def __init__(self, s_start, s_goal, heuristic_type):
self.s_start, self.s_goal = s_start, s_goal
self.heuristic_type = heuristic_type
self.Env = env.Env()
self.Plot = plotting.Plotting(self.s_start, self.s_goal)
self.u_set = self.Env.motions
self.obs = self.Env.obs
self.x = self.Env.x_range
self.y = self.Env.y_range
self.g, self.rhs, self.U = {}, {}, {}
for i in range(self.Env.x_range):
for j in range(self.Env.y_range):
self.rhs[(i, j)] = float("inf")
self.g[(i, j)] = float("inf")
self.rhs[self.s_start] = 0
self.U[self.s_start] = self.CalculateKey(self.s_start)
self.visited = set()
self.count = 0
self.fig = plt.figure()
def __init__(self, s_start, s_goal, eps, heuristic_type):
self.s_start, self.s_goal = s_start, s_goal
self.heuristic_type = heuristic_type
self.Env = env.Env() # class Env
self.Plot = plotting.Plotting(s_start, s_goal)
self.u_set = self.Env.motions # feasible input set
self.obs = self.Env.obs # position of obstacles
self.x = self.Env.x_range
self.y = self.Env.y_range
self.g, self.rhs, self.OPEN = {}, {}, {}
for i in range(1, self.Env.x_range - 1):
for j in range(1, self.Env.y_range - 1):
self.rhs[(i, j)] = float("inf")
self.g[(i, j)] = float("inf")
self.rhs[self.s_goal] = 0.0
self.eps = eps
self.OPEN[self.s_goal] = self.Key(self.s_goal)
self.CLOSED, self.INCONS = set(), dict()
self.visited = set()
self.count = 0
self.count_env_change = 0
self.obs_add = set()
self.obs_remove = set()
self.title = "Anytime D*: Small changes" # Significant changes
self.fig = plt.figure()
def main():
problemFile = '/home/aifs1/gu/phd/research/workingPaper/realtime-nancy/worlds/gridPathfinding/exampleworlds/small-3.gp'
# resultVisFile = '/home/aifs1/gu/phd/research/workingPaper/metareasoning/build_debug/onecommitvis.s3.json'
# resultVisFile = '/home/aifs1/gu/phd/research/workingPaper/metareasoning/build_debug/lsslrtavis.s3.json'
resultVisFile = '/home/aifs1/gu/phd/research/workingPaper/metareasoning/build_debug/dtrts.small-3.json'
# problemFile = '/home/aifs1/gu/phd/research/workingPaper/realtime-nancy/worlds/gridPathfinding/exampleworlds/small-2.gp'
# resultVisFile = '/home/aifs1/gu/phd/research/workingPaper/metareasoning/build_debug/onecommitvis.s2.json'
# resultVisFile = '/home/aifs1/gu/phd/research/workingPaper/metareasoning/build_debug/allthewaycommitvis.s2.json'
# resultVisFile = '/home/aifs1/gu/phd/research/workingPaper/metareasoning/build_debug/vistest.json'
# system.run('cpp executable -vis resultvisfile < input')
plot = plotting.Plotting(problemFile)
path, visited, isKeepThinking, committed = parseVisFile(resultVisFile)
print("path", path)
print("visited", visited)
print("isKeepThinking", isKeepThinking)
print("committed", committed)
# plot.animation_one(path, visited, "Fixed Strategy: Commit One")
# plot.animation_alltheway(path, visited, "Fixed Strategy: Commit All")
plot.animation_deepthinking(path, visited, isKeepThinking, committed,
"Our Approach")
def __init__(self, s_start, s_goal):
self.s_start, self.s_goal = s_start, s_goal
self.Env = env.Env()
self.Plot = plotting.Plotting(self.s_start, self.s_goal)
self.u_set = self.Env.motions
self.obs = self.Env.obs
self.x = self.Env.x_range
self.y = self.Env.y_range
self.fig = plt.figure()
self.OPEN = set()
self.t = {}
self.PARENT = {}
self.h = {}
self.k = {}
self.path = []
self.visited = set()
self.count = 0
for i in range(self.Env.x_range):
for j in range(self.Env.y_range):
self.t[(i, j)] = 'NEW'
self.k[(i, j)] = 0.0
self.h[(i, j)] = float("inf")
self.PARENT[(i, j)] = None
self.h[self.s_goal] = 0.0
def __init__(self, s_start, s_goal, heuristic_type):
self.s_start, self.s_goal = s_start, s_goal
self.heuristic_type = heuristic_type
self.Env = env.Env() # class Env
self.Plot = plotting.Plotting(s_start, s_goal)
self.u_set = self.Env.motions # feasible input set
self.obs = self.Env.obs # position of obstacles
self.x = self.Env.x_range
self.y = self.Env.y_range
self.g, self.rhs, self.OPEN = {}, {}, {}
self.parent = {}
self.cknbr = {}
self.ccknbr = {}
self.bptr = {}
self.init_table()
for i in range(self.Env.x_range):
for j in range(self.Env.y_range):
self.rhs[(i, j)] = float("inf")
self.g[(i, j)] = float("inf")
self.bptr[(i, j)] = (i, j)
self.rhs[self.s_goal] = 0.0
self.OPEN[self.s_goal] = self.CalculateKey(self.s_goal)
self.visited = set()
self.count = 0
self.fig = plt.figure()
def main():
s_start = (5, 5)
s_goal = (45, 25)
BF = BestFirst(s_start, s_goal, 'euclidean')
plot = plotting.Plotting(s_start, s_goal)
path, visited = BF.searching()
plot.animation(path, visited, "Best-first Searching") # animation
def main():
s_start = (5, 5)
s_goal = (45, 25)
dfs = DFS(s_start, s_goal)
plot = plotting.Plotting(s_start, s_goal)
path, visited = dfs.searching()
plot.animation(path, visited, "Depth-first Searching (DFS)") # animation
def main():
s_start = (5, 5)
s_goal = (45, 25)
astar = Astar(s_start, s_goal, "euclidean")
plot = plotting.Plotting(s_start, s_goal)
path, visited = astar.searching()
plot.animation(path, visited, "A*") # animation
def main():
s_start = (5, 5)
s_goal = (45, 25)
dijkstra = Dijkstra(s_start, s_goal, 'None')
plot = plotting.Plotting(s_start, s_goal)
path, visited = dijkstra.searching()
plot.animation(path, visited, "Dijkstra's") # animation generate
def main():
s_start = (5, 5)
s_goal = (45, 25)
arastar = AraStar(s_start, s_goal, 2.5, "euclidean")
plot = plotting.Plotting(s_start, s_goal)
path, visited = arastar.searching()
plot.animation_ara_star(path, visited, "Anytime Repairing A* (ARA*)")
def main():
s_start = (5, 5)
s_goal = (45, 25)
bfs = BFS(s_start, s_goal)
plot = plotting.Plotting(s_start, s_goal)
path, visited = bfs.searching()
plot.animation(path, visited, "Breadth-first Searching (BFS)")
def main():
x_start = (5, 5)
x_goal = (45, 25)
bastar = BidirectionalAStar(x_start, x_goal, "euclidean")
plot = plotting.Plotting(x_start, x_goal)
path, visited_fore, visited_back = bastar.searching()
plot.animation_bi_astar(path, visited_fore, visited_back, "Bidirectional-A*") # animation
def main():
s_start = (10, 5)
s_goal = (45, 25)
lrta = LrtAstarN(s_start, s_goal, 250, "euclidean")
plot = plotting.Plotting(s_start, s_goal)
lrta.searching()
plot.animation_lrta(lrta.path, lrta.visited,
"Learning Real-time A* (LRTA*)")
def main():
s_start = (10, 5)
s_goal = (45, 25)
rtaa = RTAAStar(s_start, s_goal, 240, "euclidean")
plot = plotting.Plotting(s_start, s_goal)
rtaa.searching()
plot.animation_lrta(rtaa.path, rtaa.visited,
"Real-time Adaptive A* (RTAA*)")
def main():
s_start = (5, 5)
s_goal = (45, 25)
dfs = DFS(s_start, s_goal, 'None')
plot = plotting.Plotting(s_start, s_goal)
path, visited = dfs.searching()
visited = list(dict.fromkeys(visited))
plot.animation(path, visited, "Depth-first Searching (DFS)") # animation
def __init__(self, s_start, s_goal):
self.s_start, self.s_goal = s_start, s_goal
self.Env = env.Env()
self.plotting = plotting.Plotting(self.s_start, self.s_goal)
self.u_set = self.Env.motions # feasible input set
self.obs = self.Env.obs # position of obstacles
self.OPEN = queue.QueuePrior() # OPEN set
self.OPEN.put(self.s_start, self.Heuristic(self.s_start))
self.CLOSED = [] # CLOSED set / visited order
self.PARENT = {self.s_start: self.s_start}
def __init__(self, s_start, s_goal):
self.s_start, self.s_goal = s_start, s_goal
self.Env = env.Env()
self.plotting = plotting.Plotting(self.s_start, self.s_goal)
self.u_set = self.Env.motions # feasible input set
self.obs = self.Env.obs # position of obstacles
self.OPEN = queue.QueueFIFO() # OPEN set: visited nodes
self.OPEN.put(self.s_start)
self.CLOSED = [] # CLOSED set: explored nodes
self.PARENT = {self.s_start: self.s_start}
def __init__(self, s_start, s_goal):
self.s_start, self.s_goal = s_start, s_goal
self.Env = env.Env()
self.plotting = plotting.Plotting(self.s_start, self.s_goal)
self.u_set = self.Env.motions # feasible input set
self.obs = self.Env.obs # position of obstacles
self.g = {self.s_start: 0, self.s_goal: float("inf")} # cost to come
self.OPEN = queue.QueuePrior() # priority queue / OPEN set
self.OPEN.put(self.s_start, 0)
self.CLOSED = [] # closed set & visited
self.PARENT = {self.s_start: self.s_start}
def __init__(self, s_start, s_goal):
self.s_start = s_start
self.s_goal = s_goal
self.kp = 5.0
self.eta = 400
self.r = 30.0
self.OL = 10
self.rr = 2
self.Env = env.Env()
self.Plot = plotting.Plotting(s_start, s_goal)
self.u_set = self.Env.motions # feasible input set
self.obs = self.Env.obs # position of obstacles
self.x = self.Env.x_range
self.y = self.Env.y_range
def __init__(self, s_start, s_goal):
self.s_start, self.s_goal = s_start, s_goal
self.Env = env.Env()
self.Plot = plotting.Plotting(self.s_start, self.s_goal)
self.u_set = self.Env.motions
self.obs = self.Env.obs
self.x = self.Env.x_range
self.y = self.Env.y_range
self.fig = plt.figure()
self.OPEN = set()
self.t = dict()
self.PARENT = dict()
self.h = dict()
self.k = dict()
self.path = []
self.visited = set()
self.count = 0
