DroneSim

A real-time simulator using the example of an aerial swarm of drones in a survivor rescue scenario with the help of common Python libraries.

Scenario

Swarm of Drones (UAV)     nD
Detect ground survivors   nS
Rescue them
Fixed range               R
Fixed test area           L x L
Random distribution
Random walk by drones

Solution

Class for UAV               UAV.py
Class for Survivors         survivor.py
Functions for chaos         chaos.py
Define constant             constants.py
Global variables            constants.py
Real-time simulation        main.py
Plotting the results        plotter.py

UAV class

Data members:
ID							unique identity
position				x, y
velocity				velx, vely
rescued					list of survivors rescued
_time_					to measure time
Init_time				time of initialization

Functions:
__init__					Initialize
update_position		Move the UAV
search_survivors	Search for survivors

Survivor class

Data members:
ID                unique identity
position          x, y
marked_safe       whether rescued

Randomness

random_pos():
random.uniform(0, L)

random_val():
random.uniform(-10.0, 10.0)

Random walk mobility of UAVs

update_position(time_elapsed):
  x = (x + velx* time_elapsed* random_val() )%L
  y = (y + vely* time_elapsed* random_val() )%L

Searching for Survivors

search_survivors():
  init_time = time()
  While search is not complete:
  Mark safe the survivors in range
  Time delay (for rescue)
  update_position( time() - _time_ )
  _time_ = time()
  Write data to file

Defining Global variables

DRONES        List of drone objects
SURVIVORS     List of survivor objects
missions      List of parallel threads
output_file   File for logging output

Creating objects

for i in range(nD):
DRONES.append( UAV(i, random_pos(), random_pos(), Vmax, Vmax )

for i in range(nS):
SURVIVORS.append( survivor(i, random_pos(), random_pos() )

Running Parallel threads

for i in range(nD):
  process = threading.Thread( target=DRONES[i].search_survivors(), args=None )
  process.start()
  missions.append(process)

Extracting results

search_survivors():
  ...
  While search is not complete:
  ...
  ...
  output_file.write("%d, %f\n"%( len(SURVIVORS),time.time()- self.init_time) )

Visual Representation

import matplotlib.pyplot as plt
f = open(‘out.txt’, ‘r’)
N, T = [], []
for line in f:
  l = line.split(‘,’)
  N.append(int(l[0]))
  T.append(float(l[1]))
plt.plot(T,N)
plt.show()