L, i)
# Compute the pp algorithm
if i == 0:
si_velocities[:, i] = np.sum(x_si[:, j] - x_si[:, i, None], 1)
if i == 1 and k%20 == 0:
si_velocities[:,i] = np.sum(waypoints[:, 0, None] - x_si[:, i, None], 1)
# #Keep single integrator control vectors under specified magnitude
# # Threshold control inputs
norms = np.linalg.norm(si_velocities, 2, 0)
idxs_to_normalize = (norms > magnitude_limit)
si_velocities[:, idxs_to_normalize] *= magnitude_limit/norms[idxs_to_normalize]
# Use the barrier certificate to avoid collisions
si_velocities = si_barrier_cert(si_velocities, x_si)
# Transform single integrator to unicycle
dxu = si_to_uni_dyn(si_velocities, x)
for i in range(N):
# if i == 1: # evader
# dxu[:,i] = dxu[:,i] * 3
if i == 0: # pursuer
dxu[:,i] = dxu[:,i] * 1.05
# if i==1 and k%100==0: # pursuer
# if i ==1 and k > 50:
# dxu[1,i] = random.random() * np.sign(random.uniform(-1, 1)) * 100
# dxu[1,i] = random.uniform(-100, 100)
# Set the velocities of agents 1,...,N
r.set_velocities(np.arange(N), dxu)
# Calculate agent energy cost
for i in range(N):
agent_energy_level[i] -= np.linalg.norm(old_x[i] - x[:2,[i]]) * 10
# Calculate the distance between pursuer and envader
if i == 0:
pursuer_evader_distance[i] = np.linalg.norm(old_x[i] - x[:2,[1]]) * 10
else:
pursuer_evader_distance[i] = np.linalg.norm(old_x[i] - x[:2,[0]]) * 10
# detect the number of aliens
if alien_detector(N-1, x, -1, sensing_distance):
numActiveAlien +=1
# recode old position
old_x.clear()
for i in range(N):
old_x.append(x[:2, [i]])
if pursuer_evader_distance[0] <= 3:
print('cost time is ' + str(k))
os._exit(0)
# Iterate the simulation
r.step()
def main():
pursuit_game_pp()
if __name__ == '__main__':
main()