├── .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 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 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 | ![](figs/floor-medium-MIP.gif) 75 | 76 | ## License 77 | MIP-MCPP is released under the GPL version 3. See LICENSE.txt for further details. 78 | -------------------------------------------------------------------------------- /data/cfgs/default.yaml: -------------------------------------------------------------------------------- 1 | Threads: 8 2 | TimeLimit: 600 3 | OptimalityTol: 1e-3 4 | SoftMemLimit: 8 -------------------------------------------------------------------------------- /data/cfgs/server.yaml: -------------------------------------------------------------------------------- 1 | Threads: 16 2 | TimeLimit: 600 3 | OptimalityTol: 1e-3 4 | SoftMemLimit: 64 -------------------------------------------------------------------------------- /data/instances/floor_large-30x30-k4.istc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/reso1/MIP-MCPP/8682c7b7d0fc8640819f5dde1454700ee13473b4/data/instances/floor_large-30x30-k4.istc -------------------------------------------------------------------------------- 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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 | -------------------------------------------------------------------------------- /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 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /solver.py: -------------------------------------------------------------------------------- 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 | --------------------------------------------------------------------------------