results plot1 WIP

@agent/initialize.m

@@ -15,6 +15,7 @@ function obj = initialize(obj, pos, collisionGeometry, sensorModel, comRange, ma
     end
 
     obj.pos = pos;
+    obj.lastPos = pos;
     obj.vel = zeros(1, 3);
     obj.lastVel = zeros(1, 3);
     obj.collisionGeometry = collisionGeometry;

@agent/run.m

@@ -14,6 +14,13 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents, useD
         obj (1, 1) {mustBeA(obj, "agent")};
     end
 
+    % Always update lastPos/lastVel so constrainMotion evaluates barriers at
+    % the correct (most recent) position, even when this agent has no partition.
+    obj.lastPos = obj.pos;
+    if useDoubleIntegrator
+        obj.lastVel = obj.vel;
+    end
+
     % Collect objective function values across partition
     partitionMask = partitioning == index;
     if ~any(partitionMask(:))
@@ -79,10 +86,8 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents, useD
     gradNorm = norm(gradC);
 
     % Compute unconstrained next state
-    obj.lastPos = obj.pos;
     if useDoubleIntegrator
         % Double-integrator: gradient produces desired acceleration with damping
-        obj.lastVel = obj.vel;
         if gradNorm < 1e-100
             a_gradient = zeros(1, 3);
         else

@miSim/constrainMotion.m

@@ -39,10 +39,10 @@ function [obj] = constrainMotion(obj)
     h(logical(eye(nAgents))) = 0; % self value is 0
     for ii = 1:(nAgents - 1)
         for jj = (ii + 1):nAgents
-            h(ii, jj) = norm(obj.agents{ii}.pos - obj.agents{jj}.pos)^2 - (obj.agents{ii}.collisionGeometry.radius + obj.agents{jj}.collisionGeometry.radius)^2;
+            h(ii, jj) = norm(obj.agents{ii}.lastPos - obj.agents{jj}.lastPos)^2 - (obj.agents{ii}.collisionGeometry.radius + obj.agents{jj}.collisionGeometry.radius)^2;
             h(jj, ii) = h(ii, jj);
 
-            A(kk, (3 * ii - 2):(3 * ii)) =  -2 * (obj.agents{ii}.pos - obj.agents{jj}.pos);
+            A(kk, (3 * ii - 2):(3 * ii)) =  -2 * (obj.agents{ii}.lastPos - obj.agents{jj}.lastPos);
             A(kk, (3 * jj - 2):(3 * jj)) = -A(kk, (3 * ii - 2):(3 * ii));
             % Slack derived from existing params: recovery velocity = max gradient approach velocity.
             % Correction splits between 2 agents, so |A| = 2*r_sum
@@ -69,11 +69,11 @@ function [obj] = constrainMotion(obj)
     for ii = 1:nAgents
         for jj = 1:size(obj.obstacles, 1)
             % find closest position to agent on/in obstacle
-            cPos = obj.obstacles{jj}.closestToPoint(obj.agents{ii}.pos);
+            cPos = obj.obstacles{jj}.closestToPoint(obj.agents{ii}.lastPos);
 
-            hObs(ii, jj) = dot(obj.agents{ii}.pos - cPos, obj.agents{ii}.pos - cPos) - obj.agents{ii}.collisionGeometry.radius^2;
+            hObs(ii, jj) = dot(obj.agents{ii}.lastPos - cPos, obj.agents{ii}.lastPos - cPos) - obj.agents{ii}.collisionGeometry.radius^2;
 
-            A(kk, (3 * ii - 2):(3 * ii)) = -2 * (obj.agents{ii}.pos - cPos);
+            A(kk, (3 * ii - 2):(3 * ii)) = -2 * (obj.agents{ii}.lastPos - cPos);
             % Floor for single-agent constraint: full correction on one agent, |A| = 2*r_i
             r_i = obj.agents{ii}.collisionGeometry.radius;
             v_max_i = obj.agents{ii}.initialStepSize / obj.timestep;
@@ -93,37 +93,37 @@ function [obj] = constrainMotion(obj)
     h_xMin = 0.0; h_xMax = 0.0; h_yMin = 0.0; h_yMax = 0.0; h_zMin = 0.0; h_zMax = 0.0;
     for ii = 1:nAgents
         % X minimum
-        h_xMin = (obj.agents{ii}.pos(1) - obj.domain.minCorner(1)) - obj.agents{ii}.collisionGeometry.radius;
+        h_xMin = (obj.agents{ii}.lastPos(1) - obj.domain.minCorner(1)) - obj.agents{ii}.collisionGeometry.radius;
         A(kk, (3 * ii - 2):(3 * ii)) = [-1, 0, 0];
         b(kk) = obj.barrierGain * max(0, h_xMin)^obj.barrierExponent;
         kk = kk + 1;
 
         % X maximum
-        h_xMax = (obj.domain.maxCorner(1) - obj.agents{ii}.pos(1)) - obj.agents{ii}.collisionGeometry.radius;
+        h_xMax = (obj.domain.maxCorner(1) - obj.agents{ii}.lastPos(1)) - obj.agents{ii}.collisionGeometry.radius;
         A(kk, (3 * ii - 2):(3 * ii)) = [1, 0, 0];
         b(kk) = obj.barrierGain * max(0, h_xMax)^obj.barrierExponent;
         kk = kk + 1;
 
         % Y minimum
-        h_yMin = (obj.agents{ii}.pos(2) - obj.domain.minCorner(2)) - obj.agents{ii}.collisionGeometry.radius;
+        h_yMin = (obj.agents{ii}.lastPos(2) - obj.domain.minCorner(2)) - obj.agents{ii}.collisionGeometry.radius;
         A(kk, (3 * ii - 2):(3 * ii)) = [0, -1, 0];
         b(kk) = obj.barrierGain * max(0, h_yMin)^obj.barrierExponent;
         kk = kk + 1;
 
         % Y maximum
-        h_yMax = (obj.domain.maxCorner(2) - obj.agents{ii}.pos(2)) - obj.agents{ii}.collisionGeometry.radius;
+        h_yMax = (obj.domain.maxCorner(2) - obj.agents{ii}.lastPos(2)) - obj.agents{ii}.collisionGeometry.radius;
         A(kk, (3 * ii - 2):(3 * ii)) = [0, 1, 0];
         b(kk) = obj.barrierGain * max(0, h_yMax)^obj.barrierExponent;
         kk = kk + 1;
 
         % Z minimum — enforce z >= minAlt + radius (not just z >= domain floor + radius)
-        h_zMin = (obj.agents{ii}.pos(3) - obj.minAlt) - obj.agents{ii}.collisionGeometry.radius;
+        h_zMin = (obj.agents{ii}.lastPos(3) - obj.minAlt) - obj.agents{ii}.collisionGeometry.radius;
         A(kk, (3 * ii - 2):(3 * ii)) = [0, 0, -1];
         b(kk) = obj.barrierGain * max(0, h_zMin)^obj.barrierExponent;
         kk = kk + 1;
 
         % Z maximum
-        h_zMax = (obj.domain.maxCorner(3) - obj.agents{ii}.pos(3)) - obj.agents{ii}.collisionGeometry.radius;
+        h_zMax = (obj.domain.maxCorner(3) - obj.agents{ii}.lastPos(3)) - obj.agents{ii}.collisionGeometry.radius;
         A(kk, (3 * ii - 2):(3 * ii)) = [0, 0, 1];
         b(kk) = obj.barrierGain * max(0, h_zMax)^obj.barrierExponent;
         kk = kk + 1;
@@ -145,9 +145,9 @@ function [obj] = constrainMotion(obj)
             if obj.constraintAdjacencyMatrix(ii, jj)
                 paddingFactor = 0.9; % Barrier at 90% of actual range; real comms still work beyond this
                 r_comms = paddingFactor * min([obj.agents{ii}.commsGeometry.radius, obj.agents{jj}.commsGeometry.radius]);
-                hComms(ii, jj) = r_comms^2 - norm(obj.agents{ii}.pos - obj.agents{jj}.pos)^2;
+                hComms(ii, jj) = r_comms^2 - norm(obj.agents{ii}.lastPos - obj.agents{jj}.lastPos)^2;
 
-                A(kk, (3 * ii - 2):(3 * ii)) =  2 * (obj.agents{ii}.pos - obj.agents{jj}.pos);
+                A(kk, (3 * ii - 2):(3 * ii)) =  2 * (obj.agents{ii}.lastPos - obj.agents{jj}.lastPos);
                 A(kk, (3 * jj - 2):(3 * jj)) = -A(kk, (3 * ii - 2):(3 * ii));
 
                 % One-step forward invariance: b = h/dt ensures h cannot

@miSim/miSim.m

@@ -7,7 +7,6 @@ classdef miSim
         timestepIndex = NaN; % index of the current timestep (useful for time-indexed arrays)
         maxIter = NaN; % maximum number of simulation iterations
         domain;
-        objective;
         obstacles; % geometries that define obstacles within the domain
         agents; % agents that move within the domain
         adjacency = false(0, 0); % Adjacency matrix representing communications network graph
@@ -67,7 +66,6 @@ classdef miSim
                 obj (1, 1) miSim
             end
             obj.domain = rectangularPrism;
-            obj.objective = sensingObjective;
             obj.obstacles = {rectangularPrism};
             obj.agents = {agent};
         end

@miSim/teardown.m

@@ -39,7 +39,6 @@ function obj = teardown(obj)
     obj.timestepIndex = NaN;
     obj.maxIter = NaN;
     obj.domain = rectangularPrism;
-    obj.objective = sensingObjective;
     obj.obstacles = cell(0, 1);
     obj.agents = cell(0, 1);
     obj.adjacency = NaN;

@miSim/validate.m

@@ -7,11 +7,11 @@ function validate(obj)
 
     %% Communications Network Validators
     if max(conncomp(graph(obj.adjacency))) ~= 1
-        warning("Network is not connected");
+        error("Network is not connected");
     end
 
     if any(obj.adjacency - obj.constraintAdjacencyMatrix < 0, "all")
-        warning("Eliminated network connections that were necessary");
+        error("Eliminated network connections that were necessary");
     end
 
     %% Obstacle Validators
@@ -20,10 +20,9 @@ function validate(obj)
         for kk = 1:size(obj.agents, 1)
             P = min(max(obj.agents{kk}.pos, obj.obstacles{jj}.minCorner), obj.obstacles{jj}.maxCorner);
             d = obj.agents{kk}.pos - P;
-            if dot(d, d) < obj.agents{kk}.collisionGeometry.radius^2
+            if dot(d, d) < obj.agents{kk}.collisionGeometry.radius^2 - 1e-3
-                warning("%s colliding with %s by %d", obj.agents{kk}.label, obj.obstacles{jj}.label, dot(d, d) - obj.agents{kk}.collisionGeometry.radius^2); % this will cause quadprog to fail
+                error("%s colliding with %s by %d", obj.agents{kk}.label, obj.obstacles{jj}.label, - dot(d, d) + obj.agents{kk}.collisionGeometry.radius^2); % this will cause quadprog to fail
             end
         end
     end
-
 end

@miSim/writeInits.m

@@ -14,6 +14,9 @@ function writeInits(obj)
     comRanges = cellfun(@(x) x.commsGeometry.radius, obj.agents);
     initialStepSize = cellfun(@(x) x.initialStepSize, obj.agents);
     pos = cell2mat(cellfun(@(x) x.pos, obj.agents, 'UniformOutput', false));
+    obsMinCorners = cell2mat(cellfun(@(x) x.minCorner, obj.obstacles, 'UniformOutput', false));
+    obsMaxCorners = cell2mat(cellfun(@(x) x.maxCorner, obj.obstacles, 'UniformOutput', false));
+
 
     % Combine with simulation parameters
     inits = struct("timestep", obj.timestep, "maxIter", obj.maxIter, "minAlt", obj.obstacles{end}.maxCorner(3), ...
@@ -24,7 +27,9 @@ function writeInits(obj)
                     "useDoubleIntegrator", obj.useDoubleIntegrator, "dampingCoeff", obj.dampingCoeff, ...
                     "alphaDist", alphaDist, "betaDist", betaDist, "alphaTilt", alphaTilt, "betaTilt", betaTilt, ...
                     ... % ^^^ PARAMETERS ^^^ | vvv STATES vvv
-                    "pos", pos); % still needs obstacle states and objective state
+                    "pos", pos, "objectivePos", obj.domain.objective.groundPos, "objectiveSigma", obj.domain.objective.objectiveSigma, ...
+                    "obsMinCorners", obsMinCorners, "obsMaxCorners", obsMaxCorners, ...
+                    "objectiveIntegral", sum(obj.domain.objective.values(:)));
 
     % Save all parameters to output file
     initsFile = strcat(obj.artifactName, "_miSimInits");

@sensingObjective/initialize.m

@@ -1,4 +1,4 @@
-function obj = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange, sensorPerformanceMinimum)
+function obj = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange, sensorPerformanceMinimum, objectiveMu, objectiveSigma)
     arguments (Input)
         obj (1,1) {mustBeA(obj, "sensingObjective")};
         objectiveFunction (1, 1) {mustBeA(objectiveFunction, "function_handle")};
@@ -6,6 +6,8 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
         discretizationStep (1, 1) double = 1;
         protectedRange (1, 1) double = 1;
         sensorPerformanceMinimum (1, 1) double = 1e-6;
+        objectiveMu (:, 2) double = NaN(1, 2);
+        objectiveSigma (:, 2, 2) double = NaN(1, 2, 2);
     end
     arguments (Output)
         obj (1,1) {mustBeA(obj, "sensingObjective")};
@@ -37,8 +39,13 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
 
     % store ground position
     idx = obj.values == 1;
+    if any(isnan(objectiveMu))
         obj.groundPos = [obj.X(idx), obj.Y(idx)];
         obj.groundPos = obj.groundPos(1, 1:2); % for safety, in case 2 points are maximal (somehow)
+    else
+        obj.groundPos = objectiveMu;
+    end
+    obj.objectiveSigma = objectiveSigma;
 
-    assert(domain.distance([obj.groundPos, domain.center(3)]) > protectedRange, "Domain is crowding the sensing objective")
+    assert(domain.distance([obj.groundPos, ones(size(obj.groundPos, 1), 1) .* domain.center(3)]) > protectedRange, "Domain is crowding the sensing objective")
 end

@sensingObjective/initializeRandomMvnpdf.m

@@ -11,7 +11,7 @@ function obj = initializeRandomMvnpdf(obj, domain, discretizationStep, protected
 
     % Set random objective position
     mu = domain.minCorner;
-    while domain.distance(mu) < protectedRange
+    while domain.distance(mu) < protectedRange * 1.01
         mu = domain.random();
     end
 

@sensingObjective/sensingObjective.m

@@ -2,7 +2,8 @@ classdef sensingObjective
     % Sensing objective definition parent class
     properties (SetAccess = private, GetAccess = public)
         label = "";
-        groundPos = [NaN, NaN];
+        groundPos = NaN(1, 2);
+        objectiveSigma = NaN(1, 2, 2);
         discretizationStep = NaN;
         X = [];
         Y = [];

aerpaw/config/scenario.csv

@@ -1,2 +1,2 @@
 timestep, maxIter, minAlt, discretizationStep, protectedRange, initialStepSize, barrierGain, barrierExponent, collisionRadius, comRange, alphaDist, betaDist, alphaTilt, betaTilt, domainMin, domainMax, objectivePos, objectiveVar, sensorPerformanceMinimum, initialPositions, numObstacles, obstacleMin, obstacleMax, useDoubleIntegrator, dampingCoeff, useFixedTopology
-5, 100, 30.0, 0.1, 2.0, 2.0, 100, 3, "5.0, 5.0", "25.0, 25.0", "80.0, 80.0", "0.25, 0.25", "5.0, 5.0", "0.1, 0.1", "0.0, 0.0, 0.0", "80.0, 80.0, 80.0", "55.0, 55.0", "40, 25, 25, 40", 0.15, "15.0, 10.0, 40.0, 5.0, 10.0, 45.0", 1, "1.0, 25.0, 0.0", "30.0, 30.0, 50.0", 1, 2.0, 1
+1, 150, 30.0, 0.1, 2.0, 1, 1, 1, "5.0, 5.0", "25.0, 25.0", "80.0, 80.0", "0.25, 0.25", "5.0, 5.0", "0.1, 0.1", "0.0, 0.0, 0.0", "80.0, 80.0, 80.0", "55.0, 55.0", "40, 25, 25, 40", 0.15, "15.0, 10.0, 40.0, 5.0, 10.0, 45.0", 1, "1.0, 25.0, 0.0", "30.0, 30.0, 50.0", 1, 2.0, 1

plot1.m

@@ -0,0 +1,81 @@
+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
+Cfinal = NaN(12, 1);
+n = NaN(12, 1);
+doubleIntegrator = NaN(12, 1);
+numObjective = NaN(12, 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);
+    for jj = 1:length(hist.out.agent)
+        alphaDist(jj, ii) = hist.out.agent(jj).sensor.alphaDist;
+    end
+end
+
+sensors = 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;
+f = figure;
+x = axes; grid(x, "on");
+
+n_unique = sort(unique(n));
+C = [];
+for ii = 1:length(n_unique)
+    nIdx = n == n_unique(ii);
+    C = [C; [Cfinal(nIdx)]']; 
+end
+bar(C);
+xlabel("Number of agents");
+ylabel("Final coverage (fraction of maximum)");
+title("Final performance of parameterizations");
+legend(["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex");
+grid("on");
+keyboard
+

resources/project/o8EfsliYZoi0Pg0hEr9bLYBd-k0/C05cVxQ9NvFXxKc5bQbtmN_GvSAd.xml

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Info/>

resources/project/o8EfsliYZoi0Pg0hEr9bLYBd-k0/C05cVxQ9NvFXxKc5bQbtmN_GvSAp.xml

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Info location="1" type="DIR_SIGNIFIER"/>

resources/project/q138eJA8Ym4eSfM3RFMVvg63QtU/o8EfsliYZoi0Pg0hEr9bLYBd-k0d.xml

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Info/>

resources/project/q138eJA8Ym4eSfM3RFMVvg63QtU/o8EfsliYZoi0Pg0hEr9bLYBd-k0p.xml

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Info location="plot1" type="File"/>

test/results.m

@@ -0,0 +1,298 @@
+classdef results < matlab.unittest.TestCase
+    properties (Constant, Access = private)
+        seed = 1;
+    end
+
+    properties (Access = private)
+        % System under test
+        testClass = miSim;
+
+        %% Diagnostic Parameters
+        % No effect on simulation dynamics
+        makeVideo = false; % disable video writing for big performance increase
+        makePlots = true; % disable plotting for big performance increase (also disables video)
+        plotCommsGeometry = false; % disable plotting communications geometries
+
+        %% Fixed Test Parameters
+        useFixedTopology = true; % No lesser neighbor, fixed network instead
+        minDimension = 50; % minimum domain size
+        maxDimension = 100; % maximum domain size
+        discretizationStep = 0.1;
+        protectedRange = 5;
+        collisionRadius = 5;
+        sensorPerformanceMinimum = 0.005;
+        comRange = 20;
+        maxIter = 200;
+        initialStepSize = 1;
+        numObstacles = 3;
+        barrierGain = 1;
+        barrierExponent = 1;
+        timestep = 0.5;
+        dampingCoeff = 2;
+    end
+
+    properties (TestParameter)
+        %% Test Iterations
+        % Specific parameter combos to run iterations on
+        n = struct('n3', 3, 'n5', 5, 'n6', 6); % number of agents
+        config = results.makeConfigs();
+    end
+
+    methods (TestClassSetup)
+        function setSeed(tc)
+            rng(tc.seed);
+        end
+    end
+
+    methods (TestMethodSetup)
+        % % Generate a random domain for each test
+        % function tc = setDomain(tc)
+        %     tc.testClass.domain = rectangularPrism;
+        %     % random integer-dimensioned cubic domain
+        %     tc.testClass.domain = tc.testClass.domain.initializeRandom(REGION_TYPE.DOMAIN, "Domain", tc.minDimension);
+        %     % Random bivariate normal PDF objective
+        %     tc.testClass.domain.objective = tc.testClass.domain.objective.initializeRandomMvnpdf(tc.testClass.domain, tc.discretizationStep, tc.protectedRange);
+        % end
+    end
+
+    methods (Static, Access = private)
+        function c = makeConfigs()
+            rng(results.seed);
+            abMin = 6; % alpha*beta >= 6 ensures membership(0) = tanh(3) >= 0.995
+            alphaDist = rand(1, 2) .* [100, 100];
+            betaDist = abMin ./ alphaDist + rand(1, 2) .* (20 - abMin ./ alphaDist);
+            alphaTilt = 10 + rand(1, 2) .* [20, 20];
+            betaTilt = abMin ./ alphaTilt + rand(1, 2) .* (50 - abMin ./ alphaTilt);
+            sensors = struct('alphaDist', num2cell(alphaDist), 'alphaTilt', num2cell(alphaTilt), 'betaDist', num2cell(betaDist), 'betaTilt', num2cell(betaTilt));
+            sensor1 = sigmoidSensor;
+            sensor2 = sigmoidSensor;
+            sensor1 = sensor1.initialize(sensors(1).alphaDist, sensors(1).betaDist, sensors(1).alphaTilt, sensors(1).betaTilt);
+            sensor2 = sensor2.initialize(sensors(2).alphaDist, sensors(2).betaDist, sensors(2).alphaTilt, sensors(2).betaTilt);
+            sensor1.plotParameters;
+            sensor2.plotParameters;
+            c = struct('A_1_alpha', struct('numDist', 1, 'sensor', sensors(1), 'doubleIntegrator', false), ...
+                        'A_1_beta',  struct('numDist', 1, 'sensor', sensors(1), 'doubleIntegrator', true), ...
+                        'A_2_alpha', struct('numDist', 1, 'sensor', sensors(2), 'doubleIntegrator', false), ...
+                        'B_1_beta',  struct('numDist', 2, 'sensor', sensors(1), 'doubleIntegrator', true));
+        end
+    end
+
+    methods (Test)
+        function plot1_runs(tc, n, config)
+        % OVERRIDES
+        % function plot1_runs(tc)
+            % n = 3;
+            % config = struct('numDist', 1, 'sensor', struct('alphaDist', 100, 'alphaTilt', 2, 'betaDist', 10, 'betaTilt', 0.5), 'doubleIntegrator', false);
+           
+            % Set up random cube domain
+            minAlt = tc.minDimension(1) * rand() * 0.5;
+            tc.testClass.domain = tc.testClass.domain.initializeRandom(REGION_TYPE.DOMAIN, "Domain", tc.minDimension, tc.maxDimension, tc.testClass.domain, minAlt);
+            % Place sensing objective(s)
+            objectiveMu = [];
+            objectiveSigma = [];
+            for ii = 1:config.numDist
+                mu = tc.testClass.domain.minCorner;
+                while tc.testClass.domain.distance(mu) < tc.protectedRange * 1.01
+                    mu = tc.testClass.domain.random();
+                end
+                notPosDef = true;
+                while notPosDef
+                    sig = reshape(sort(rand(1, 4) * min(tc.testClass.domain.dimensions(1:2))), [1, 2, 2]);
+                    sig(1, 2, 1) = sig(1, 1, 2);
+                    [~, notPosDef] = chol(squeeze(sig));
+                end
+                objectiveMu = [objectiveMu; mu(1:2)];
+                objectiveSigma = cat(1, objectiveSigma, sig);
+            end
+            tc.testClass.domain.objective = tc.testClass.domain.objective.initialize(objectiveFunctionWrapper(objectiveMu, objectiveSigma), tc.testClass.domain, tc.discretizationStep, tc.protectedRange, tc.sensorPerformanceMinimum, objectiveMu, objectiveSigma);
+
+            % Initialize agents
+            agents = cell(n, 1);
+            [agents{:}] = deal(agent);
+
+            % Initialize sensor model
+            sensorModel = sigmoidSensor;
+            sensorModel = sensorModel.initialize(config.sensor.alphaDist, config.sensor.betaDist, config.sensor.alphaTilt, config.sensor.betaTilt);
+
+            % Place agents in a quadrant that contains no objective peaks
+            midXY = (tc.testClass.domain.minCorner(1:2) + tc.testClass.domain.maxCorner(1:2)) / 2;
+            occupied = false(2, 2);
+            for ii = 1:size(objectiveMu, 1)
+                occupied(1 + (objectiveMu(ii, 1) >= midXY(1)), ...
+                         1 + (objectiveMu(ii, 2) >= midXY(2))) = true;
+            end
+            freeQ = find(~occupied);
+            if isempty(freeQ)
+                qi = 1;
+            else
+                qi = freeQ(randi(numel(freeQ)));
+            end
+            [xi, yi] = ind2sub([2, 2], qi);
+            xLim = [tc.testClass.domain.minCorner(1), midXY(1), tc.testClass.domain.maxCorner(1)];
+            yLim = [tc.testClass.domain.minCorner(2), midXY(2), tc.testClass.domain.maxCorner(2)];
+            agentBounds = [max(xLim(xi),   tc.testClass.domain.minCorner(1) + tc.collisionRadius), ...
+                           max(yLim(yi),   tc.testClass.domain.minCorner(2) + tc.collisionRadius), ...
+                           minAlt + tc.collisionRadius; ...
+                           min(xLim(xi+1), tc.testClass.domain.maxCorner(1) - tc.collisionRadius), ...
+                           min(yLim(yi+1), tc.testClass.domain.maxCorner(2) - tc.collisionRadius), ...
+                           tc.testClass.domain.maxCorner(3) - tc.collisionRadius];
+            collisionGeometry = spherical;
+            for jj = 1:n
+                retry = true;
+                while retry
+                    retry = false;
+
+                    if jj == 1
+                        % First agent: uniform random within placement bounds
+                        agentPos = agentBounds(1, :) + (agentBounds(2, :) - agentBounds(1, :)) .* rand(1, 3);
+                    else
+                        % Sample near centroid of existing agents to maximize
+                        % probability of being within comRange of all others
+                        positions = cell2mat(cellfun(@(x) x.pos, agents(1:(jj-1)), 'UniformOutput', false));
+                        centroid = mean(positions, 1);
+                        maxSpread = max(vecnorm(positions - centroid, 2, 2));
+                        safeRadius = tc.comRange - maxSpread;
+
+                        if safeRadius > 2 * tc.collisionRadius
+                            % Uniform random within guaranteed-connected sphere
+                            dir = randn(1, 3);
+                            dir = dir / norm(dir);
+                            r = safeRadius * rand()^(1/3);
+                            agentPos = centroid + r * dir;
+                        else
+                            % Safe sphere too small; sample within comms sphere
+                            % of random existing agent (comRange check below)
+                            baseIdx = randi(jj - 1);
+                            agentPos = agents{baseIdx}.commsGeometry.random();
+                        end
+                    end
+
+                    % Check within placement bounds
+                    if any(agentPos <= agentBounds(1, :)) || any(agentPos >= agentBounds(2, :))
+                        retry = true;
+                        continue;
+                    end
+
+                    % Check sensor performance threshold
+                    if sensorModel.sensorPerformance(agentPos, [agentPos(1:2), 0]) < tc.sensorPerformanceMinimum * 10
+                        retry = true;
+                        continue;
+                    end
+
+                    % Check within comRange of ALL existing agents (complete graph)
+                    for kk = 1:(jj - 1)
+                        if norm(agents{kk}.pos - agentPos) >= tc.comRange
+                            retry = true;
+                            break;
+                        end
+                    end
+                    if retry, continue; end
+
+                    % Check collision with ALL existing agents
+                    for kk = 1:(jj - 1)
+                        if norm(agents{kk}.pos - agentPos) < agents{kk}.collisionGeometry.radius + tc.collisionRadius
+                            retry = true;
+                            break;
+                        end
+                    end
+                end
+
+                % Initialize agent
+                collisionGeometry = collisionGeometry.initialize(agentPos, tc.collisionRadius, REGION_TYPE.COLLISION, sprintf("Agent %d Collision Region", jj));
+                agents{jj} = agents{jj}.initialize(agentPos, collisionGeometry, sensorModel, tc.comRange, tc.maxIter, tc.initialStepSize, sprintf("Agent %d", jj), tc.plotCommsGeometry);
+            end
+
+            % Randomly shuffle agents to vary index-based topology
+            agents = agents(randperm(numel(agents)));
+
+            % Add random obstacles
+            obstacles = cell(tc.numObstacles, 1);
+            [obstacles{:}] = deal(rectangularPrism);
+
+            % Define target region for obstacles (between agents and objective)
+            agentExtent = max(cell2mat(cellfun(@(x) x.pos(1:2), agents, "UniformOutput", false))) + max(cellfun(@(x) x.collisionGeometry.radius, agents));
+            objExtent = tc.testClass.domain.objective.groundPos - tc.testClass.domain.objective.protectedRange;
+            % Per-axis: use gap if valid, else fall back to full domain
+            obsMin = zeros(1, 2);
+            obsMax = zeros(1, 2);
+            for dim = 1:2
+                if agentExtent(dim) < objExtent(dim)
+                    obsMin(dim) = agentExtent(dim);
+                    obsMax(dim) = objExtent(dim);
+                else
+                    obsMin(dim) = tc.testClass.domain.minCorner(dim);
+                    obsMax(dim) = tc.testClass.domain.maxCorner(dim);
+                end
+            end
+
+            for jj = 1:size(obstacles, 1)
+                retry = true;
+                while retry
+                    retry = false;
+
+                    % Generate corners within target region
+                    cornersXY = obsMin + sort(rand(2, 2), 1, "ascend") .* (obsMax - obsMin);
+                    corners = [cornersXY, [minAlt; minAlt + rand * (tc.testClass.domain.maxCorner(3) - minAlt)]];
+
+                    % Initialize obstacle using proposed coordinates
+                    obstacles{jj} = obstacles{jj}.initialize(corners, REGION_TYPE.OBSTACLE, sprintf("Obstacle %d", jj));
+
+                    % Make sure the obstacle doesn't crowd the objective
+                    for kk = 1:size(tc.testClass.domain.objective.groundPos, 1)
+                        if ~retry && obstacles{jj}.distance([tc.testClass.domain.objective.groundPos(kk, 1:2), minAlt]) <= tc.testClass.domain.objective.protectedRange
+                            retry = true;
+                            continue;
+                        end
+                    end
+
+                    % Check if the obstacle collides with an existing obstacle
+                    if ~retry && jj > 1 && tc.obstacleCollisionCheck(obstacles(1:(jj - 1)), obstacles{jj})
+                        retry = true;
+                        continue;
+                    end
+
+                    % Check if the obstacle collides with an agent
+                    if ~retry
+                        for kk = 1:size(agents, 1)
+                            P = min(max(agents{kk}.pos, obstacles{jj}.minCorner), obstacles{jj}.maxCorner);
+                            d = agents{kk}.pos - P;
+                            if dot(d, d) <= agents{kk}.collisionGeometry.radius^2
+                                retry = true;
+                                break;
+                            end
+                        end
+                    end
+
+                    if retry
+                        continue;
+                    end
+                end
+            end
+
+            % Set up simulation
+            tc.testClass = tc.testClass.initialize(tc.testClass.domain, agents, tc.barrierGain, tc.barrierExponent, minAlt, tc.timestep, tc.maxIter, obstacles, tc.makePlots, tc.makeVideo, config.doubleIntegrator, tc.dampingCoeff, tc.useFixedTopology);
+
+            % Save simulation parameters to output file
+            tc.testClass.writeInits();
+
+            % Run
+            tc.testClass = tc.testClass.run();
+
+            % Cleanup
+            tc.testClass = tc.testClass.teardown();
+        end
+    end
+
+    methods
+        function c = obstacleCollisionCheck(~, obstacles, obstacle)
+            % Check if the obstacle intersects with any other obstacles
+            c = false;
+            for ii = 1:size(obstacles, 1)
+                if geometryIntersects(obstacles{ii}, obstacle)
+                    c = true;
+                    return;
+                end
+            end
+        end
+    end
+end

util/objectiveFunctionWrapper.m

@@ -4,12 +4,12 @@ function f = objectiveFunctionWrapper(center, sigma)
     % composite objectives in particular
     arguments (Input)
         center (:, 2) double;
-        sigma (2, 2) double = eye(2);
+        sigma (:, 2, 2) double = eye(2);
     end
     arguments (Output)
         f (1, 1) {mustBeA(f, "function_handle")};
     end
-    
+    assert(size(center, 1) == size(sigma, 1));
-    f = @(x,y) sum(cell2mat(arrayfun(@(i) mvnpdf([x(:), y(:)], center(i,:), sigma), 1:size(center,1), "UniformOutput", false)), 2);
+    f = @(x,y) sum(cell2mat(arrayfun(@(i) mvnpdf([x(:), y(:)], center(i,:), squeeze(sigma(i, :, :))), 1:size(center,1), "UniformOutput", false)), 2);
 
 end