Merge branch 'preparation_for_final_submission' into 'master'

code for the final submission

See merge request ICS/ics-private/phd-students/papers/2023-code-acc-distributed-model-predictive-flocking-with-obstacle-avoidance-and-asymmetric-interaction-forces!1

README.md

@@ -1,15 +1,13 @@
 # 2023-code-ACC-Distributed Model Predictive Flocking with Obstacle Avoidance and Asymmetric Interaction Forces
 
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7139904.svg)](https://doi.org/10.5281/zenodo.7139904)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7139904.svg)](https://doi.org/10.5281/zenodo.7139903)
 
 ## General
 
-This repository contains an implementation of the algorithms and simulations described in 
+This repository contains an implementation of the algorithms and simulations presented in 
 
 > P.Hastedt and H. Werner, "Distributed Model Predictive Flocking with Obstacle Avoidance and Asymmetric Interaction Forces"
 
-submitted to ACC, 2023.
-
 It may be used to recreate and validate the simulation results and figures from the paper. To do so, run either of the two scripts `simulation.m` and `evaluation.m`.
 
 Additionally, videos for the scenarios described in the paper are provided in the `videos` directory.
@@ -26,7 +24,7 @@ At the top of `simulation.m`, the algorithm and scenario to be simulated can be
 
 ## Evaluation
 
-At the top of `evaluation.m`, the scenarios to be compared can be selected by adding the corresponding data index to the `dataSelection` array.  To evaluate additional data generated by the simulation, copy the `.mat` files from the `simulation/out` directory to the `data` directory and add the name of the data file to the `simData` array at the top of `evaluation.m`.
+At the top of `evaluation.m`, the scenarios to be compared can be selected by changing the `scenarioIndex` variable.  To evaluate additional data generated by the simulation, copy the `.mat` files from the `simulation/out` directory to the `data` directory and add the name of the data file to the `simData` array at the top of `evaluation.m`.
 
 ## Prerequisites
 

evaluation.m

@@ -17,18 +17,29 @@
 
 % Available Data
 simData = [
-    "hastedt_scenario_1"        % 1
-    "hastedt_scenario_2"        % 2
-    "huang_scenario_1"          % 3
-    "huang_scenario_2"          % 4
-    "olfati-saber_scenario_1"   % 5
-    "olfati-saber_scenario_2"   % 6
+    "hastedt_scenario_1"            % 1
+    "hastedt_scenario_2"            % 2
+    "huang_scenario_1"              % 3
+    "huang_scenario_2"              % 4
+    "olfati-saber_scenario_1"       % 5
+    "olfati-saber_scenario_2"       % 6
     ];
 
-% Select Data to compare by adding the indices from the simData array
-dataSelection = [1,3,5]; % scenario 1 comparison
-% dataSelection = [2,4,6]; % scenario 2 comparison
+% Select the scenario to evaluate by setting the scenarioIndex. More data
+% can be added to the comparison by changing the dataSelection arrays.
 
+% Scenarios
+% 1: Large obstacle
+% 2: Field of small Obstacles
+
+scenarioIndex = 1;
+
+switch (scenarioIndex)
+    case (1)
+        dataSelection = [1,3,5];
+    case (2)
+        dataSelection = [2,4,6];
+end
 %% Comparison/Evaluation
 % Minimum distance variable initialization
 t_distN = {};
@@ -49,28 +60,46 @@
 for j=1:length(dataSelection)
     figure()
     load(dataPath+simData(dataSelection(j)));
-    viscircles(param.obstacles(1:2,:)',param.obstacles(3,:),'Color','black', 'LineWidth', 1); hold on;
+    if ~isempty(param.obstacles)
+        viscircles(param.obstacles(1:2,:)',param.obstacles(3,:),'Color','black', 'LineWidth', 1); hold on;
+    end
     for i = 1:size(out.data.position,3)
         plot(out.data.position(:,1,i),out.data.position(:,2,i),'b'); hold on;
     end
+
     plot(squeeze(out.data.position(1,1,:)),squeeze(out.data.position(1,2,:)),'kx','MarkerSize',10,'LineWidth',1); hold on;
     plot(squeeze(out.data.position(end,1,:)),squeeze(out.data.position(end,2,:)),'kx','MarkerSize',10,'LineWidth',1); hold on;
     title("Agent Trajectories "+ replace(erase(simData(dataSelection(j)),".mat"),"_","\_"));
     xlabel('x');
     ylabel('y');
-    xlim([-5 100]);
-    ylim([-5 100]);
+    if (scenarioIndex ~=3)
+        xlim([-5 100]);
+        ylim([-5 100]);
+    end
 end
 
+%% Input Comparison
+names = {};
+for i = 1:length(dataSelection)
+    load(dataPath+simData(dataSelection(i)));
+    u_rms(i) = calculateTotalInputUsed(out);
+    names{i} = replace(erase(simData(dataSelection(i)),".mat"),"_","\_");
+end
+figure()
+bar(1:i,u_rms);
+set(gca, 'XTick', 1:length(names),'XTickLabel',names);
+title('RMS Input Values')
+
 %% Minimum distances
 figure()
 for i = 1:size(distN,2)
+    tmax = t_distN{i}(end);
     plot(t_distN{i},distN{i}, 'DisplayName',replace(erase(simData(dataSelection(i)),".mat"),"_","\_")+" q_{ij}");
     hold on;
     plot(t_distO{i},distO{i}, '--','DisplayName',replace(erase(simData(dataSelection(i)),".mat"),"_","\_")+" q_{io}");
     hold on;
 end
-xlim([0,400])
+xlim([0,tmax])
 ylim([0,8.4])
 grid on;
 title('Minimum inter-agent and agent-obstacle distances')
@@ -79,18 +108,6 @@
 legend show;
 legend('Location','southeast');
 
-%% Input Comparison
-names = {};
-for i = 1:length(dataSelection)
-    load(dataPath+simData(dataSelection(i)));
-    u_rms(i) = calculateTotalInputUsed(out);
-    names{i} = replace(erase(simData(dataSelection(i)),".mat"),"_","\_");
-end
-figure()
-bar(1:i,u_rms);
-set(gca, 'XTick', 1:length(names),'XTickLabel',names);
-title('RMS Input Values')
-
 %% Minimum Obstacle Distance Comparison
 names = {};
 for i = 1:length(dataSelection)
@@ -126,7 +143,4 @@
 figure()
 bar(1:i,clearanceTime);
 set(gca, 'XTick', 1:length(names),'XTickLabel',names);
-title('Obstacle Clearance Time')
-
-%% Potential Comparison
-plotPotentials()
+title('Obstacle Clearance Time')