├── .gitignore
├── .gitmodules
├── LICENSE.txt
├── MIP_MCPP
├── __init__.py
├── constraints.py
├── graph_utils.py
├── heuristics.py
├── instance.py
├── mcpp_planner.py
├── misc.py
├── model.py
└── warmstarter.py
├── README.md
├── data
├── cfgs
│ ├── default.yaml
│ └── server.yaml
├── instances
│ ├── floor_large-30x30-k4.istc
│ ├── floor_medium-20x20-k12.istc
│ ├── floor_small-5x10-k4.istc
│ ├── maze_large-30x30-k8.istc
│ ├── maze_medium-20x20-k6.istc
│ ├── maze_small-10x10-k6.istc
│ ├── terrain_large_1-32x32-k4.istc
│ ├── terrain_large_2-32x32-k4.istc
│ ├── terrain_medium-20x20-k4.istc
│ └── terrain_small-10x10-k8.istc
└── solutions
│ ├── floor_large-30x30-k4-alpha_0.3_warmstart.solu
│ ├── floor_medium-20x20-k12-beta_0.9_warmstart.solu
│ ├── floor_small-5x10-k4.solu
│ ├── maze_large-30x30-k8-beta_0.9_warmstart.solu
│ ├── maze_medium-20x20-k6-beta_0.9-warmstart.solu
│ ├── maze_small-10x10-k6-beta_0.9_warmstart.solu
│ ├── terrain_large_1-32x32-k4-alpha_0.9_warmstart.solu
│ ├── terrain_large_2-32x32-k4-beta_0.6_warmstart.solu
│ ├── terrain_medium-20x20-k4-beta_0.6_warmstart.solu
│ ├── terrain_small-10x10-k8-alpha_0.9-warmstart.solu
│ └── terrain_small-10x10-k8.solu
├── figs
└── floor-medium-MIP.gif
├── instance_maker.py
├── planner.py
├── requirements.txt
└── solver.py
/.gitignore:
--------------------------------------------------------------------------------
1 | .DS_Store
2 | *__pycache__*
3 | data/sim_records/*
4 | test*
--------------------------------------------------------------------------------
/.gitmodules:
--------------------------------------------------------------------------------
1 | [submodule "MSTC_Star"]
2 | path = MSTC_Star
3 | url = https://github.com/reso1/MSTC_Star.git
4 | branch = no_diagonal_motion
5 |
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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674 | .
--------------------------------------------------------------------------------
/MIP_MCPP/__init__.py:
--------------------------------------------------------------------------------
1 | import os
2 | import sys
3 |
4 | sys.path.append(os.path.join(os.path.dirname(__file__), "..", "MSTC_Star"))
5 |
--------------------------------------------------------------------------------
/MIP_MCPP/constraints.py:
--------------------------------------------------------------------------------
1 | from enum import Enum
2 | from typing import Dict, List
3 |
4 | import numpy as np
5 | import scipy.sparse as sp
6 |
7 |
8 | class BoundType(Enum):
9 | LowerBound = 0
10 | UpperBound = 1
11 | EqualBound = 2
12 |
13 |
14 | class Constraints:
15 |
16 | def __init__(
17 | self,
18 | name: str, # constraint group name
19 | num_constraints: int, # number of constraints
20 | bound: float, # bound value
21 | bound_type: BoundType, # bound type
22 | num_e: List[int], # list of number of edges foreach i
23 | num_v: List[int], # list of number of verts foreach i
24 | has_z=False # has z variables
25 | ) -> None:
26 |
27 | self.k = len(num_e)
28 | self.name = name
29 | self.num_constraints = num_constraints
30 | self.bound_type = bound_type
31 | self.has_z = has_z
32 |
33 | # [x^1_e1, x^1_e2, ...] + [x^2_e1, x^2_e2, ...] + ... + [x^k_e1, x^k_e2, ...]
34 | self.x_coeffs = np.zeros((num_constraints, sum(num_e)))
35 | # [y^1_v1, y^1_v2, ...] + [y^2_v1, y^2_v2, ...] + ... + [y^k_v1, y^k_v2, ...]
36 | self.y_coeffs = np.zeros((num_constraints, sum(num_v)))
37 | # [fu^1_e1, fu^1_e2, ...] + [fu^2_e1, fu^2_e2, ...] + ... + [fu^k_e1, fu^k_e2, ...]
38 | self.fu_coeffs = np.zeros((num_constraints, sum(num_e)))
39 | # [fv^1_e1, fv^1_e2, ...] + [fv^2_e1, fv^2_e2, ...] + ... + [fv^k_e1, fv^k_e2, ...]
40 | self.fv_coeffs = np.zeros((num_constraints, sum(num_e)))
41 | self.tau_coeff = 0
42 |
43 | self.bound = bound
44 |
45 | if self.has_z:
46 | # [z^1_v1, z^1_v2, ...] + [z^2_v1, z^2_v2, ...] + ... + [z^k_v1, z^k_v2, ...]
47 | self.z_coeffs = np.zeros((num_constraints, sum(num_v)))
48 |
49 | def build(self, x, y, fu, fv, tau, z=None):
50 |
51 | lhs = sp.csr_matrix(self.x_coeffs) @ x + \
52 | sp.csr_matrix(self.y_coeffs) @ y + \
53 | sp.csr_matrix(self.fu_coeffs) @ fu + \
54 | sp.csr_matrix(self.fv_coeffs) @ fv + \
55 | self.tau_coeff * tau
56 |
57 | if z is not None and self.has_z:
58 | lhs += sp.csr_matrix(self.z_coeffs) @ z
59 |
60 | bounds = self.bound * np.ones((self.num_constraints, 1))
61 |
62 | if self.bound_type == BoundType.LowerBound:
63 | return lhs >= bounds, self.name
64 | elif self.bound_type == BoundType.UpperBound:
65 | return lhs <= bounds, self.name
66 | else:
67 | return lhs == bounds, self.name
68 |
69 | def __str__(self, x_ind, y_ind, fu_ind, fv_ind) -> str:
70 | def __sub_str(coeff, var_name):
71 | if coeff > 0:
72 | return f" +{coeff:.0f}*{var_name}[{i},{j}]"
73 | elif coeff < 0:
74 | return f" {coeff:.0f}*{var_name}[{i},{j}]"
75 | else:
76 | return ""
77 |
78 | s = f"{self.name}:\n"
79 | for ci in range(self.num_constraints):
80 | sub_s = f"({ci}): \t" + \
81 | f"{self.tau_coeff}*tau" if self.tau_coeff != 0 else ""
82 |
83 | for i in range(self.k):
84 | for j in range(x_ind[i], x_ind[i+1]):
85 | sub_s += __sub_str(self.x_coeffs[ci, j], 'x')
86 | for j in range(y_ind[i], y_ind[i+1]):
87 | sub_s += __sub_str(self.y_coeffs[ci, j], 'y')
88 | for j in range(fu_ind[i], fu_ind[i+1]):
89 | sub_s += __sub_str(self.fu_coeffs[ci, j], 'fu')
90 | for j in range(fv_ind[i], fv_ind[i+1]):
91 | sub_s += __sub_str(self.fv_coeffs[ci, j], 'fv')
92 |
93 | if self.bound_type == BoundType.LowerBound:
94 | sub_s += f" >= {self.bound}\n"
95 | elif self.bound_type == BoundType.UpperBound:
96 | sub_s += f" <= {self.bound}\n"
97 | else:
98 | sub_s += f" == {self.bound}\n"
99 |
100 | s += sub_s
101 |
102 | return s
103 |
--------------------------------------------------------------------------------
/MIP_MCPP/graph_utils.py:
--------------------------------------------------------------------------------
1 | import random
2 |
3 | import matplotlib.pyplot as plt
4 | import networkx as nx
5 | from matplotlib.colors import rgb2hex
6 |
7 | from MIP_MCPP.misc import uv_sorted
8 |
9 |
10 | def nx_graph_read(filepath: str) -> nx.Graph:
11 | f = open(filepath, 'r')
12 | lines = f.readlines()
13 | node_items = lines[0].split('#')
14 |
15 | G = nx.Graph()
16 | for i, node_str in enumerate(node_items):
17 | pos_x, pos_y = node_str.strip().split(', ')
18 | pos = (float(pos_x[1:]), float(pos_y[:-1]))
19 | G.add_node(i, pos=pos)
20 |
21 | edge_items = lines[1:]
22 | for edge_str in edge_items:
23 | s, t, w = edge_str.strip().split('#')
24 | G.add_edge(int(s), int(t), weight=float(w))
25 |
26 | return G
27 |
28 |
29 | def graph_plot(
30 | G: nx.Graph, ax,
31 | graph_scale=1.0,
32 | marker_shape='.',
33 | color_rgba=(0.5, 0.5, 0.5, 1),
34 | with_node_labels=True,
35 | with_edge_labels=True
36 | ) -> None:
37 |
38 | color_hex = rgb2hex(color_rgba)
39 |
40 | pos_dict = nx.get_node_attributes(G, 'pos')
41 |
42 | if with_node_labels:
43 | nx.draw(
44 | G, pos_dict, ax=ax, with_labels=True,
45 | node_color="w", node_shape='o', edgecolors=color_hex, linewidths=2,
46 | edge_color=color_hex, width=2
47 | )
48 | else:
49 | nx.draw(
50 | G, pos_dict, ax=ax, with_labels=with_node_labels,
51 | node_color=color_hex, node_shape=marker_shape, edgecolors=color_hex, linewidths=1,
52 | edge_color=color_hex, width=1, node_size=24 * graph_scale
53 | )
54 | # nx.draw(
55 | # G, pos_dict, ax=ax, with_labels=with_node_labels,
56 | # node_color = color_hex, node_shape = '.', edgecolors = 'k', linewidths = 0.5,
57 | # edge_color = 'k', width = 1, node_size = 24 * graph_scale
58 | # )
59 |
60 | if with_edge_labels:
61 | edge_labels = {
62 | (u, v): f"{G[u][v]['weight']:.1f}" for u, v in list(G.edges())}
63 | nx.draw_networkx_edge_labels(
64 | G, pos=pos_dict, ax=ax, edge_labels=edge_labels)
65 |
66 |
67 | def create_grid_graph(n_rows: int, n_cols: int) -> nx.Graph:
68 | # Create an empty graph
69 | G = nx.Graph()
70 |
71 | def v_index(i, j): return i * n_cols + j
72 |
73 | # Add nodes with random negative weights and positions
74 | for i in range(n_rows):
75 | for j in range(n_cols):
76 | terrain_weight = random.uniform(1, 4)
77 | G.add_node(v_index(i, j), pos=(i, j),
78 | terrain_weight=round(terrain_weight, 3))
79 |
80 | # Add edges with random positive weights
81 | for i in range(n_rows):
82 | for j in range(n_cols):
83 | u = v_index(i, j)
84 | if i > 0:
85 | v = v_index(i-1, j)
86 | G.add_edge(u, v, weight=(
87 | G.nodes[u]["terrain_weight"]+G.nodes[v]["terrain_weight"])/2)
88 | if j > 0:
89 | v = v_index(i, j-1)
90 | G.add_edge(u, v, weight=(
91 | G.nodes[u]["terrain_weight"]+G.nodes[v]["terrain_weight"])/2)
92 |
93 | return G
94 |
95 |
96 | def remove_cycles(G: nx.Graph, Ti_edges):
97 | Ti_edges = set([uv_sorted((u, v)) for u, v in Ti_edges])
98 | Ti = G.edge_subgraph(Ti_edges).copy()
99 |
100 | while True:
101 | cycles = nx.cycle_basis(Ti)
102 | if cycles == []:
103 | break
104 |
105 | C = cycles[0]
106 | C_edges = [(C[i], C[(i+1) % len(C)]) for i in range(len(C))]
107 | u, v = sorted(
108 | C_edges, key=lambda e: Ti[e[0]][e[1]]["weight"], reverse=True)[0]
109 | Ti.remove_edge(u, v)
110 | Ti_edges.remove(uv_sorted((u, v)))
111 |
112 | return Ti, Ti_edges
113 |
114 |
115 | if __name__ == '__main__':
116 | import os
117 |
118 | import matplotlib.pyplot as plt
119 | G = nx_graph_read(os.path.join(
120 | 'graph_data', 'GRID_20x20_UNWEIGHTED_FREE.graph'))
121 | # find_all_cycles(G)
122 | C = nx.find_cycle(G)
123 | print(C)
124 | fig, ax = plt.subplots()
125 | graph_plot(G, ax, with_edge_labels=False)
126 | plt.show()
127 |
--------------------------------------------------------------------------------
/MIP_MCPP/heuristics.py:
--------------------------------------------------------------------------------
1 | import heapq
2 | from itertools import product
3 | from typing import Dict, Tuple
4 |
5 | import networkx as nx
6 | import numpy as np
7 |
8 | from MIP_MCPP.instance import Instance
9 |
10 |
11 | def connectivity_check(G: nx.Graph, r: int, verts_to_remove: set) -> set:
12 | V_H = set(G.nodes()) - verts_to_remove
13 | components = list(nx.connected_components(G.subgraph(V_H)))
14 |
15 | if len(components) != 1:
16 | # record the component index of each v in V_H
17 | v_in_comp, root_comp_ind = {}, -1
18 | for v in V_H:
19 | for i, c in enumerate(components):
20 | if v in c:
21 | if v == r:
22 | root_comp_ind = i
23 | else:
24 | v_in_comp[v] = i
25 | break
26 |
27 | # for each component c, get the nearset vertex of c to V_H
28 | nearest_comp_verts = {i: set() for i in range(len(components))}
29 | min_path_cost = [float("inf") for i in range(len(components))]
30 | min_path = [[] for i in range(len(components))]
31 | for v in verts_to_remove:
32 | for u in G.neighbors(v):
33 | if u in V_H and u not in components[root_comp_ind]:
34 | comp_ind = v_in_comp[u]
35 | if u not in nearest_comp_verts[comp_ind]:
36 | nearest_comp_verts[comp_ind].add(u)
37 | path = nx.shortest_path(G, r, u, "weight")
38 | path_cost = sum([G[path[i]][path[i+1]]["weight"]
39 | for i in range(len(path)-1)])
40 | if path_cost < min_path_cost[comp_ind]:
41 | min_path_cost[comp_ind], min_path[comp_ind] = path_cost, path
42 |
43 | for path in min_path:
44 | verts_to_remove = verts_to_remove - set(path)
45 |
46 | return verts_to_remove
47 |
48 |
49 | """ Parabolic Removal Heuristics """
50 |
51 |
52 | def get_endpoint_ray(x_lb, x_ub, y_lb, y_ub, pos_s, pos_t, pos_dict) -> Tuple[float, float]:
53 |
54 | px_s, py_s = pos_s
55 | px_t, py_t = pos_t
56 | dx, dy = px_t - px_s, py_t - py_s
57 | def k(): return dy / dx
58 | def b(): return py_t - k() * px_t
59 |
60 | if (dy >= dx >= 0) or (dy > 0 >= dx):
61 | for y in np.linspace(y_ub, py_t, num=y_ub-py_t+1, endpoint=True):
62 | x = round((y - b()) / k()) if dx != 0 else px_t
63 | x = x_lb if x_lb == x + 1 else (x_ub if x_ub == x - 1 else x)
64 | if (x, y) in pos_dict:
65 | return (int(x), int(y))
66 | elif (dy <= dx <= 0) or (dy < 0 <= dx):
67 | for y in np.linspace(y_lb, py_t, num=py_t-y_lb+1, endpoint=True):
68 | x = round((y - b()) / k()) if dx != 0 else px_t
69 | x = x_lb if x_lb == x + 1 else (x_ub if x_ub == x - 1 else x)
70 | if (x, y) in pos_dict:
71 | return (int(x), int(y))
72 | elif (dx > dy >= 0) or (dx > 0 >= dy):
73 | for x in np.linspace(x_ub, px_t, num=x_ub-px_t+1, endpoint=True):
74 | y = round(k() * x + b()) if dy != 0 else py_t
75 | y = y_lb if y_lb == y + 1 else (y_ub if y_ub == y - 1 else y)
76 | if (x, y) in pos_dict:
77 | return (int(x), int(y))
78 | elif (dx < dy <= 0) or (dx < 0 <= dy):
79 | for x in np.linspace(x_lb, px_t, num=px_t-x_lb+1, endpoint=True):
80 | y = round(k() * x + b()) if dy != 0 else py_t
81 | y = y_lb if y_lb == y + 1 else (y_ub if y_ub == y - 1 else y)
82 | if (x, y) in pos_dict:
83 | return (int(x), int(y))
84 |
85 | return (px_t, py_t)
86 |
87 |
88 | def heur_parabolic(istc: Instance, alpha: float) -> Tuple[dict, dict, dict]:
89 | assert(alpha >= 0)
90 |
91 | V = {r: set() for r in istc.R}
92 | Pxr = {r: {} for r in istc.R}
93 | Pyr = {r: {} for r in istc.R}
94 |
95 | pos = nx.get_node_attributes(istc.G, "pos")
96 | pos2v = {p: v for v, p in pos.items()}
97 | x_lb, y_lb, x_ub, y_ub = istc.bounds
98 |
99 | def parabola(x, a, b): return a * (x - b[0])**2 + b[1]
100 | d2r = {r: nx.shortest_path_length(
101 | istc.G, source=r, weight="weight") for r in istc.R}
102 | degrees = nx.degree(istc.G)
103 | B = [v for v in istc.V if degrees[v] < 4]
104 |
105 | def sigmoid(x): return 1 / (1 + np.exp(-x))
106 |
107 | for ri, rj in product(istc.R, istc.R):
108 |
109 | if ri == rj:
110 | continue
111 |
112 | d1 = nx.shortest_path_length(istc.G, ri, rj)
113 | cij = sorted(B, key=lambda v: d2r[ri][v] - d2r[rj][v], reverse=True)[0]
114 | d2 = nx.shortest_path_length(istc.G, rj, cij)
115 |
116 | verts_to_remove = set()
117 | a = (alpha * sigmoid(d2/d1))**2
118 | rot = np.arctan2(pos[ri][1]-pos[rj][1], pos[ri]
119 | [0]-pos[rj][0]) + np.pi/2
120 | for p in product(range(x_lb, x_ub+1), range(y_lb, y_ub+1)):
121 | if p not in pos2v:
122 | continue
123 |
124 | v = pos2v[p]
125 | if v not in istc.V:
126 | continue
127 |
128 | # Rotate the point around rj
129 | xr = pos[rj][0] + np.cos(-rot)*(p[0] - pos[rj][0]) - \
130 | np.sin(-rot)*(p[1] - pos[rj][1])
131 | yr = pos[rj][1] + np.sin(-rot)*(p[0] - pos[rj][0]) + \
132 | np.cos(-rot)*(p[1] - pos[rj][1])
133 |
134 | if yr >= parabola(xr, a, pos[rj]):
135 | verts_to_remove.add(v)
136 |
137 | V[ri] = V[ri].union(verts_to_remove)
138 |
139 | px = np.linspace(x_lb-10, x_ub+10, num=1000)
140 | py = parabola(px, a, pos[rj])
141 | pxr = pos[rj][0] + np.cos(rot)*(px - pos[rj][0]) - \
142 | np.sin(rot)*(py - pos[rj][1])
143 | pyr = pos[rj][1] + np.sin(rot)*(px - pos[rj][0]) + \
144 | np.cos(rot)*(py - pos[rj][1])
145 | Pxr[ri][rj] = pxr
146 | Pyr[ri][rj] = pyr
147 |
148 | for r in istc.R:
149 | V[r] = connectivity_check(istc.G, r, V[r])
150 |
151 | return V, Pxr, Pyr
152 |
153 |
154 | """ Subgraph Removal Heuristics """
155 |
156 |
157 | def get_separation_graph(G: nx.Graph, ri: int, rj: int) -> nx.Graph:
158 |
159 | S = set()
160 | dist2i = nx.shortest_path_length(G, source=ri, weight="weight")
161 | dist2j = nx.shortest_path_length(G, source=rj, weight="weight")
162 | for v in G.nodes():
163 | if dist2i[v] > dist2j[v]:
164 | S.add(v)
165 |
166 | return G.subgraph(S)
167 |
168 |
169 | def farthest_first_search(G: nx.Graph, c: int, n_max_verts: int, cost2r) -> set:
170 | visited = set([c])
171 |
172 | Q = [(-cost2r(c), c)]
173 | while Q and len(visited) <= n_max_verts:
174 | _, v = heapq.heappop(Q)
175 |
176 | if v in G.nodes():
177 | for u in G.neighbors(v):
178 | if u not in visited:
179 | visited.add(u)
180 | heapq.heappush(Q, (-cost2r(u), u))
181 |
182 | return visited
183 |
184 |
185 | def heur_subgraph(istc: Instance, beta: float = 1.0) -> Tuple[dict, dict, dict]:
186 |
187 | verts_to_remove = {r: set() for r in istc.R}
188 | Cs = {r: [] for r in istc.R}
189 | Ss = {r: {} for r in istc.R}
190 |
191 | def sigmoid(x): return 1 / (1 + np.exp(-x))
192 | d2r = {r: nx.shortest_path_length(
193 | istc.G, source=r, weight="weight") for r in istc.R}
194 | degrees = nx.degree(istc.G)
195 | B = [v for v in istc.V if degrees[v] < 4]
196 |
197 | for ri, rj in product(istc.R, istc.R):
198 | if ri == rj:
199 | continue
200 |
201 | Sij = get_separation_graph(istc.G, ri, rj)
202 |
203 | d1 = nx.shortest_path_length(istc.G, ri, rj)
204 | cij = sorted(B, key=lambda v: d2r[ri][v] - d2r[rj][v], reverse=True)[0]
205 | d2 = nx.shortest_path_length(istc.G, rj, cij)
206 |
207 | n_verts_lim = round(len(Sij.nodes()) * beta * sigmoid(d1/(1e-6+d2)))
208 |
209 | M_verts = farthest_first_search(
210 | Sij, cij, n_verts_lim,
211 | cost2r=lambda v: nx.shortest_path_length(
212 | istc.G, v, ri, weight="weight")
213 | )
214 |
215 | Cs[ri].append(cij)
216 | Ss[ri][rj] = list(Sij.nodes)
217 | verts_to_remove[ri] = verts_to_remove[ri].union(M_verts)
218 |
219 | for r in istc.R:
220 | verts_to_remove[r] = connectivity_check(istc.G, r, verts_to_remove[r])
221 |
222 | return verts_to_remove, Cs, Ss
223 |
--------------------------------------------------------------------------------
/MIP_MCPP/instance.py:
--------------------------------------------------------------------------------
1 | from __future__ import annotations
2 |
3 | import os
4 | import pickle
5 | import random
6 | from itertools import product
7 | from typing import Tuple
8 |
9 | import matplotlib.pyplot as plt
10 | import networkx as nx
11 | import numpy as np
12 |
13 | from MIP_MCPP.graph_utils import create_grid_graph, graph_plot
14 | from MIP_MCPP.misc import PLT_SHAPES, colormap, uv_sorted
15 |
16 |
17 | class Instance:
18 |
19 | """ Problem Instance Class """
20 | DEFAULT_INSTANCE_DIR = os.path.join("data", "instances")
21 | DEFAULT_SOLUTION_DIR = os.path.join("data", "solutions")
22 | DEFAULT_IMAGE_DIR = os.path.join("data", "figs")
23 |
24 | def __init__(self, G: nx.Graph, R: list, name: str) -> None:
25 | self.k = len(R)
26 | self.n, self.m = G.number_of_nodes(), G.number_of_edges()
27 | self.name = name
28 |
29 | grid_spec = self.name.split("-")[0]
30 | self.width, self.height = [int(v) for v in grid_spec.split("x")]
31 |
32 | self.G = G
33 | self.R = R
34 | self.I = list(range(self.k))
35 | self.V = list(range(self.n))
36 | self.E = list(range(self.m))
37 |
38 | w_dict = nx.get_edge_attributes(G, "weight")
39 | self.uv2e = {uv_sorted(uv): e for e, uv in enumerate(w_dict.keys())}
40 | self.e2uv = [uv_sorted(uv) for uv in G.edges()]
41 | self.w = np.array(list(w_dict.values()))
42 |
43 | def draw_instance(self, ax, marker_scale=1.0, root=True, obstacle=True) -> None:
44 | pos = nx.get_node_attributes(self.G, "pos")
45 | pos2v = {p: v for v, p in pos.items()}
46 | x_lb, y_lb, x_ub, y_ub = self.bounds
47 |
48 | graph_plot(
49 | self.G, ax,
50 | graph_scale=marker_scale,
51 | color_rgba=(0.5, 0.5, 0.5, 1),
52 | with_node_labels=False, with_edge_labels=False)
53 |
54 | if root:
55 | for r in self.R:
56 | ax.scatter(pos[r][0], pos[r][1], 64 *
57 | marker_scale, marker='o', color='r')
58 |
59 | if obstacle:
60 | for p in product(range(x_lb, x_ub+1), range(y_lb, y_ub+1)):
61 | if p not in pos2v:
62 | ax.scatter(p[0], p[1], marker='s',
63 | s=200*marker_scale, color='k')
64 |
65 | ax.axis("equal")
66 | ax.set_xlim(x_lb-1, x_ub+1)
67 | ax.set_ylim(y_lb-1, y_ub+1)
68 | ax.set_xlabel("")
69 | ax.set_ylabel("")
70 | ax.axis("off")
71 | # ax.xaxis.set_ticks(np.linspace(x_lb, x_ub, (x_ub-x_lb+1)//2))
72 | # ax.yaxis.set_ticks(np.linspace(y_lb, y_ub, (y_ub-y_lb+1)//2))
73 | # ax.xaxis.set_ticklabels([])
74 | # ax.yaxis.set_ticklabels([])
75 | # ax.grid(True, color='gray', linestyle='--', linewidth=0.25)
76 |
77 | def save_instance_image(self, filename, dir: str = DEFAULT_IMAGE_DIR) -> None:
78 | path = os.path.join(
79 | dir, self.name + '.png' if filename is None else filename)
80 | fig, ax = plt.subplots()
81 | self.draw_instance(ax, obstacle=False)
82 | fig.set_size_inches(20, 20)
83 | fig.tight_layout()
84 | fig.savefig(path, dpi=200)
85 |
86 | def draw_solution(self, edges, ax, graph_scale=3.0) -> None:
87 | pos_dict = nx.get_node_attributes(self.G, 'pos')
88 | arrowprops = dict(facecolor='black', shrink=0.05,
89 | width=0.1, headwidth=0.1)
90 | for r in self.R:
91 | xy = (pos_dict[r][0]+0.1, pos_dict[r][1]+0.1)
92 | xytext = (pos_dict[r][0]+0.25, pos_dict[r][1]+0.25)
93 | # ax.annotate(text='R', xy=xy, xytext=xytext, arrowprops=arrowprops)
94 |
95 | cmap = colormap("tab20")
96 | for i in self.I:
97 | Ti_e = self.G.edge_subgraph(edges[i]) if edges[i] != [
98 | ] else self.G.subgraph(self.R[i])
99 | graph_plot(
100 | Ti_e, ax,
101 | graph_scale=graph_scale,
102 | marker_shape=PLT_SHAPES[i % self.k],
103 | color_rgba=cmap(i/self.k),
104 | with_node_labels=False, with_edge_labels=False
105 | )
106 |
107 | @property
108 | def bounds(self) -> Tuple[float, float, float, float]:
109 | pos = nx.get_node_attributes(self.G, "pos")
110 | x_lb = min([p[0] for p in list(pos.values())])
111 | y_lb = min([p[1] for p in list(pos.values())])
112 | x_ub = max([p[0] for p in list(pos.values())])
113 | y_ub = max([p[1] for p in list(pos.values())])
114 | return x_lb, y_lb, x_ub, y_ub
115 |
116 | @staticmethod
117 | def varname(k: int, pool_size: int) -> str:
118 | return f"lambda{k}_{pool_size}"
119 |
120 | @staticmethod
121 | def read(filename: str, dir: str = DEFAULT_INSTANCE_DIR) -> Instance:
122 | with open(os.path.join(dir, filename), 'rb') as f:
123 | istc = pickle.load(f)
124 | return istc
125 |
126 | def write(self, filename: str = None, dir: str = DEFAULT_INSTANCE_DIR) -> None:
127 | path = os.path.join(
128 | dir, self.name+'.istc' if filename is None else filename)
129 | with open(path, 'wb') as f:
130 | pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
131 | print(f"Wrote instance to {path}")
132 |
133 | @staticmethod
134 | def write_solution(edges: list, filename: str, dir: str = DEFAULT_SOLUTION_DIR) -> None:
135 | with open(os.path.join(dir, filename), 'wb') as f:
136 | pickle.dump(edges, f, pickle.HIGHEST_PROTOCOL)
137 |
138 | @staticmethod
139 | def read_solution(filename: str, dir: str = DEFAULT_SOLUTION_DIR) -> list:
140 | with open(os.path.join(dir, filename+'.solu'), 'rb') as f:
141 | edges = pickle.load(f)
142 | return edges
143 |
144 | @staticmethod
145 | def create_random_free(name, R=None) -> Instance:
146 | items = name.split("-")
147 | width, height = [int(v) for v in items[0].split("x")]
148 | k = int(items[-1][1:])
149 |
150 | G = create_grid_graph(width, height)
151 |
152 | if R is None:
153 | n = G.number_of_nodes()
154 | R = [random.randint(0, n-1) for _ in range(k)]
155 |
156 | return Instance(G, R, name)
157 |
158 | @staticmethod
159 | def create_from_binary_map(filename: str) -> Instance:
160 | OBS = (0, 0, 0)
161 | ROOT = (1, 0, 0)
162 | map = plt.imread(filename)
163 | width, height, _ = map.shape
164 | def cvt_idx(x, y): return (y, width-x)
165 |
166 | G, R = nx.Graph(), []
167 | num_nodes = 0
168 | pos2v = {}
169 | for x in range(width):
170 | for y in range(height):
171 | if tuple(map[x][y]) != OBS:
172 | G.add_node(num_nodes, pos=cvt_idx(x, y))
173 | pos2v[cvt_idx(x, y)] = num_nodes
174 | if tuple(map[x][y]) == ROOT:
175 | R.append(num_nodes)
176 | num_nodes += 1
177 |
178 | for x in range(width):
179 | for y in range(height):
180 | for inc_x, inc_y in [(-1, 0), (1, 0), (0, 1), (0, -1)]:
181 | xc, yc = x + inc_x, y + inc_y
182 | if cvt_idx(x, y) in pos2v and cvt_idx(xc, yc) in pos2v:
183 | G.add_edge(pos2v[cvt_idx(x, y)],
184 | pos2v[cvt_idx(xc, yc)], weight=1)
185 |
186 | istc_name = filename.split("/")[-1]
187 | return Instance(G, R, istc_name.split(".")[0])
188 |
189 | def __str__(self) -> str:
190 | return ",\t".join([
191 | self.name,
192 | f"% of obs.={1 - self.n/(self.width*self.height):.3f}",
193 | f"# of verts={self.n}",
194 | f"# of edges={self.m}",
195 | f"# of vars.={(self.n + 3*self.m)*self.k + 1}"
196 | ])
197 |
198 | def draw_covering_nodes(self, ax, graph_scale=1.0) -> None:
199 | pos = nx.get_node_attributes(self.G, "pos")
200 | for v in self.G.nodes():
201 | for xc, yc in [(0.25, -0.25), (0.25, 0.25), (-0.25, 0.25), (-0.25, -0.25)]:
202 | _nx, _ny = pos[v][0] + xc, pos[v][1] + yc
203 | ax.plot(_nx, _ny, "o", mfc="w", mec="k",
204 | ms=0.5*graph_scale, alpha=0.5)
205 |
--------------------------------------------------------------------------------
/MIP_MCPP/mcpp_planner.py:
--------------------------------------------------------------------------------
1 | import time
2 | from typing import Tuple, List
3 |
4 | import matplotlib
5 | import matplotlib.pyplot as plt
6 | import networkx as nx
7 |
8 | from MSTC_Star.mcpp.mfc_planner import MFCPlanner
9 | from MSTC_Star.mcpp.mstc_star_planner import MSTCStarPlanner
10 | from MSTC_Star.mcpp.rtc_planner import RTCPlanner
11 | from MSTC_Star.mcpp.stc_planner import STCPlanner
12 | from MSTC_Star.utils.nx_graph import mst, navigate
13 | from MSTC_Star.utils.robot import Robot
14 | from MIP_MCPP.graph_utils import graph_plot
15 | from MIP_MCPP.instance import Instance
16 |
17 |
18 | def simulate(
19 | name: str,
20 | planner: STCPlanner,
21 | paths: list,
22 | weights: list,
23 | scale: float,
24 | dt: float,
25 | obs_graph: nx.Graph,
26 | is_write: bool = False,
27 | is_show: bool = False
28 | ) -> None:
29 |
30 | k, R = planner.k, planner.R
31 | color = ['r', 'm', 'b', 'k', 'c', 'g']
32 | fig = plt.figure()
33 | fig.set_size_inches(8*scale, 8*scale)
34 | fig.tight_layout()
35 | ax = plt.axes()
36 | # ax.margins(x=0.15, y=0.15)
37 | ax.axes.xaxis.set_ticklabels([])
38 | ax.axes.yaxis.set_ticklabels([])
39 | plt.grid(True)
40 | plt.gcf().canvas.mpl_connect(
41 | 'key_release_event',
42 | lambda event: [exit(0) if event.key == 'escape' else None])
43 |
44 | robots = [Robot(paths[i], planner.H) for i in range(k)]
45 | t_finish = [robots[i].T[-1] for i in range(k)]
46 | t_max = max(t_finish)
47 |
48 | if not is_write and not is_show:
49 | print(f'Final Max Weights: {max(weights)}')
50 | return
51 |
52 | lines, markers, texts = [None]*k, [None]*k, [None]*(k+1)
53 | xs_vec, ys_vec = [None]*k, [None]*k
54 |
55 | def init():
56 | plt.title(f'{name} (Max Weights={max(weights): .2f})',
57 | fontdict={'size': 12*scale})
58 | texts[-1] = ax.text(
59 | 1, 1, '', va='top', ha='right', transform=ax.transAxes,
60 | font={'size': 8*scale})
61 |
62 | # MST of spanning graph
63 | M = mst(planner.G)
64 | for s, t in M.edges():
65 | x1, y1 = s
66 | x2, y2 = t
67 | ax.plot([x1, x2], [y1, y2], 'ok', mfc='r')
68 | # covering nodes
69 | rho = planner.generate_cover_trajectory(R[0], mst(planner.G))
70 | for cn_x, cn_y in rho:
71 | ax.plot(cn_x, cn_y, 'o', mec='k', mfc='w', ms=5)
72 | # obstacle graph
73 | for s, t in obs_graph.edges():
74 | x1, y1 = s
75 | x2, y2 = t
76 | ax.plot([x1, x2], [y1, y2], '-xk', ms=10, mew=3)
77 |
78 | for i in range(k):
79 | c = color[i % len(color)]
80 | line, = ax.plot([], [], '-'+c, alpha=0.35, lw=8)
81 | marker, = ax.plot([], [], 'o'+c, ms=8)
82 | # changable texts
83 | texts[i] = ax.text(
84 | 1, 0.975-i*0.025, '', va='top', ha='right',
85 | transform=ax.transAxes, font={'size': 8})
86 | # trajectories and robots
87 | lines[i], markers[i] = line, marker
88 | xs_vec[i], ys_vec[i] = zip(*paths[i])
89 | # depots
90 | ax.plot(R[i][0], R[i][1], '*k', mfc=c, ms=10)
91 | # ax.text(R[i][0]+0.1, R[i][1]+0.1, f'R{i}')
92 |
93 | return lines + markers + texts
94 |
95 | # record remaining uncovered nodes
96 | uncovered = set()
97 | direction = ['SE', 'NE', 'NW', 'SW']
98 | for node in planner.G.nodes:
99 | for sn in [planner.__get_subnode_coords__(node, d) for d in direction]:
100 | uncovered.add(sn)
101 |
102 | def animate(ti):
103 | ts = ti * dt
104 | for i in range(k):
105 | last_coord_idx, cur_state = robots[i].get_cur_state(ts)
106 | xs = xs_vec[i][:last_coord_idx+1] + (cur_state.x, )
107 | ys = ys_vec[i][:last_coord_idx+1] + (cur_state.y, )
108 | # texts[i].set_text(f'R{i}: ')
109 | lines[i].set_data(xs, ys)
110 | markers[i].set_data(cur_state.x, cur_state.y)
111 | node = (xs_vec[i][last_coord_idx], ys_vec[i][last_coord_idx])
112 | if node in uncovered:
113 | uncovered.remove(node)
114 | texts[-1].set_text(f'T[s]={ts: .2f}, # of uncovered={len(uncovered)}')
115 |
116 | return lines + markers + texts
117 |
118 | anim = matplotlib.animation.FuncAnimation(
119 | fig, animate, int(t_max/dt), init, interval=5,
120 | blit=True, repeat=False, cache_frame_data=False)
121 |
122 | if is_write:
123 | anim.save(f'data/sim_records/{name}.mp4', fps=300, dpi=200,
124 | progress_callback=lambda i, n: print(f'{name}: saving frame {i}/{n}'))
125 |
126 | if is_show:
127 | plt.show()
128 |
129 |
130 | def mfc_plan(istc: Instance, ax=None, graph_scale=3.0
131 | ) -> Tuple[MFCPlanner, list, list, float]:
132 |
133 | pos = nx.get_node_attributes(istc.G, "pos")
134 | tw = nx.get_node_attributes(istc.G, "terrain_weight")
135 | pos2v = {p: v for v, p in pos.items()}
136 |
137 | G = nx.Graph()
138 | for u, v in istc.G.edges():
139 | G.add_edge(pos[u], pos[v], weight=istc.G[u][v]["weight"])
140 |
141 | nx.set_node_attributes(
142 | G, {pos[v]: w for v, w in tw.items()}, "terrain_weight")
143 | R = [pos[r] for r in istc.R]
144 | cap = float("inf")
145 |
146 | planner = MFCPlanner(G, istc.k, R, cap)
147 | rtc_planner = RTCPlanner(planner.G, planner.R, planner.k)
148 |
149 | ts0 = time.time()
150 | match_tuple, max_weights, opt_B = rtc_planner.k_tree_cover()
151 | ts1 = time.time()
152 |
153 | sol_edges = {r:set() for r in R}
154 | for r, val in match_tuple.items():
155 | L, S, P = val
156 | for idx in range(len(P)-1):
157 | sol_edges[r].add((pos2v[P[idx]], pos2v[P[idx+1]]))
158 | for u, v in L.edges():
159 | sol_edges[r].add((pos2v[u], pos2v[v]))
160 | for u, v in S.edges():
161 | sol_edges[r].add((pos2v[u], pos2v[v]))
162 |
163 | paths, weights = STC_on_MMRTC_sol(planner, istc, [sol_edges[pos[r]] for r in istc.R])
164 |
165 | for i, t in enumerate(paths):
166 | tx, ty = zip(*t)
167 | if ax:
168 | ax.plot(tx, ty, lw=4*graph_scale, alpha=0.3)
169 |
170 | if ax:
171 | istc.draw_solution(sol_edges, ax, graph_scale)
172 |
173 | print(f"Planning Time = {ts1-ts0} secs")
174 |
175 | return planner, paths, weights, ts1-ts0
176 |
177 |
178 | def mstcstar_plan(istc: Instance, ax=None, graph_scale=3.0
179 | ) -> Tuple[MSTCStarPlanner, list, list, float]:
180 | pos = nx.get_node_attributes(istc.G, "pos")
181 | tw = nx.get_node_attributes(istc.G, "terrain_weight")
182 |
183 | G = nx.Graph()
184 | for u, v in istc.G.edges():
185 | G.add_edge(pos[u], pos[v], weight=istc.G[u][v]["weight"])
186 |
187 | nx.set_node_attributes(
188 | G, {pos[v]: w for v, w in tw.items()}, "terrain_weight")
189 | R = [pos[r] for r in istc.R]
190 | cap = float("inf")
191 |
192 | planner = MSTCStarPlanner(G, istc.k, R, cap, True)
193 | ts0 = time.time()
194 | plans = planner.allocate()
195 | ts1 = time.time()
196 |
197 | paths, weights = planner.simulate(plans, False)
198 | for i, t in enumerate(paths):
199 | tx, ty = zip(*t)
200 | if ax:
201 | ax.plot(tx, ty, lw=4*graph_scale, alpha=0.3)
202 |
203 | if ax:
204 | M = nx.minimum_spanning_tree(istc.G)
205 | graph_plot(M, ax, graph_scale=3*graph_scale,
206 | with_node_labels=False, with_edge_labels=False)
207 |
208 | print(f"Planning Time = {ts1-ts0} secs")
209 |
210 | return planner, paths, weights, ts1-ts0
211 |
212 |
213 | class MIPPlanner(STCPlanner):
214 |
215 | def __init__(self, istc: Instance) -> None:
216 | self.istc = istc
217 |
218 | pos = nx.get_node_attributes(self.istc.G, "pos")
219 | tw = nx.get_node_attributes(istc.G, "terrain_weight")
220 |
221 | G = nx.Graph()
222 | for u, v in self.istc.G.edges():
223 | G.add_edge(pos[u], pos[v], weight=self.istc.G[u][v]["weight"])
224 |
225 | nx.set_node_attributes(
226 | G, {pos[v]: w for v, w in tw.items()}, "terrain_weight")
227 |
228 | self.R = [pos[r] for r in self.istc.R]
229 | self.k = istc.k
230 | self.G = G
231 | self.H = self.generate_decomposed_graph(G, self.R)
232 |
233 | def simulate(self, sol_edges) -> Tuple[List, List]:
234 | return STC_on_MMRTC_sol(self, self.istc, sol_edges)
235 |
236 |
237 | def mip_plan(istc: Instance, sol_edges, ax=None, graph_scale=3.0
238 | ) -> Tuple[MIPPlanner, list, list, float]:
239 | planner = MIPPlanner(istc)
240 | paths, weights = planner.simulate(sol_edges)
241 |
242 | for i, t in enumerate(paths):
243 | tx, ty = zip(*t)
244 | if ax:
245 | ax.plot(tx, ty, lw=4*graph_scale, alpha=0.3)
246 |
247 | if ax:
248 | istc.draw_solution(sol_edges, ax, graph_scale)
249 |
250 | return planner, paths, weights
251 |
252 |
253 | def STC_on_MMRTC_sol(planner:STCPlanner, istc:Instance, sol_edges):
254 | pos = nx.get_node_attributes(istc.G, "pos")
255 | traj, weights = [[] for i in istc.I], [0 for _ in istc.I]
256 | for i, r in enumerate(istc.R):
257 | Ti = nx.Graph()
258 | for u, v in sol_edges[i]:
259 | Ti.add_edge(pos[u], pos[v], weight=istc.G[u][v]["weight"])
260 |
261 | if Ti.number_of_edges() != 0:
262 | pi = planner.generate_cover_trajectory(pos[r], Ti)
263 | else:
264 | pi = [planner.__get_subnode_coords__(pos[r], d) for d in ["SE", "NE", "NW", "SW"]]
265 |
266 | traj[i] = [pos[r]] + pi + [pos[r]]
267 | weights[i] = planner.__get_travel_weights__(traj[i])
268 |
269 | return traj, weights
270 |
--------------------------------------------------------------------------------
/MIP_MCPP/misc.py:
--------------------------------------------------------------------------------
1 | import matplotlib
2 |
3 | SEP_LITERAL = "="*50 + "\n"
4 | PLT_SHAPES = ['o', 'p', "X", "s", 'p', 'P',
5 | '*', 'v', '^', '<', '>', '+', "x", "h"]
6 |
7 | colormap = lambda name='Accent': matplotlib.cm.get_cmap(name)
8 |
9 |
10 | def uv_sorted(e): return (e[0], e[1]) if e[0] < e[1] else (e[1], e[0])
11 |
--------------------------------------------------------------------------------
/MIP_MCPP/model.py:
--------------------------------------------------------------------------------
1 | from collections import defaultdict
2 | from typing import List, Tuple
3 |
4 | import gurobipy as gp
5 | import networkx as nx
6 | import numpy as np
7 | from gurobipy import GRB
8 |
9 | from MIP_MCPP.constraints import BoundType as BT
10 | from MIP_MCPP.constraints import Constraints
11 | from MIP_MCPP.heuristics import heur_parabolic, heur_subgraph
12 | from MIP_MCPP.instance import Instance
13 | from MIP_MCPP.misc import SEP_LITERAL, uv_sorted
14 |
15 |
16 | class Model:
17 | """ MMRTC Model """
18 |
19 | def __init__(self, istc:Instance) -> None:
20 | self.istc = istc
21 |
22 | def _init_var_info(self, H:List[nx.Graph]) -> None:
23 | self.num_vars = 1
24 | self.num_v, self.num_e = [], []
25 |
26 | for i in self.istc.I:
27 | self.num_e.append(self.istc.m if H is None else H[i].number_of_edges())
28 | self.num_v.append(self.istc.n if H is None else H[i].number_of_nodes())
29 | self.num_vars += self.num_v[-1] + 3 * self.num_e[-1]
30 |
31 | self.e_ind = [0] + np.cumsum(self.num_e).tolist()
32 | self.v_ind = [0] + np.cumsum(self.num_v).tolist()
33 |
34 | def _init_constrs(self, H:List[nx.Graph]) -> None:
35 | if H is None:
36 | H = [self.istc.G for i in self.istc.I]
37 |
38 | self.v2id, self.id2v, self.e2id, self.id2e = [], [], [], []
39 | for i in self.istc.I:
40 | self.v2id.append({v:idx for idx, v in enumerate(H[i].nodes())})
41 | self.id2v.append({idx:v for idx, v in enumerate(H[i].nodes())})
42 | self.e2id.append({self.istc.uv2e[uv_sorted(uv)]:idx for idx, uv in enumerate(H[i].edges())})
43 | self.id2e.append({idx:self.istc.uv2e[uv_sorted(uv)] for idx, uv in enumerate(H[i].edges())})
44 |
45 | self.num_constrs = 3 * self.istc.k + self.istc.n + sum(self.num_e) + sum(self.num_v)
46 | self.C_makespan = Constraints('makespan', self.istc.k, 0, BT.UpperBound, self.num_e, self.num_v)
47 | self.C_rooted = Constraints('rooted', self.istc.k, 1, BT.EqualBound, self.num_e, self.num_v)
48 | self.C_tree = Constraints('tree', self.istc.k, 1, BT.EqualBound, self.num_e, self.num_v)
49 | self.C_cover = Constraints('cover', self.istc.n, 1, BT.LowerBound, self.num_e, self.num_v)
50 | self.flow = Constraints('flow', sum(self.num_e), 0, BT.EqualBound, self.num_e, self.num_v)
51 | self.C_acyclic = Constraints('acyclic', sum(self.num_v), 1-1/self.istc.n, BT.UpperBound, self.num_e, self.num_v)
52 | self.C_y_defs = []
53 |
54 | # -----------------------------------------------------------------------------
55 | for i in self.istc.I:
56 | # CONSTRAINTS(makespan): sum_e{w_e * x_e^i} - tau <= 0
57 | self.C_makespan.tau_coeff = -1
58 | self.C_makespan.x_coeffs[i, self.e_ind[i]:self.e_ind[i+1]] = \
59 | list(nx.get_edge_attributes(H[i], "weight").values())
60 |
61 | # CONSTRAINTS(rooted): y_ri^i = 1
62 | self.C_rooted.y_coeffs[i, self.v_ind[i] + self.v2id[i][self.istc.R[i]]] = 1
63 |
64 | # CONSTRAINTS(tree): -sum_e{x_e^i} + sum_v{y_v^i} = 1
65 | self.C_tree.x_coeffs[i, self.e_ind[i]:self.e_ind[i+1]] = -1
66 | self.C_tree.y_coeffs[i, self.v_ind[i]:self.v_ind[i+1]] = 1
67 |
68 | # -----------------------------------------------------------------------------
69 | for v in self.istc.V:
70 | for i in self.istc.I:
71 | if v in self.v2id[i]:
72 | # CONSTRAINTS(cover): sum_i{y_v^i} >= 1
73 | self.C_cover.y_coeffs[v, self.v_ind[i]+self.v2id[i][v]] = 1
74 |
75 | # -----------------------------------------------------------------------------
76 | for i in self.istc.I:
77 | for e in self.istc.E:
78 | if e in self.e2id[i]:
79 | e_id = self.e_ind[i] + self.e2id[i][e]
80 | # CONSTRAINTS(flow): - x_e^i + fu_e^i + fv_e^i = 0
81 | self.flow.x_coeffs[e_id, e_id] = -1
82 | self.flow.fu_coeffs[e_id, e_id] = 1
83 | self.flow.fv_coeffs[e_id, e_id] = 1
84 |
85 | # -----------------------------------------------------------------------------
86 | v_ngbs_edge = [defaultdict(list) for _ in self.istc.I]
87 | for i in self.istc.I:
88 | for v in self.istc.V:
89 | if H[i].has_node(v):
90 | for u in H[i].neighbors(v):
91 | uv = uv_sorted((u, v))
92 | v_ngbs_edge[i][v].append((uv, self.istc.uv2e[uv]))
93 |
94 | for v in self.istc.V:
95 | for i in self.istc.I:
96 | if len(v_ngbs_edge[i][v]) == 0:
97 | continue
98 |
99 | self.num_constrs += len(v_ngbs_edge[i][v])
100 | C_y_def = Constraints(f'y_def_({i},{v})', len(v_ngbs_edge[i][v]), 0, BT.UpperBound, self.num_e, self.num_v)
101 | for j, val in enumerate(v_ngbs_edge[i][v]):
102 | uv, e = val
103 | v_id = self.v_ind[i] + self.v2id[i][v]
104 | e_id = self.e_ind[i] + self.e2id[i][e]
105 | # CONSTRAINTS(acyclic): sum_e{fv_e^i} <= 1 - 1 / |V|
106 | if v == uv[0]:
107 | self.C_acyclic.fu_coeffs[v_id, e_id] = 1
108 | else:
109 | self.C_acyclic.fv_coeffs[v_id, e_id] = 1
110 |
111 | # CONSTRAINTS(y_def): x_e^i - y_v^i <= 0
112 | C_y_def.x_coeffs[j, e_id] = 1
113 | C_y_def.y_coeffs[j, v_id] = -1
114 |
115 | self.C_y_defs.append(C_y_def)
116 |
117 | def _init_model_params(self, args:dict):
118 | """ Use GUROBI to solve the MMRTC instance """
119 | model = gp.Model("MMRTC")
120 |
121 | model.params.Threads = int(args["Threads"])
122 | model.params.OptimalityTol = float(args["OptimalityTol"])
123 | if args.get("TimeLimit"):
124 | model.params.TimeLimit = float(args["TimeLimit"])
125 | if args.get("SoftMemLimit"):
126 | model.params.SoftMemLimit = float(args["SoftMemLimit"])
127 |
128 | print(
129 | "\n" + SEP_LITERAL + \
130 | f"Number of trees = {self.istc.k}\n" + \
131 | f"Number of verts = {self.istc.n}\n" + \
132 | f"Number of edges = {self.istc.m}\n" + \
133 | f"Number of variables = {self.num_vars}\n" + \
134 | f"Number of constraints = {self.num_constrs}\n" + \
135 | SEP_LITERAL
136 | )
137 |
138 | return model
139 |
140 | def wrapup(self, args:dict, H:List[nx.Graph]=None) -> None:
141 | self._init_var_info(H)
142 | self._init_constrs(H)
143 | model = self._init_model_params(args)
144 |
145 | """ Variables """
146 | tau = model.addVar(name="tau", vtype=GRB.CONTINUOUS)
147 | x = model.addMVar(name="x", shape=(sum(self.num_e), 1), vtype=GRB.BINARY)
148 | y = model.addMVar(name="y", shape=(sum(self.num_v), 1), vtype=GRB.BINARY)
149 | fu = model.addMVar(name="fu", shape=(sum(self.num_e), 1), vtype=GRB.CONTINUOUS)
150 | fv = model.addMVar(name="fv", shape=(sum(self.num_e), 1), vtype=GRB.CONTINUOUS)
151 |
152 | """ Objective """
153 | model.setObjective(tau, GRB.MINIMIZE)
154 |
155 | """ Constraints """
156 | constrs = [self.C_makespan, self.C_rooted, self.C_tree,self.C_cover,
157 | self.flow, self.C_acyclic] + self.C_y_defs
158 | for C in constrs:
159 | constr, name = C.build(x, y, fu, fv, tau)
160 | model.addConstr(constr, name = name)
161 |
162 | self.model, self.x, self.y, self.fu, self.fv = model, x, y, fu, fv
163 |
164 | def solve(self) -> Tuple[list, list]:
165 |
166 | try:
167 | self.model.optimize()
168 | except gp.GurobiError as e:
169 | print(f"Error code {e.errno}: {e}")
170 | except AttributeError:
171 | print('Encountered an attribute error')
172 |
173 | sol_edges = [[] for _ in self.istc.I]
174 | sol_verts = [[] for _ in self.istc.I]
175 |
176 | try:
177 | for i in self.istc.I:
178 | for e_id in range(self.e_ind[i], self.e_ind[i+1]):
179 | if self.x.X[e_id] > 0.5:
180 | e = self.id2e[i][e_id - self.e_ind[i]]
181 | sol_edges[i].append(self.istc.e2uv[e])
182 | for v_id in range(self.v_ind[i], self.v_ind[i+1]):
183 | if self.y.X[v_id] > 0.5:
184 | v = self.id2v[i][v_id-self.v_ind[i]]
185 | sol_verts[i].append(v)
186 | except Exception as e:
187 | print(e)
188 |
189 | return sol_edges, sol_verts
190 |
191 | def apply_heur(self, alpha:float, beta:float) -> Tuple[List[nx.Graph], int]:
192 | if alpha is not None and beta is None:
193 | Vi, _, _ = heur_parabolic(self.istc, alpha)
194 | elif alpha is None and beta is not None:
195 | Vi, _, _ = heur_subgraph(self.istc, beta)
196 | elif alpha is not None and beta is not None:
197 | V_prh, _, _ = heur_parabolic(self.istc, alpha)
198 | V_srh, _, _ = heur_subgraph(self.istc, beta)
199 | Vi = {r:set.union(V_prh[r], V_srh[r]) for r in self.istc.R}
200 | else:
201 | return [self.istc.G for _ in self.istc.I], 0
202 |
203 | H = []
204 | num_vars_removed = 0
205 | for i in self.istc.I:
206 | residual_graph_verts = set(self.istc.V) - Vi[self.istc.R[i]]
207 | Hi = self.istc.G.subgraph(residual_graph_verts)
208 | assert(nx.is_connected(Hi))
209 | H.append(Hi)
210 | num_vars_removed += H[i].number_of_nodes() + 3 * H[i].number_of_edges()
211 |
212 | return H, 1 - num_vars_removed / (self.istc.k * (self.istc.n + 3 * self.istc.m))
213 |
--------------------------------------------------------------------------------
/MIP_MCPP/warmstarter.py:
--------------------------------------------------------------------------------
1 | from collections import defaultdict
2 | from typing import Callable, Tuple
3 |
4 | import matplotlib.pyplot as plt
5 | import networkx as nx
6 |
7 | from MSTC_Star.mcpp.rtc_planner import RTCPlanner
8 | from MIP_MCPP.graph_utils import graph_plot, remove_cycles
9 | from MIP_MCPP.misc import uv_sorted
10 | from MIP_MCPP.model import Model
11 |
12 |
13 | def gen_flow(G: nx.Graph, debug=False) -> Tuple[dict, dict]:
14 | if debug:
15 | fig, ax = plt.subplots()
16 | k = G.number_of_nodes()
17 | f_eu = {uv_sorted(uv): 0 for uv in G.edges()}
18 | f_ev = {uv_sorted(uv): 0 for uv in G.edges()}
19 | flow_leftover = {v: 1-1/k for v in G.nodes()}
20 |
21 | assert(nx.cycle_basis(G) == [])
22 |
23 | while G.number_of_edges() != 0:
24 |
25 | for e in G.edges():
26 | u, v = uv_sorted(e)
27 | e = (u, v)
28 | if G.degree(u) == 1 and G.degree(v) != 1:
29 | G.remove_edge(u, v)
30 | f_eu[e] = round(flow_leftover[u], 12)
31 | f_ev[e] = round(1 - f_eu[e], 12)
32 | assert(f_eu[e]+f_ev[e] == 1)
33 | flow_leftover[u] -= f_eu[e]
34 | flow_leftover[v] -= f_ev[e]
35 | elif G.degree(u) != 1 and G.degree(v) == 1:
36 | G.remove_edge(u, v)
37 | f_ev[e] = round(flow_leftover[v], 12)
38 | f_eu[e] = round(1 - f_ev[e], 12)
39 | assert(f_eu[e]+f_ev[e] == 1)
40 | flow_leftover[v] -= f_ev[e]
41 | flow_leftover[u] -= f_eu[e]
42 | elif G.degree(u) == 1 and G.degree(v) == 1:
43 | G.remove_edge(u, v)
44 | f_eu[e] = round(flow_leftover[u], 12)
45 | f_ev[e] = round(1-flow_leftover[u], 12)
46 | assert(f_eu[e]+f_ev[e] == 1)
47 | flow_leftover[u] -= f_eu[e]
48 | flow_leftover[v] -= f_ev[e]
49 |
50 | if debug:
51 | ax.cla()
52 | graph_plot(G, ax, with_edge_labels=False)
53 | plt.draw()
54 | plt.pause(0.05)
55 |
56 | return f_eu, f_ev
57 |
58 |
59 | class WarmStarter:
60 |
61 | @staticmethod
62 | def warmstart_RTC(model: Model, varfunc: Callable) -> list:
63 | istc = model.istc
64 | pos = nx.get_node_attributes(istc.G, "pos")
65 | pos2v = {p: v for v, p in pos.items()}
66 |
67 | G = nx.Graph()
68 | for u, v in istc.G.edges():
69 | G.add_edge(pos[u], pos[v], weight=istc.G[u][v]["weight"])
70 |
71 | pos_rec = defaultdict(list)
72 | for i in istc.I:
73 | pos_rec[pos[istc.R[i]]].append(i)
74 |
75 | r2pos = {}
76 | for p, ii in pos_rec.items():
77 | r2pos[ii[0]] = p
78 | for idx in range(1, len(ii)):
79 | r_dummy = (p[0]+0.1*idx, p[1]+0.1*idx)
80 | G.add_edge(r_dummy, p, weight=0)
81 | r2pos[ii[idx]] = r_dummy
82 | pos2v[r_dummy] = istc.R[ii[idx]]
83 |
84 | R = [r2pos[i] for i in istc.I]
85 | assert(len(set(R)) == istc.k)
86 | rtc_planner = RTCPlanner(G, R, len(R))
87 | match_tuple, _, _ = rtc_planner.k_tree_cover()
88 |
89 | sol_edges = [[] for _ in istc.I]
90 | sol_verts = [set([]) for _ in istc.I]
91 |
92 | # get corresponding vars. value
93 | r2i = {r: i for i, r in enumerate(istc.R)}
94 | for r, val in match_tuple.items():
95 | r = pos2v[r]
96 | i = r2i[r]
97 | L, S, P = val
98 | L = [(pos2v[u], pos2v[v]) for u, v in L.edges()]
99 | S = [(pos2v[u], pos2v[v]) for u, v in S.edges()]
100 | sol_edges[i] = L + S + \
101 | [(pos2v[P[idx]], pos2v[P[idx+1]]) for idx in range(len(P)-1)]
102 |
103 | if sol_edges[i] == []:
104 | varfunc(model.y[model.v_ind[i]+model.v2id[i][r]], 1)
105 | sol_verts[i].add(r)
106 |
107 | for u, v in sol_edges[i]:
108 | sol_verts[i].add(u)
109 | sol_verts[i].add(v)
110 | if u == v:
111 | sol_edges[i].remove((u, v))
112 |
113 | Ti, sol_edges[i] = remove_cycles(istc.G, sol_edges[i])
114 |
115 | assert(len(list(nx.connected_components(Ti))) <= 1)
116 |
117 | f_eu, f_ev = gen_flow(Ti)
118 |
119 | for u, v in sol_edges[i]:
120 | e = istc.uv2e[uv_sorted((u, v))]
121 | eid = model.e_ind[i] + model.e2id[i][e]
122 | uid = model.v_ind[i] + model.v2id[i][u]
123 | vid = model.v_ind[i] + model.v2id[i][v]
124 | varfunc(model.x[eid], 1)
125 | varfunc(model.y[uid], 1)
126 | varfunc(model.y[vid], 1)
127 | varfunc(model.fu[eid], f_eu[uv_sorted((u, v))])
128 | varfunc(model.fv[eid], f_ev[uv_sorted((u, v))])
129 |
130 | V = set()
131 | for v in sol_verts:
132 | V = V.union(v)
133 |
134 | assert(len(V) == istc.n)
135 | return sol_edges
136 |
137 | @staticmethod
138 | def warmstart_MST(model: Model, H, varfunc: Callable) -> list:
139 | if H is None:
140 | H = [model.istc.G for _ in model.istc.I]
141 |
142 | sol_edges = [[] for _ in model.istc.I]
143 |
144 | for i in model.istc.I:
145 | Mi = nx.minimum_spanning_tree(H[i])
146 | sol_edges[i] = list(Mi.edges())
147 | f_eu, f_ev = gen_flow(Mi.copy())
148 | for u, v in Mi.edges():
149 | e = model.istc.uv2e[uv_sorted((u, v))]
150 | eid = model.e_ind[i] + model.e2id[i][e]
151 | uid = model.v_ind[i] + model.v2id[i][u]
152 | vid = model.v_ind[i] + model.v2id[i][v]
153 | varfunc(model.x[eid], 1)
154 | varfunc(model.y[uid], 1)
155 | varfunc(model.y[vid], 1)
156 | varfunc(model.fu[eid], f_eu[uv_sorted((u, v))])
157 | varfunc(model.fv[eid], f_ev[uv_sorted((u, v))])
158 |
159 | return sol_edges
160 |
161 | @staticmethod
162 | def apply(model: Model, type="MST", H=None) -> Model:
163 | def _set_var_start(var, val):
164 | var.setAttr("start", val)
165 |
166 | if type == "RTC":
167 | init_sol_edges = WarmStarter.warmstart_RTC(model, _set_var_start)
168 | elif type == "MST":
169 | init_sol_edges = WarmStarter.warmstart_MST(
170 | model, H, _set_var_start)
171 |
172 | return model
173 |
174 | @staticmethod
175 | def check_feasibility(model: Model, type="MST", H=None) -> None:
176 | def _fix_var(var, val):
177 | var.setAttr("lb", val)
178 | var.setAttr("ub", val)
179 |
180 | if type == "RTC":
181 | WarmStarter.warmstart_RTC(model, _fix_var)
182 | elif type == "MST":
183 | WarmStarter.warmstart_MST(model, H, _fix_var)
184 |
185 | try:
186 | model.model.computeIIS()
187 | model.model.write("model.ilp")
188 | except Exception as e:
189 | print(e)
190 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # MIP-MCPP
2 | This repository is the implementation of the MIP, MIP-PRH and MIP-SRH models for the Min-Max Rooted Tree Cover (MMRTC) problem and their corresponding planners for the graph-based multi-robot coverage path planning problem from the following paper:
3 |
4 | *Jingtao Tang and Hang Ma. "Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Heuristics." IEEE Robotics and Automation Letters (Aug. 2023).* [[paper]](https://ieeexplore.ieee.org/abstract/document/10225271), [[video]](https://ieeexplore.ieee.org/ielx7/7083369/10220574/10225271/supp1-3306996.mp4?arnumber=10225271), [[project]](https://reso1.github.io/blog/posts/grid_mcpp)
5 |
6 | Please cite this article if you use this code for the multi-robot coverage path planning problem.
7 |
8 | ## Installation
9 | ### 1. Python lib:
10 | `pip install -r requirements.txt`
11 |
12 | ### 2. Gurobi lib:
13 | > optional if you don't want to run solver.py for MIP optimization. Pre-run model solutions are provided in directory 'data/solutions'.
14 |
15 | Please refer to [[Gurobi]](https://www.gurobi.com/) for the installation. (they have trial and academic licenses)
16 |
17 | ## Description
18 |
19 | ### 1. The MMRTC MIP Solver
20 | > The MCPP problem is reduced to the MMRTC problem and then solved with the STC algorithm. Please refer to the paper for more details.
21 |
22 | #### Usage
23 | ```bash
24 | python solver.py [-h] [--solver_cfg SOLVER_CFG] [--alpha ALPHA] [--beta BETA] [--warm_start WARM_START] istc
25 | ```
26 | - Required:
27 | - `istc`: the instance name stored in directory 'data/instances'.
28 | - Optional:
29 | - `--solver_cfg SOLVER_CFG`: path to the Gurobi configuration file. (see 'data/cfgs' for reference)
30 | - `alpha ALPHA`: parameter of Parabolic Removal Heuristics (PRH). Will solve the MIP-PRH model if specified.
31 | - `beta BETA`: parameter of Subgraph Removal Heuristics (SRH). Will solve the MIP-SRH model if specified.
32 | - `--warm_start WARM_START`: type of warm-startup for the model optimization. Use 'RTC' for the original MIP model and 'MST' for MIP-PRH and MIP-SRH.
33 |
34 | ### 2. The Instance Maker
35 | A simple routine to create random MMRTC instance.
36 | - if map is provided, then a terrain with uniform terrain weight of 1 is generated, encoded by:
37 | - obstacle vertex: black pixel, rgb=(0,0,0)
38 | - free vertex: white pixel, rgb=(1,1,1)
39 | - root vertex: red pixel, rgb=(1,0,0)
40 | - otherwise, an empty terrain with random weights will be generated.
41 |
42 | #### Usage
43 | ```bash
44 | python instance_maker.py [-h] [--map MAP] name
45 | ```
46 |
47 | - Required:
48 | - `name`: the instance name in the format of '[grid width]x[grid height]-[Characteristics]-k[# of roots]'.
49 | - If no map is provided, the generated instance is a `[grid width]`x`[grid height]` empty terrain with `[# of roots]` subtrees (or robots) and randomized terrain weights.
50 |
51 | - Optional:
52 | - `--map MAP`: path to the map to create the instance.
53 |
54 | ### 3. The MCPP Planner
55 | The MCPP planners with simulation, including MFC, MSTC$^*$ and MIP (the method in this paper).
56 |
57 | #### Usage:
58 | ```bash
59 | python planner.py [-h] [--method METHOD] [--istc_sol_name ISTC_SOL_NAME] [--scale SCALE] [--dt DT] [--write WRITE] istc
60 | ```
61 | - Required:
62 | - `istc`: the instance name stored in directory 'data/instances'.
63 | - Optional:
64 | - `--method METHOD`: planner type choose from {MFC, MSTC*, MIP}.
65 | - `-istc_sol_name ISTC_SOL_NAME`: MIP solution name stored in the directroy 'data/solutions'. (only required when planner type is MIP)
66 | - `--scale SCALE`: the canvas scaling factor for visualization.
67 | - `--dt DT`: delta time of simulation.
68 | - `--write WRITE`: is writing the simulation as MP4. (ffmpeg lib is required)
69 |
70 |
71 | ## MCPP Simulation Results
72 | - The floor-medium instance using the MMRTC solution from MIP-SRH model
73 |
74 | 
75 |
76 | ## License
77 | MIP-MCPP is released under the GPL version 3. See LICENSE.txt for further details.
78 |
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/data/cfgs/default.yaml:
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1 | Threads: 8
2 | TimeLimit: 600
3 | OptimalityTol: 1e-3
4 | SoftMemLimit: 8
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/data/cfgs/server.yaml:
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1 | Threads: 16
2 | TimeLimit: 600
3 | OptimalityTol: 1e-3
4 | SoftMemLimit: 64
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/data/instances/floor_large-30x30-k4.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/floor_large-30x30-k4.istc
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/data/instances/floor_medium-20x20-k12.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/floor_medium-20x20-k12.istc
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/data/instances/floor_small-5x10-k4.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/floor_small-5x10-k4.istc
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/data/instances/maze_large-30x30-k8.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/maze_large-30x30-k8.istc
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/data/instances/maze_medium-20x20-k6.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/maze_medium-20x20-k6.istc
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/data/instances/maze_small-10x10-k6.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/maze_small-10x10-k6.istc
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/data/instances/terrain_large_1-32x32-k4.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/terrain_large_1-32x32-k4.istc
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/data/instances/terrain_large_2-32x32-k4.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/terrain_large_2-32x32-k4.istc
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/data/instances/terrain_medium-20x20-k4.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/terrain_medium-20x20-k4.istc
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/data/instances/terrain_small-10x10-k8.istc:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/terrain_small-10x10-k8.istc
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/data/solutions/floor_large-30x30-k4-alpha_0.3_warmstart.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/floor_large-30x30-k4-alpha_0.3_warmstart.solu
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/data/solutions/floor_medium-20x20-k12-beta_0.9_warmstart.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/floor_medium-20x20-k12-beta_0.9_warmstart.solu
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/data/solutions/floor_small-5x10-k4.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/floor_small-5x10-k4.solu
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/data/solutions/maze_large-30x30-k8-beta_0.9_warmstart.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/maze_large-30x30-k8-beta_0.9_warmstart.solu
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/data/solutions/maze_medium-20x20-k6-beta_0.9-warmstart.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/maze_medium-20x20-k6-beta_0.9-warmstart.solu
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/data/solutions/maze_small-10x10-k6-beta_0.9_warmstart.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/maze_small-10x10-k6-beta_0.9_warmstart.solu
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/data/solutions/terrain_large_1-32x32-k4-alpha_0.9_warmstart.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/terrain_large_1-32x32-k4-alpha_0.9_warmstart.solu
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/data/solutions/terrain_large_2-32x32-k4-beta_0.6_warmstart.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/terrain_large_2-32x32-k4-beta_0.6_warmstart.solu
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/data/solutions/terrain_medium-20x20-k4-beta_0.6_warmstart.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/terrain_medium-20x20-k4-beta_0.6_warmstart.solu
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/data/solutions/terrain_small-10x10-k8-alpha_0.9-warmstart.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/terrain_small-10x10-k8-alpha_0.9-warmstart.solu
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/data/solutions/terrain_small-10x10-k8.solu:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/solutions/terrain_small-10x10-k8.solu
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/figs/floor-medium-MIP.gif:
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https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/figs/floor-medium-MIP.gif
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/instance_maker.py:
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1 | import argparse
2 |
3 | from MIP_MCPP.instance import Instance
4 |
5 |
6 | def main():
7 | parser = argparse.ArgumentParser()
8 | parser.add_argument(
9 | "name", help="Instance Name, should be '[grid width]x[grid height]-[Characteristics]-k[# of roots]'")
10 | parser.add_argument("--map", default=None,
11 | help="Path to the Binary Map to Create the Instance")
12 |
13 | args = parser.parse_args()
14 |
15 | if args.map:
16 | istc = Instance.create_from_binary_map(args.map)
17 | else:
18 | istc = Instance.create_random_free(args.name)
19 |
20 | if istc:
21 | istc.write(args.name + '.istc')
22 |
23 |
24 | if __name__ == "__main__":
25 | main()
26 |
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/planner.py:
--------------------------------------------------------------------------------
1 | import argparse
2 | import os
3 |
4 | import networkx as nx
5 |
6 | from MIP_MCPP.instance import Instance
7 | from MIP_MCPP.mcpp_planner import mfc_plan, mip_plan, mstcstar_plan, simulate
8 |
9 |
10 | def plan(
11 | istc_name: str,
12 | method: str,
13 | istc_sol_name: str,
14 | scale: float,
15 | dt: float,
16 | is_write: bool
17 | ) -> None:
18 |
19 | istc = Instance.read(f"{istc_name}.istc")
20 |
21 | if method == "MFC":
22 | planner, paths, weights, runtime = mfc_plan(istc)
23 | elif method == "MSTC_Star":
24 | planner, paths, weights, runtime = mstcstar_plan(istc)
25 | elif method == "MIP":
26 | if istc_sol_name:
27 | sol_edges = Instance.read_solution(istc_sol_name)
28 | else:
29 | for fn in os.listdir(Instance.DEFAULT_SOLUTION_DIR):
30 | if fn.startswith(istc_name):
31 | sol_edges = Instance.read_solution(fn[:-5])
32 | break
33 | planner, paths, weights = mip_plan(istc, sol_edges)
34 | else:
35 | print(f"unsupported method {method}")
36 | return
37 |
38 | print(f"Method: {method}, Makespan: {max(weights)}")
39 |
40 | simulate(
41 | name=f"{istc_name}-{method if method != 'MSTC_Star' else 'MSTC*'}",
42 | planner=planner,
43 | paths=paths,
44 | weights=weights,
45 | scale=scale,
46 | dt=dt,
47 | obs_graph=nx.Graph(),
48 | is_write=is_write,
49 | is_show=not is_write
50 | )
51 |
52 |
53 | def main():
54 | parser = argparse.ArgumentParser()
55 | parser.add_argument("istc", help="Instance Name")
56 | parser.add_argument("--method", default="MIP",
57 | help="Planner Type from {MFC, MSTC*, MIP}")
58 | parser.add_argument("--istc_sol_name", default=None,
59 | help="MIP Solution Name Stored in 'data/solutions'")
60 | parser.add_argument("--scale", default=1.0, help="Plot Scaling Factor")
61 | parser.add_argument("--dt", default=0.02, help="Delta Time of Simulation")
62 | parser.add_argument("--write", default=False,
63 | help="Is Writing the Simulation as MP4")
64 |
65 | args = parser.parse_args()
66 |
67 | plan(
68 | istc_name=args.istc,
69 | method=args.method,
70 | istc_sol_name=args.istc_sol_name,
71 | scale=float(args.scale),
72 | dt=float(args.dt),
73 | is_write=bool(args.write)
74 | )
75 |
76 |
77 | if __name__ == "__main__":
78 | main()
79 |
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/requirements.txt:
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1 | gurobipy==10.0.1
2 | matplotlib==3.7.0
3 | networkx==3.1
4 | numpy==1.23.5
5 | pandas==1.5.3
6 | PyYAML==6.0
7 | scipy==1.9.1
8 | seaborn==0.12.2
9 |
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/solver.py:
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1 | import argparse
2 | import os
3 |
4 | import yaml
5 |
6 | from MIP_MCPP.instance import Instance
7 | from MIP_MCPP.model import Model
8 | from MIP_MCPP.warmstarter import WarmStarter
9 |
10 |
11 | def solve(
12 | istc_name: str,
13 | solver_args: dict,
14 | alpha: float,
15 | beta: float,
16 | warm_start: str
17 | ) -> None:
18 |
19 | istc = Instance.read(istc_name + ".istc")
20 |
21 | model = Model(istc)
22 | H, perc_vars_removed = model.apply_heur(alpha, beta)
23 | model.wrapup(solver_args, H)
24 | if warm_start:
25 | model = WarmStarter.apply(model, warm_start, H)
26 | sol_edges, sol_verts = model.solve()
27 |
28 | sol_name = "" if alpha is None and beta is None else f"-alpha_{alpha}-beta_{beta}_warmstart{warm_start}"
29 | res_str = ",\t".join(str(s) for s in [
30 | istc_name,
31 | "None" if alpha is None else alpha,
32 | "None" if beta is None else beta,
33 | model.num_vars if alpha is None and beta is None else round(
34 | perc_vars_removed, 3)
35 | ])
36 |
37 | if sol_edges == []:
38 | obj_val, mip_gap = "/", "/"
39 | else:
40 | obj_val = round(model.model.objVal, 3)
41 | mip_gap = round(model.model.MIPGap, 3),
42 | Instance.write_solution(sol_edges, istc_name + sol_name + ".solu")
43 |
44 | res_str += ",\t".join(str(s) for s in [
45 | "",
46 | warm_start,
47 | obj_val,
48 | mip_gap,
49 | round(model.model.objBound, 3),
50 | round(model.model.RunTime, 3)
51 | ])
52 |
53 | with open(os.path.join("data", "res.txt"), "a") as f:
54 | f.write(res_str + "\n")
55 |
56 | return sol_edges, sol_verts
57 |
58 |
59 | def main():
60 | parser = argparse.ArgumentParser()
61 | parser.add_argument("istc", help="Instance Name")
62 | parser.add_argument("--solver_cfg", default="default.yaml",
63 | help="Gurobi Configuration File Path")
64 | parser.add_argument("--alpha", default=None,
65 | help="Parabolic Heuristics Parameter")
66 | parser.add_argument("--beta", default=None,
67 | help="Subgraph Heuristics Parameter")
68 | parser.add_argument("--warm_start", default=None,
69 | help="Warm startup the optimization: [RTC] for non-heuritiscs model and [MST] for heuritsics model")
70 |
71 | args = parser.parse_args()
72 |
73 | with open(os.path.join("data", "cfgs", args.solver_cfg)) as f:
74 | solver_args = yaml.load(f, yaml.Loader)
75 | solver_args["OptimalityTol"] = float(solver_args["OptimalityTol"])
76 |
77 | solve(
78 | istc_name=args.istc,
79 | solver_args=solver_args,
80 | alpha=float(args.alpha) if args.alpha else None,
81 | beta=float(args.beta) if args.beta else None,
82 | warm_start=args.warm_start if args.warm_start else None
83 | )
84 |
85 |
86 | if __name__ == "__main__":
87 | main()
88 |
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