plots 3 and 4

plot_1.m

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+clear;
+% Load data
+dataPath = fullfile('.', 'sandbox', 'plot1');
+simHists = dir(dataPath); simHists = simHists(3:end);
+simInits = simHists(endsWith({simHists.name}, 'miSimInits.mat'));
+simHists = simHists(endsWith({simHists.name}, 'miSimHist.mat'));
+assert(length(simHists) == length(simInits), "input data availability mismatch");
+
+% Initialize plotting data
+nRuns = length(simHists);
+Cfinal = NaN(nRuns, 1);
+n = NaN(nRuns, 1);
+doubleIntegrator = NaN(nRuns, 1);
+numObjective = NaN(nRuns, 1);
+positions = cell(6, nRuns);
+commsRadius = NaN(nRuns, 1);
+collisionRadius = NaN(nRuns, 1);
+
+% Aggregate relevant data
+for ii = 1:length(simHists)
+    initName = strrep(simInits(ii).name, "_miSimInits.mat", "");
+    histName = strrep(simHists(ii).name, "_miSimHist.mat", "");
+    assert(initName == histName);
+
+    init = load(fullfile(simInits(ii).folder, simInits(ii).name));
+    hist = load(fullfile(simHists(ii).folder, simHists(ii).name));
+
+    % Stash relevant data
+    Cfinal(ii) = hist.out.perf(end) / init.objectiveIntegral;
+    n(ii) = init.numAgents;
+    doubleIntegrator(ii) = init.useDoubleIntegrator;
+    numObjective(ii) = size(init.objectivePos, 1);
+    commsRadius(ii) = unique(init.comRange);
+    collisionRadius(ii) = unique(init.collisionRadius);
+    for jj = 1:length(hist.out.agent)
+        alphaDist(jj, ii) = hist.out.agent(jj).sensor.alphaDist;
+        positions{jj, ii} = hist.out.agent(jj).pos;
+        assert(hist.out.agent(jj).commsRadius == commsRadius(ii));
+        assert(hist.out.agent(jj).collisionRadius == collisionRadius(ii));
+    end
+
+    alphaDist2 = unique(alphaDist);
+    if length(alphaDist2) > 1
+        alphaDist2 = alphaDist2(1);
+    end
+    if doubleIntegrator(ii) && unique(alphaDist(:, ii)) == alphaDist2 && numObjective(ii) == 1
+        a2betaIdx = ii;
+        a2beta = struct("init", init, "hist", hist.out);
+    end
+end
+
+commsRadius = unique(commsRadius); assert(isscalar(commsRadius));
+collisionRadius = unique(collisionRadius); assert(isscalar(collisionRadius));
+sensors = flip(unique(alphaDist(1, :)));
+
+config = [];
+for ii = 1:length(simHists)
+    % number of agents
+    s = num2str(n(ii));
+
+    % number of objectives
+    if numObjective(ii) == 1
+        s = strcat(s, "_A");
+    elseif numObjective(ii) == 2
+        s = strcat(s, "_B");
+    end
+
+    % sensor pararmeter set
+    if alphaDist(1, ii) == sensors(1)
+        s = strcat(s, "_I");
+    elseif alphaDist(1, ii) == sensors(2)
+        s = strcat(s, "_II");
+    end
+
+    % agent dynamics
+    if ~doubleIntegrator(ii)
+        s = strcat(s, '_alpha');
+    elseif doubleIntegrator(ii)
+        s = strcat(s, '_beta');
+    end
+    config = [config; s];
+end
+
+%%
+close all;
+f1 = figure;
+x1 = axes;
+
+n_unique = sort(unique(n));
+C = [];
+for ii = 1:length(n_unique)
+    nIdx = n == n_unique(ii);
+    C = [C; [Cfinal(nIdx)]'];
+end
+bar(C);
+set(x1, 'XTickLabel', string(n_unique));
+xlabel("Number of agents");
+ylabel("Final coverage (normalized)");
+title("Final performance of parameterizations");
+legend(["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex", "Location", "northwest");
+grid("on");
+ylim([0, 1/2]);
+
+%%
+f2 = figure;
+x2 = axes;
+
+% Compute pairwise distances between agents in each column of positions
+% cell array
+% Compute pairwise distances between agents
+maxPairs = nchoosek(6, 2); % 15 pairs for max 6 agents
+pairDist = cell(maxPairs, nRuns);
+for ii = 1:nRuns
+    pp = 0;
+    for jj = 1:n(ii)-1
+        for kk = jj+1:n(ii)
+            pp = pp + 1;
+            pairDist{pp, ii} = vecnorm(positions{jj, ii} - positions{kk, ii}, 2, 2);
+        end
+    end
+end
+
+% Cap pairwise distances at communications range
+for ii = 1:nRuns
+    nPairs = nchoosek(n(ii), 2);
+    for pp = 1:nPairs
+        pairDist{pp, ii} = min(pairDist{pp, ii}, commsRadius);
+    end
+end
+
+% Compute mean, min, max pairwise distance across all pairs and timesteps per run
+meanPairDist = NaN(nRuns, 1);
+minPairDist = NaN(nRuns, 1);
+maxPairDist = NaN(nRuns, 1);
+for ii = 1:nRuns
+    nPairs = nchoosek(n(ii), 2);
+    D = vertcat(pairDist{1:nPairs, ii});
+    meanPairDist(ii) = mean(D);
+    minPairDist(ii) = min(D);
+    maxPairDist(ii) = max(D);
+end
+
+% Group pairwise distance stats by n-value (same layout as bar plot)
+nConfigs = nRuns / length(n_unique);
+meanD = NaN(length(n_unique), nConfigs);
+minD = NaN(length(n_unique), nConfigs);
+maxD = NaN(length(n_unique), nConfigs);
+for ii = 1:length(n_unique)
+    idx = find(n == n_unique(ii));
+    meanD(ii, :) = meanPairDist(idx)';
+    minD(ii, :) = minPairDist(idx)';
+    maxD(ii, :) = maxPairDist(idx)';
+end
+
+% Plot whiskers (min to max) with mean markers
+nGroups = length(n_unique);
+barWidth = 0.8;
+groupWidth = barWidth / nConfigs;
+hold(x2, 'on');
+for jj = 1:nConfigs
+    xPos = (1:nGroups) + (jj - (nConfigs + 1) / 2) * groupWidth;
+    errorbar(x2, xPos, meanD(:, jj), meanD(:, jj) - minD(:, jj), maxD(:, jj) - meanD(:, jj), ...
+        'o', 'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 10);
+end
+hold(x2, 'off');
+set(x2, 'XTick', 1:nGroups, 'XTickLabel', string(n_unique));
+xlabel(x2, "Number of agents");
+ylabel(x2, "Pairwise distance");
+title(x2, "Pairwise Agent Distances (min/mean/max)");
+legend(x2, ["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex");
+grid(x2, "on");
+
+yline(collisionRadius, 'r--', "Label", "Collision Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
+yline(commsRadius, 'r--', "Label", "Communications Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
+ylim([0, commsRadius + 5]);