Commit 38fed5df authored by Jayant Khatkar's avatar Jayant Khatkar

cleanup of scheduler

parent b7dc48ae
import gcode2contour as gc
import numpy as np
from gcode2contour import Position
class Action:
An action can be a job, or moving between jobs.
It has a start location, an end location, a time to execute
and when relevant, the associated job
def __init__(self, start_loc, end_loc, t, job=None):
self.start_location = start_loc
self.end_location = end_loc
self.time = t
self.job = job
def travel_to(self, other_job):
Calculate distance to another job
End position of this job to the start position of the other job
return abs(self.end_location - other_job.start_location)
def travel_from(self, other_job):
Calculate distance to another job
Start position of this job to the end position of the other job
return abs(self.start_location - other_job.end_location)
def contours2Actions(contours):
Convert a list of contours into a list of actions
actions = []
for c in contours:
actions.append(Action(c.pos[0], c.pos[-1], c.time[-1], c))
return actions
class Plan:
stores the plan of the whole system so far
def __init__(self, robot_list):
initiate at unplanned state
self.robots = robot_list
self.actions = {k:[] for k in robot_list}
self.time = {k:0 for k in robot_list}
def __getitem__(self, robot):
return (self.actions[robot], self.times[robot])
def append(self, action, robot):
self.time[robot] += action.time
from DecMCTS import Tree
import numpy.random as rand
from planning_utils import *
# Creat a bunch of jobs
# Each job has a time to do (in seconds?)
......@@ -9,24 +10,34 @@ n_jobs = 20
job_time_min = 0
job_time_max = 10
vel = 0.1
job_locs = [(x,y) for x,y in zip(rand.rand(n_jobs), rand.rand(n_jobs))]
job_locs = [Position(x,y,0)
for x,y in zip(rand.rand(n_jobs), rand.rand(n_jobs))]
job_times = [job_time_min+(job_time_max-job_time_min)*t
for t in rand.rand(n_jobs)]
def dist(p1,p2):
return ((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2)**0.5
travel_time = {(j1,j2):dist(job_locs[j1], job_locs[j2])/vel
# This is the todo list for the decmcts scheduler
# - for now not considering dependencies
actions = [Action(job_locs[i], job_locs[i], job_times[i], job=i)
for i in range(n_jobs)]
# Premptively calculate distance pairs
travel_time = {(j1,j2):actions[j1].travel_to(actions[j2])/vel
for j1 in range(n_jobs)
for j2 in range(n_jobs)}
for j2 in range(n_jobs)
if j1!=j2}
# Data needed for any calculations
data = {
"job_locs": job_locs,
"job_times": job_times
"actions": actions
"tt": travel_time
# State For a particular robot
class State:
Stores the state of one robot for MCTS
def __init__(self, start_pos): = []
self.time_so_far = 0
......@@ -40,21 +51,22 @@ class State:
self.time_wasted += data["tt"][[-1],[-2]]
self.current_pos = job_locs[new_job]
def state_storer(data, parent_state, action):
if parent_state == None:
return State((0,0)) # This state is also used Null action when calculating local reward
state = deepcopy(parent_state)
state = deepcopy(parent_state) # NOTE THIS WILL NEED TO CHANGE WITH CONTOURS
return state
# All actions not done by the current robot are considered available
# actions of other robots are not considered
def avail_actions(data, states, robot_id):
return [j for j in list(range(n_jobs)) if j not in states[robot_id].jobs]
# reward is inversely proportional to total time taken
def reward(dat, states):
time_wasted = [states[robot].time_wasted for robot in states]
return 1/sum(each_robot_sum)
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