raghavthakar

# optimise the values in TPM from the X_dens
def TPMOptimisation(X_dens, task_ids):

    # X_dens is the pi vector
    # len(task_ids) gives the number of tasks (num_tasks)

    # create the identity matrix with dimensions same as that of P (num_tasks*num_tasks)
    I = []
    for i in range(len(task_ids)):
        temp_row = [0 for j in range(len(task_ids))]

raghavthakar

#### Running command: "make -j8 -l8" in "/home/raghav/OntarioTech/AutonomousWheelchair/build"
####
[  0%] Built target nav_msgs_generate_messages_cpp
[  0%] Built target std_msgs_generate_messages_cpp
[  0%] Built target geometry_msgs_generate_messages_cpp
[  0%] Built target _wheelchair_control_generate_messages_check_deps_MoveDistanceActionFeedback
[  0%] Built target _wheelchair_control_generate_messages_check_deps_MoveDistanceGoal
[  0%] Built target sensor_msgs_generate_messages_cpp
[  0%] Built target _wheelchair_control_generate_messages_check_deps_MoveDistanceAction
[  0%] Built target _wheelchair_control_generate_messages_check_deps_MoveDistanceFeedback

raghavthakar

from ortools.sat.python import cp_model
import math
# Custom library for visualisation. Can be omitted.
# from visualiser import Visualiser

NUM_AGENTS = 3
CELLS_PER_SIDE = 6
NUM_CELLS = CELLS_PER_SIDE * CELLS_PER_SIDE
INF = 5000

raghavthakar

from ortools.sat.python import cp_model
import math

SOURCE = 1
SINK = 395
CELLS_PER_SIDE = 20
NUM_CELLS = CELLS_PER_SIDE * CELLS_PER_SIDE
INF = 10000

# Weight matrix flattened  row-wise

raghavthakar

from ortools.sat.python import cp_model
import math
# Custom library for visualisation. Can be omitted.
from visualiser import Visualiser

NUM_CELLS = 25
CELLS_PER_SIDE  = int(math.sqrt(NUM_CELLS))
START_CELL = 2
TARGET_CELL = 24

raghavthakar

from ortools.sat.python import cp_model
import math
# Custom library for visualisation. Can be omitted.
from visualiser import Visualiser

NUM_CELLS = 25
CELLS_PER_SIDE  = int(math.sqrt(NUM_CELLS))
START_CELL = 5
TARGET_CELL = 23