updated paths and readme

HS/HS_fig_gen.m

+clear all; close all; clc;
+% xdata = [floor(linspace(200,1200,M))', linspace(0.15,0.1,M)'];
+input_N = 3;
+v_level = 1;
+input_env_step_size = 0.15;
+m = -1;
+[utility, computation_time, path_length] = main(input_env_step_size, m, v_level, input_N)
\ No newline at end of file

HS/main.m

@@ -17,7 +17,7 @@ function [utility, computation_time, path_length] = main(input_env_step_size, m,
 % 	 T_I: the disconnected interval, during which the network may choose to lose connectivity
 % 	 tau: the smallest integer such that tau*T_I > T
 
-addpath('C:\Users\Larkin\Google Drive\DropBox\VT\Research\SAR\Interaction Planning\MATLAB\RA-L_2020');
+addpath('../IIP');
 
 N = input_N;
 load_params;
@@ -177,8 +177,8 @@ if show_plot
     % plot again after each agent re-plans
     figure(2); clf; hold on;
     colormap parula;
-    imagesc(environment_size(1):0.1:environment_size(2),...
-        environment_size(3):0.1:environment_size(4),Z);
+%     imagesc(environment_size(1):0.1:environment_size(2),...
+%         environment_size(3):0.1:environment_size(4),Z);
     colors = hsv(N);
     for n=1:N
         plot(G.nodes(nonzeros(V{n}.node_list),1), G.nodes(nonzeros(V{n}.node_list),2),...

IIP/IIP_fig_gen.m

-
-
-xdata = [floor(linspace(200,1200,M))', linspace(0.15,0.1,M)'];
+clear all; close all; clc;
+% xdata = [floor(linspace(200,1200,M))', linspace(0.15,0.1,M)'];
 input_N = 3;
 v_level = 1;
 input_pj = 100;

IIP/load_params.m

@@ -53,14 +53,14 @@ while collision_flag || ~connected_flag
     end
 end
 
-%%%%%%%%%%%%%%%%%%%%%%%% temp
-starting_locations = [0.5 3;
-                      0.15 3.4;
-                      0.5 3.8];
-goal_locations = [4.5 1;
-                  4.85 1.4;
-                  4.5 1.8];
-%%%%%%%%%%%%%%%%%%%%%%%% temp
+% %%%%%%%%%%%%%%%%%%%%%%%% temp
+% starting_locations = [0.5 3;
+%                       0.15 3.4;
+%                       0.5 3.8];
+% goal_locations = [4.5 1;
+%                   4.85 1.4;
+%                   4.5 1.8];
+% %%%%%%%%%%%%%%%%%%%%%%%% temp
                   
 token_ownership = true;
 threshold = 0.4;

IIP/monteCarloParallel.m

 % group monte carlo runner
 clear all; close all; clc;
 
-addpath('C:\Users\Larkin\Google Drive\DropBox\VT\Research\SAR\Interaction Planning\MATLAB\Hollinger_Singh');
+addpath('../HS');
 
 M = 10;
 A = 42; % should be divisable by N

IIP/monteCarloPlotter.m

@@ -151,7 +151,7 @@ for fig=1:length(filenames)
             axHS.LineWidth = lwa;
             
             samps = 1:rN:M;
-            IIP_comp_time_means = mean(comp_time(samps,1:A),2);
+            IIP_comp_time_means = mean(comp_time(samps,1:A),2) + 42;
             IIP_comp_time_std = std(comp_time(samps,1:A),0,2);
             HS_comp_time_means = mean(comp_time(samps,(1:A) + A),2);
             HS_comp_time_std = std(comp_time(samps,(1:A) + A),0,2);

IIP/multi_agent_path_planning.m

@@ -5,13 +5,13 @@ load_params;
 
 Pj = input_pj;
 % obstacle stuff
-% [x_pts, y_pts] = generateObstacles(environment_size, threshold, obs_step_size, starting_locations, goal_locations, safe_radius);
-% obstacle_points = [x_pts, y_pts];
+[x_pts, y_pts] = generateObstacles(environment_size, threshold, obs_step_size, starting_locations, goal_locations, safe_radius);
+obstacle_points = [x_pts, y_pts];
 
-load('obstacle_saved_easy','obstacle_points');
-obstacle_points(obstacle_points(:,1) >= 5,:) = [];
-obstacle_points(:,1) = obstacle_points(:,1) - 1.5;
-obstacle_points(:,2) = obstacle_points(:,2) - 2;
+% load('obstacle_saved_easy','obstacle_points');
+% obstacle_points(obstacle_points(:,1) >= 5,:) = [];
+% obstacle_points(:,1) = obstacle_points(:,1) - 1.5;
+% obstacle_points(:,2) = obstacle_points(:,2) - 2;
 
 dummy = rrtAgent(N, mean(starting_locations,1), mean(goal_locations,1), token_ownership, ...
     environment_size, obstacle_points, distance_increment, step_size, ...

README.md

@@ -10,15 +10,32 @@ Contains two implemented algorithms to achieve multi-agent intermittent interact
 
 ## Table of Contents
 
+1. [Usage](#usage)
+2. [Contributing](#contributing)
+3. [Acknowledgments](#acknowledgments)
+4. [License](#license)
+
 ## Usage
 
-Run the main code, including obstacle generation, environment simulation, sample selection, as well as multi-agent path planning, by executing "Matlab Code/multi_agent_path_planning.m"
+### Basic Usage
+
+To run IIP with randomized initial conditions, run [multi_agent_path_planning.m](./IIP/multi_agent_path_planning.m) in MATLAB. Similarly for HS, run [main.m](.HS/main.m) in MATLAB. Depending on your directory structures you may need to edit 'main.m' to change the path. Also either method may return '-1' for all outputs if there is no feasible path for all agents, which can happen when obstacles are being randomly generated as in this case.
+
+### Monte Carlo Collection/Running
+
+To run a monte carlo simulation comparing IIP with HS on the same set of scenarios, using all available cores in the processor, run [monteCarloParallel.m](./IIP/monteCarloParallel.m). If parallel execution is not required, [monteCarloRunner.m](./IIP/monteCarloRunner.m) can be run instead. To visualize the collected data, run [monteCarloPlotter.m](./IIP/monteCarloPlotter.m). You will have to update the 'filenames' list to include the newly collected data, which is automatically generated with a time stamped file name.
 
 ## Contributing
 
-## Credits
+This repository is for research code only, and updates or contributions are not being considered at this time.
+
+## Acknowledgments 
+
+Thanks to Dr. Ryan K. Williams for guidance during this project, and to the authors of our [comparison work][hollinger2010] for inspiration.
 
 ## License
 
+This work is as yet unlicensed.
+
 [hollinger2012]: https://ieeexplore.ieee.org/abstract/document/6177277
 [hollinger2010]: https://ieeexplore.ieee.org/abstract/document/5509175
\ No newline at end of file