lots of cleanup and simplification in test case construction

@agent/agent.m

@@ -9,14 +9,9 @@ classdef agent
         % State
         lastPos = NaN(1, 3); % position from previous timestep
         pos = NaN(1, 3); % current position
-        vel = NaN(1, 3); % current velocity
-        pan = NaN; % pan angle
-        tilt = NaN; % tilt angle
 
         % Collision
         collisionGeometry;
-        barrierFunction;
-        dBarrierFunction;
 
         % FOV cone
         fovGeometry;
@@ -39,7 +34,7 @@ classdef agent
     end
 
     methods (Access = public)
-        [obj] = initialize(obj, pos, vel, pan, tilt, collisionGeometry, sensorModel, guidanceModel, comRange, index, label);
+        [obj] = initialize(obj, pos, pan, tilt, collisionGeometry, sensorModel, guidanceModel, comRange, index, label);
         [obj] = run(obj, domain, partitioning, t, index, agents);
         [partitioning] = partition(obj, agents, objective)
         [obj, f] = plot(obj, ind, f);

@agent/initialize.m

@@ -1,10 +1,7 @@
-function obj = initialize(obj, pos, vel, pan, tilt, collisionGeometry, sensorModel, comRange, maxIter, label, plotCommsGeometry)
+function obj = initialize(obj, pos, collisionGeometry, sensorModel, comRange, maxIter, label, plotCommsGeometry)
     arguments (Input)
         obj (1, 1) {mustBeA(obj, 'agent')};
         pos (1, 3) double;
-        vel (1, 3) double;
-        pan (1, 1) double;
-        tilt (1, 1) double;
         collisionGeometry (1, 1) {mustBeGeometry};
         sensorModel (1, 1) {mustBeSensor};
         comRange (1, 1) double;
@@ -17,9 +14,6 @@ function obj = initialize(obj, pos, vel, pan, tilt, collisionGeometry, sensorMod
     end
 
     obj.pos = pos;
-    obj.vel = vel;
-    obj.pan = pan;
-    obj.tilt = tilt;
     obj.collisionGeometry = collisionGeometry;
     obj.sensorModel = sensorModel;
     obj.label = label;

@agent/partition.m

@@ -10,7 +10,7 @@ function [partitioning] = partition(obj, agents, objective)
     
     % Assess sensing performance of each agent at each sample point
     % in the domain
-    agentPerformances = cellfun(@(x) reshape(x.sensorModel.sensorPerformance(x.pos, x.pan, x.tilt, [objective.X(:), objective.Y(:), zeros(size(objective.X(:)))]), size(objective.X)), agents, 'UniformOutput', false);
+    agentPerformances = cellfun(@(x) reshape(x.sensorModel.sensorPerformance(x.pos, [objective.X(:), objective.Y(:), zeros(size(objective.X(:)))]), size(objective.X)), agents, 'UniformOutput', false);
     agentPerformances{end + 1} = objective.sensorPerformanceMinimum * ones(size(agentPerformances{end})); % add additional layer to represent the threshold that has to be cleared for assignment to any partiton
     agentPerformances = cat(3, agentPerformances{:});
 

@agent/run.m

@@ -13,6 +13,10 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents)
 
     % Collect objective function values across partition
     partitionMask = partitioning == index;
+    if ~unique(partitionMask)
+        % This agent has no partition, maintain current state
+        return;
+    end
     objectiveValues = domain.objective.values(partitionMask); % f(omega) on W_n
 
     % Compute sensor performance on partition
@@ -30,7 +34,7 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents)
         % Compute performance values on partition
         if ii < 5
             % Compute sensing performance
-            sensorValues = obj.sensorModel.sensorPerformance(pos, obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
+            sensorValues = obj.sensorModel.sensorPerformance(pos, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
             % Objective performance does not change for 0, +/- X, Y steps.
             % Those values are computed once before the loop and are only
             % recomputed when +/- Z steps are applied
@@ -45,7 +49,7 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents)
             % Recompute partiton-derived performance values for sensing
             maskedX = domain.objective.X(partitionMask);
             maskedY = domain.objective.Y(partitionMask);
-            sensorValues = obj.sensorModel.sensorPerformance(pos, obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
+            sensorValues = obj.sensorModel.sensorPerformance(pos, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
         end
 
         % Rearrange data into image arrays

@agent/updatePlots.m

@@ -5,6 +5,13 @@ function updatePlots(obj)
     arguments (Output)
     end
 
+    % Find change in agent position since last timestep
+    deltaPos = obj.pos - obj.lastPos;
+    if all(isnan(deltaPos)) || all(deltaPos == zeros(1, 3))
+        % Agent did not move, so nothing has to move on the plots
+        return;
+    end
+
     % Scatterplot point positions
     for ii = 1:size(obj.scatterPoints, 1)
         obj.scatterPoints(ii).XData = obj.pos(1);
@@ -12,9 +19,6 @@ function updatePlots(obj)
         obj.scatterPoints(ii).ZData = obj.pos(3);
     end
 
-    % Find change in agent position since last timestep
-    deltaPos = obj.pos - obj.lastPos;
-
     % Collision geometry edges
     for jj = 1:size(obj.collisionGeometry.lines, 2)
         % Update plotting

@miSim/constrainMotion.m

@@ -14,6 +14,11 @@ function [obj] = constrainMotion(obj)
 
     agents = [obj.agents{:}];
     v = reshape(([agents.pos] - [agents.lastPos])./obj.timestep, 3, size(obj.agents, 1))';
+    if all(isnan(v)) || all(v == zeros(1, 3))
+        % Agents are not attempting to move, so there is no motion to be
+        % constrained
+        return;
+    end
 
     % Initialize QP based on number of agents and obstacles
     nAOPairs = size(obj.agents, 1) * size(obj.obstacles, 1); % unique agent/obstacle pairs

@miSim/initialize.m

@@ -1,8 +1,7 @@
-function obj = initialize(obj, domain, objective, agents, minAlt, timestep, maxIter, obstacles, makePlots, makeVideo)
+function obj = initialize(obj, domain, agents, minAlt, timestep, maxIter, obstacles, makePlots, makeVideo)
     arguments (Input)
         obj (1, 1) {mustBeA(obj, 'miSim')};
         domain (1, 1) {mustBeGeometry};
-        objective (1, 1) {mustBeA(objective, 'sensingObjective')};
         agents (:, 1) cell;
         minAlt (1, 1) double = 1;
         timestep (:, 1) double = 0.05;
@@ -44,9 +43,6 @@ function obj = initialize(obj, domain, objective, agents, minAlt, timestep, maxI
         obj.obstacles{end, 1} = obj.obstacles{end, 1}.initialize([obj.domain.minCorner; obj.domain.maxCorner(1:2), obj.minAlt], "OBSTACLE", "Minimum Altitude Domain Constraint");
     end
 
-    % Define objective
-    obj.objective = objective;
-
     % Define agents
     obj.agents = agents;
     obj.constraintAdjacencyMatrix = logical(eye(size(agents, 1)));
@@ -76,7 +72,7 @@ function obj = initialize(obj, domain, objective, agents, minAlt, timestep, maxI
     obj.perf = [zeros(size(obj.agents, 1) + 1, 1), NaN(size(obj.agents, 1) + 1, size(obj.partitioningTimes, 1) - 1)];
 
     % Prepare h function data store
-    obj.h = NaN(size(obj.agents, 1) * (size(obj.agents, 1) - 1) / 2 + size(obj.agents, 1) * size(obj.obstacles, 1) + 6, size(obj.times, 1) - 1);
+    obj.h = NaN(size(obj.agents, 1) * (size(obj.agents, 1) - 1) / 2 + size(obj.agents, 1) * size(obj.obstacles, 1) + 6, size(obj.times, 1));
 
     % Create initial partitioning
     obj.partitioning = obj.agents{1}.partition(obj.agents, obj.domain.objective);

@miSim/miSim.m

@@ -55,7 +55,7 @@ classdef miSim
     end
 
     methods (Access = public)
-        [obj] = initialize(obj, domain, objective, agents, timestep, partitoningFreq, maxIter, obstacles);
+        [obj] = initialize(obj, domain, agents, timestep, partitoningFreq, maxIter, obstacles);
         [obj] = run(obj);
         [obj] = lesserNeighbor(obj);
         [obj] = constrainMotion(obj);
@@ -69,6 +69,7 @@ classdef miSim
         [obj] = plotH(obj);
         [obj] = updatePlots(obj);
         validate(obj);
+        teardown(obj);
     end
     methods (Access = private)
         [v] = setupVideoWriter(obj);

@miSim/teardown.m

@@ -0,0 +1,13 @@
+function teardown(obj)
+    arguments (Input)
+        obj (1, 1) {mustBeA(obj, 'miSim')};
+    end
+    arguments (Output)
+    end
+
+    % Close plots
+    close(obj.hf);
+    close(obj.fPerf);
+    close(obj.f);
+
+end

@sigmoidSensor/initialize.m

@@ -1,10 +1,8 @@
-function obj = initialize(obj, alphaDist, betaDist, alphaPan, betaPan, alphaTilt, betaTilt)
+function obj = initialize(obj, alphaDist, betaDist, alphaTilt, betaTilt)
     arguments (Input)
         obj (1, 1) {mustBeA(obj, 'sigmoidSensor')}
         alphaDist (1, 1) double;
         betaDist (1, 1) double;
-        alphaPan (1, 1) double;
-        betaPan (1, 1) double;
         alphaTilt (1, 1) double;
         betaTilt (1, 1) double;
     end
@@ -14,8 +12,6 @@ function obj = initialize(obj, alphaDist, betaDist, alphaPan, betaPan, alphaTilt
     
     obj.alphaDist = alphaDist;
     obj.betaDist = betaDist;
-    obj.alphaPan = alphaPan;
-    obj.betaPan = betaPan;
     obj.alphaTilt = alphaTilt;
     obj.betaTilt = betaTilt;
 end

@sigmoidSensor/sensorPerformance.m

@@ -1,9 +1,7 @@
-function value = sensorPerformance(obj, agentPos, agentPan, agentTilt, targetPos)
+function value = sensorPerformance(obj, agentPos, targetPos)
     arguments (Input)
         obj (1, 1) {mustBeA(obj, 'sigmoidSensor')};
         agentPos (1, 3) double;
-        agentPan (1, 1) double;
-        agentTilt (1, 1) double;
         targetPos (:, 3) double;
     end
     arguments (Output)
@@ -15,7 +13,7 @@ function value = sensorPerformance(obj, agentPos, agentPan, agentTilt, targetPos
     x = vecnorm(agentPos(1:2) - targetPos(:, 1:2), 2, 2); % distance from sensor nadir to target nadir (i.e. distance ignoring height difference)
 
     % compute tilt angle
-    tiltAngle = (180 - atan2d(x, targetPos(:, 3) - agentPos(3))) - agentTilt; % degrees
+    tiltAngle = (180 - atan2d(x, targetPos(:, 3) - agentPos(3))); % degrees
 
     % Membership functions
     mu_d = obj.distanceMembership(d);

@sigmoidSensor/sigmoidSensor.m

@@ -3,15 +3,12 @@ classdef sigmoidSensor
         % Sensor parameters
         alphaDist = NaN;
         betaDist = NaN;
-        alphaPan = NaN;
-        betaPan = NaN;
         alphaTilt = NaN; % degrees
         betaTilt = NaN;
     end
 
     methods (Access = public)
-        [obj]               = initialize(obj, alphaDist, betaDist, alphaPan, betaPan, alphaTilt, betaTilt);
+        [obj]               = initialize(obj, alphaDist, betaDist, alphaTilt, betaTilt);
-        [values, positions] = sense(obj, agent, sensingObjective, domain, partitioning);
         [value]             = sensorPerformance(obj, agentPos, agentPan, agentTilt, targetPos);
         [f] = plotParameters(obj);
     end

geometries/@rectangularPrism/initialize.m

@@ -48,13 +48,4 @@ function obj = initialize(obj, bounds, tag, label, objectiveFunction, discretiza
     if tag == REGION_TYPE.DOMAIN
         obj.objective = sensingObjective;
     end
-
-    % Initialize CBF
-    % first part evaluates to +/-1 if the point is outside/inside the collision geometry
-    % Second part determines the distance from the point to the boundary of the collision geometry
-    obj.barrierFunction = @(x) (1 - 2 * obj.collisionGeometry.contains(x)) * obj.collisionGeometry.distance(x); % x is 1x3
-    % gradient of barrier function 
-    obj.dBarrierFunction = @(x) obj.collisionGeometry.distanceGradient(x); % x is 1x3
-    % as long as the collisionGeometry object is updated during runtime,
-    % these functions never have to be updated again
 end

geometries/@rectangularPrism/rectangularPrism.m

@@ -20,10 +20,6 @@ classdef rectangularPrism
 
         % Plotting
         lines;
-
-        % collision
-        barrierFunction;
-        dBarrierFunction;
     end
     properties (SetAccess = public, GetAccess = public)
         label = "";

geometries/@spherical/initialize.m

@@ -18,11 +18,6 @@ function obj = initialize(obj, center, radius, tag, label)
     obj.radius = radius;
     obj.diameter = 2 * obj.radius;
 
-    % Initialize CBF
-    obj.barrierFunction = @(x) NaN;
-    % gradient of barrier function 
-    obj.dBarrierFunction = @(x) NaN;
-
     % fake vertices in a cross pattern
     obj.vertices = [obj.center + [obj.radius, 0, 0]; ...
                     obj.center - [obj.radius, 0, 0]; ...

geometries/@spherical/spherical.m

@@ -11,10 +11,6 @@ classdef spherical
 
         % Plotting
         lines;
-
-        % collision
-        barrierFunction;
-        dBarrierFunction;
     end
     properties (SetAccess = public, GetAccess = public)
         % Meta

resources/project/9ucwbDs2BkA9ZqIHQI_XVIGSH5A/r541J80LBY7p-VdF-2cmu2pJyd4p.xml

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

resources/project/9ucwbDs2BkA9ZqIHQI_XVIGSH5A/svhNcplUM7n23Qjwql1uZ5L6c6sd.xml

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

resources/project/E2mMq2X73DyjKhlQAouGqrsyLgg/QQkomTANNGDauE43PAdpZJlxaiQp.xml

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

resources/project/EEtUlUb-dLAdf0KpMVivaUlztwA/ohKqyO-2hbPx9f-0yqpBSUUQdsgd.xml

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

resources/project/EEtUlUb-dLAdf0KpMVivaUlztwA/ohKqyO-2hbPx9f-0yqpBSUUQdsgp.xml

@@ -1,2 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Info location="d143c27d-6824-4569-9093-8150b60976cb" type="Reference"/>

resources/project/SIL3u_W39LwE7HHYsarfFmr9gVQ/c0mYkONNU4mIjcCgfDyvbU49RuMp.xml

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

resources/project/SIL3u_W39LwE7HHYsarfFmr9gVQ/f0RkSaUnOeV8lDfnKFJ8u9hIZUUp.xml

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

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/9ucwbDs2BkA9ZqIHQI_XVIGSH5Ad.xml

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

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/9ucwbDs2BkA9ZqIHQI_XVIGSH5Ap.xml

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

resources/project/r541J80LBY7p-VdF-2cmu2pJyd4/KQTHy3u41hZ5fp_zyPTt34VbI4Yp.xml

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

resources/project/r541J80LBY7p-VdF-2cmu2pJyd4/nQxSe2SDiKQ4IIJQlHCi9SxD7Vsp.xml

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

resources/project/r541J80LBY7p-VdF-2cmu2pJyd4/peMR3cfX5HUpN7z1gUid-46m5V8d.xml

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

resources/project/r541J80LBY7p-VdF-2cmu2pJyd4/peMR3cfX5HUpN7z1gUid-46m5V8p.xml

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

resources/project/svhNcplUM7n23Qjwql1uZ5L6c6s/Jdb23yLfHsZsXlb_4WZeit3L6-Ud.xml

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

resources/project/svhNcplUM7n23Qjwql1uZ5L6c6s/Jdb23yLfHsZsXlb_4WZeit3L6-Up.xml

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

resources/project/svhNcplUM7n23Qjwql1uZ5L6c6s/a6WBlPgSaAg9MeMZ9a3VNFUwwHYp.xml

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

resources/project/svhNcplUM7n23Qjwql1uZ5L6c6s/idDvq0mRu533mATDdzl_FG-b-w4d.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Info>
-    <Category UUID="FileClassCategory">
-        <Label UUID="design"/>
-    </Category>
-</Info>

resources/project/svhNcplUM7n23Qjwql1uZ5L6c6s/jU0rMGjmBFOW1Qf0MUFrG_zqjO0d.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Info>
-    <Category UUID="FileClassCategory">
-        <Label UUID="design"/>
-    </Category>
-</Info>

resources/project/svhNcplUM7n23Qjwql1uZ5L6c6s/lA9hUETTjsTN6BFBFM_Djjvh2CId.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Info>
-    <Category UUID="FileClassCategory">
-        <Label UUID="design"/>
-    </Category>
-</Info>

resources/project/svhNcplUM7n23Qjwql1uZ5L6c6s/lA9hUETTjsTN6BFBFM_Djjvh2CIp.xml

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

sensorModels/@fixedCardinalSensor/fixedCardinalSensor.m

@@ -1,13 +0,0 @@
-classdef fixedCardinalSensor
-    % Senses in the +/-x, +/- y directions at some specified fixed length
-    properties
-        alphaTilt = NaN;
-        r = 0.1; % fixed sensing length
-    end
-
-    methods (Access = public)
-        [obj] = initialize(obj, r);
-        [neighborValues, neighborPos] = sense(obj, agent, sensingObjective, domain, partitioning);
-        [value] = sensorPerformance(obj, agentPos, agentPan, agentTilt, targetPos);
-    end
-end

sensorModels/@fixedCardinalSensor/initialize.m

@@ -1,10 +0,0 @@
-function obj = initialize(obj, r)
-    arguments(Input)
-        obj (1, 1) {mustBeA(obj, 'fixedCardinalSensor')};
-        r (1, 1) double;
-    end
-    arguments(Output)
-        obj (1, 1) {mustBeA(obj, 'fixedCardinalSensor')};
-    end
-    obj.r = r;
-end

sensorModels/@fixedCardinalSensor/sense.m

@@ -1,45 +0,0 @@
-function [neighborValues, neighborPos] = sense(obj, agent, sensingObjective, domain, partitioning)
-    arguments (Input)
-        obj (1, 1) {mustBeA(obj, 'fixedCardinalSensor')};
-        agent (1, 1) {mustBeA(agent, 'agent')};
-        sensingObjective (1, 1) {mustBeA(sensingObjective, 'sensingObjective')};
-        domain (1, 1) {mustBeGeometry};
-        partitioning (:, :) double = NaN;
-    end
-    arguments (Output)
-        neighborValues (4, 1) double;
-        neighborPos (4, 3) double;
-    end
-
-    % Set alphaTilt to produce an FOV cone with radius 'r' on the ground
-    obj.alphaTilt = atan2(obj.r, agent.pos(3));
-
-    % Evaluate objective at position offsets +/-[r, 0, 0] and +/-[0, r, 0]
-    currentPos = agent.pos(1:2);
-    neighborPos = [currentPos(1) + obj.r, currentPos(2); ... % (+x)
-                   currentPos(1), currentPos(2) + obj.r; ... % (+y)
-                   currentPos(1) - obj.r, currentPos(2); ... % (-x)
-                   currentPos(1), currentPos(2) - obj.r; ... % (-y)
-                  ];
-
-    % Check for neighbor positions that fall outside of the domain
-    outOfBounds = false(size(neighborPos, 1), 1);
-    for ii = 1:size(neighborPos, 1)
-        if ~domain.contains([neighborPos(ii, :), 0])
-            outOfBounds(ii) = true;
-        end
-    end
-
-    % Replace out of bounds positions with inoffensive in-bounds positions
-    neighborPos(outOfBounds, 1:3) = repmat(agent.pos, sum(outOfBounds), 1);
-
-    % Sense values at selected positions
-    neighborValues = [sensingObjective.objectiveFunction(neighborPos(1, 1), neighborPos(1, 2)), ... % (+x)
-                      sensingObjective.objectiveFunction(neighborPos(2, 1), neighborPos(2, 2)), ... % (+y)
-                      sensingObjective.objectiveFunction(neighborPos(3, 1), neighborPos(3, 2)), ... % (-x)
-                      sensingObjective.objectiveFunction(neighborPos(4, 1), neighborPos(4, 2)), ... % (-y)
-                     ];
-
-    % Prevent out of bounds locations from ever possibly being selected
-    neighborValues(outOfBounds) = 0;
-end

sensorModels/@fixedCardinalSensor/sensorPerformance.m

@@ -1,14 +0,0 @@
-function value = sensorPerformance(obj, agentPos, agentPan, agentTilt, targetPos)
-    arguments (Input)
-        obj (1, 1) {mustBeA(obj, 'fixedCardinalSensor')};
-        agentPos (1, 3) double;
-        agentPan (1, 1) double;
-        agentTilt (1, 1) double;
-        targetPos (:, 3) double;
-    end
-    arguments (Output)
-        value (:, 1) double;
-    end
-
-    value = 0.5 * ones(size(targetPos, 1), 1);
-end

test/parametricTestSuite.m

@@ -3,7 +3,6 @@ classdef parametricTestSuite < matlab.unittest.TestCase
         % System under test
         testClass = miSim;
         domain = rectangularPrism;
-        objective = sensingObjective;
         obstacles = cell(1, 0);
 
         %% Diagnostic Parameters
@@ -16,33 +15,52 @@ classdef parametricTestSuite < matlab.unittest.TestCase
     end
     properties (TestParameter)
         %% Simulation Parameters
-        maxIter = num2cell([200, 400]); % number of timesteps to run
+        maxIter = num2cell([25]); % number of timesteps to run
 
         % Domain parameters
-        minAlt = num2cell([1, 3]); % minimum allowed agent altitude, make sure test cases don't conflict with this
+        minAlt = num2cell([1]); % minimum allowed agent altitude, make sure test cases don't conflict with this
 
         % Sensing Objective Parameters
-        discretizationStep = num2cell([0.01, 0.05]);
+        discretizationStep = num2cell([0.01]);
 
         % Agent Parameters
-        collisionRange = num2cell([0.1, 0.5]);
+        collisionRadius = num2cell([0.1]);
 
         % Sensor Model Parameters
-        betaDist = num2cell(3:6:15);
-        betaTilt = num2cell(3:6:15);
         alphaDist = num2cell([2.5, 5]);
-        alphaTilt = num2cell([15, 30]); % (degrees)
+        betaDist = num2cell([3, 15]);
+        alphaTilt = num2cell([15, 30]); % (degrees)methods
+        betaTilt = num2cell([3, 15]);
 
         % Communications Parameters
-        comRange = num2cell(1:2:5);
+        comRange = num2cell([3]);
     end
 
     methods (Test, ParameterCombination = "exhaustive")
-        % Test methods
+        % Test cases
+        function single_agent_gradient_ascent(tc, maxIter, minAlt, discretizationStep, collisionRadius, alphaDist, betaDist, alphaTilt, betaTilt, comRange)
+            % Set up square domain
+            l = 10;
+            tc.domain = tc.domain.initialize([zeros(1, 3); l * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
+            tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([.75 * l, 0.75 * l]), tc.domain, discretizationStep, tc.protectedRange);
             
-        function single_agent_gradient_ascent(tc, maxIter, minAlt, discretizationStep, collisionRange, alphaDist, alphaTilt, betaDist, betaTilt, comRange)
+            % Set up agent
-            1;
+            sensorModel = sigmoidSensor;
-        end
+            sensorModel = sensorModel.initialize(alphaDist, betaDist, alphaTilt, betaTilt);
-    end
+            agentPos = [l/4, l/4, 3*l/4];
+            collisionGeometry = spherical;
+            collisionGeometry = collisionGeometry.initialize(agentPos, collisionRadius, REGION_TYPE.COLLISION, "Agent 1 Collision Region");
+            agents = {agent};
+            agents{1} = agents{1}.initialize(agentPos, collisionGeometry, sensorModel, comRange, maxIter, "Agent 1", tc.plotCommsGeometry);
 
+            % Set up simulation
+            tc.testClass = tc.testClass.initialize(tc.domain, agents, minAlt, tc.timestep, maxIter, tc.obstacles, tc.makePlots, tc.makeVideo);
+
+            % Run
+            tc.testClass = tc.testClass.run();
+
+            % Cleanup
+            tc.testClass.teardown();
+        end
+    end
 end

test/test_miSim.m

@@ -157,10 +157,10 @@ classdef test_miSim < matlab.unittest.TestCase
 
                     % Initialize candidate agent sensor model
                     sensor = sigmoidSensor;
-                    sensor = sensor.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), NaN, NaN, tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
+                    sensor = sensor.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
 
                     % Initialize candidate agent
-                    newAgent = tc.agents{ii}.initialize(candidatePos, zeros(1,3), 0, 0, candidateGeometry, sensor, tc.comRange, tc.maxIter); 
+                    newAgent = tc.agents{ii}.initialize(candidatePos, candidateGeometry, sensor, tc.comRange, tc.maxIter); 
 
                     % Make sure candidate agent doesn't collide with
                     % domain
@@ -208,7 +208,7 @@ classdef test_miSim < matlab.unittest.TestCase
             end
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, tc.obstacles, tc.makeVideo);
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, tc.obstacles, tc.makeVideo);
         end
         function misim_run(tc)
             % randomly create obstacles
@@ -291,10 +291,10 @@ classdef test_miSim < matlab.unittest.TestCase
 
                     % Initialize candidate agent sensor model
                     sensor = sigmoidSensor;
-                    sensor = sensor.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), NaN, NaN, tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
+                    sensor = sensor.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
 
                     % Initialize candidate agent
-                    newAgent = tc.agents{ii}.initialize(candidatePos, zeros(1,3), 0, 0, candidateGeometry, sensor, tc.comRange, tc.maxIter);
+                    newAgent = tc.agents{ii}.initialize(candidatePos, candidateGeometry, sensor, tc.comRange, tc.maxIter);
                     
                     % Make sure candidate agent doesn't collide with
                     % domain
@@ -342,7 +342,7 @@ classdef test_miSim < matlab.unittest.TestCase
             end
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, tc.obstacles, tc.makeVideo);
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, tc.obstacles, tc.makeVideo);
 
             % Run simulation loop
             tc.testClass = tc.testClass.run();
@@ -367,26 +367,26 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             % Homogeneous sensor model parameters
-            sensor = sensor.initialize(2.75, 9, NaN, NaN, 22.5, 9);
+            sensor = sensor.initialize(2.75, 9, 22.5, 9);
             % Heterogeneous sensor model parameters
-            % sensor = sensor.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), NaN, NaN, tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
+            % sensor = sensor.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin),  tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
 
             % Plot sensor parameters (optional)
             % f = sensor.plotParameters();
 
             % Initialize agents
             tc.agents = {agent; agent};
-            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + dh + [d, 0, 0], zeros(1,3), 0, 0, geometry1, sensor, 3*d, tc.maxIter);
+            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + dh + [d, 0, 0], geometry1, sensor, 3*d, tc.maxIter);
-            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center + dh - [d, 0, 0], zeros(1,3), 0, 0, geometry2, sensor, 3*d, tc.maxIter);
+            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center + dh - [d, 0, 0], geometry2, sensor, 3*d, tc.maxIter);
 
             % Optional third agent along the +Y axis
             geometry3 = rectangularPrism;
             geometry3 = geometry3.initialize([tc.domain.center + dh - [0, d, 0] - tc.collisionRanges(1) * ones(1, 3); tc.domain.center + dh - [0, d, 0] + tc.collisionRanges(1) * ones(1, 3)], REGION_TYPE.COLLISION);
             tc.agents{3} = agent;
-            tc.agents{3} = tc.agents{3}.initialize(tc.domain.center + dh - [0, d, 0], zeros(1, 3), 0, 0, geometry3, sensor, 3*d, tc.maxIter);
+            tc.agents{3} = tc.agents{3}.initialize(tc.domain.center + dh - [0, d, 0], geometry3, sensor, 3*d, tc.maxIter);
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, cell(0, 1), false, false);
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, cell(0, 1), false, false);
         
             tc.verifyEqual(tc.testClass.partitioning(500, 500:502), [2, 3, 1]); % all three near center
             tc.verifyLessThan(sum(tc.testClass.partitioning == 1, 'all'), sum(tc.testClass.partitioning == 0, 'all')); % more non-assignments than partition 1 assignments
@@ -409,19 +409,19 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             % Homogeneous sensor model parameters
-            % sensor = sensor.initialize(2.5666, 5.0807, NaN, NaN, 20.8614, 13); % 13
+            % sensor = sensor.initialize(2.5666, 5.0807, 20.8614, 13); % 13
             alphaDist = l/2; % half of domain length/width
-            sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 20, 3);
+            sensor = sensor.initialize(alphaDist, 3, 20, 3);
 
             % Plot sensor parameters (optional)
             % f = sensor.plotParameters();
 
             % Initialize agents
             tc.agents = {agent};
-            tc.agents{1} = tc.agents{1}.initialize([tc.domain.center(1:2), 3], zeros(1,3), 0, 0, geometry1, sensor, 3, tc.maxIter);
+            tc.agents{1} = tc.agents{1}.initialize([tc.domain.center(1:2), 3], geometry1, sensor, 3, tc.maxIter);
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, cell(0, 1), false, false);
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, cell(0, 1), false, false);
             close(tc.testClass.fPerf);
 
             tc.verifyEqual(unique(tc.testClass.partitioning), [0; 1]);
@@ -442,9 +442,9 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             % Homogeneous sensor model parameters
-            % sensor = sensor.initialize(2.5666, 5.0807, NaN, NaN, 20.8614, 13); % 13
+            % sensor = sensor.initialize(2.5666, 5.0807, 20.8614, 13); % 13
             alphaDist = l/2; % half of domain length/width
-            sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 20, 3);
+            sensor = sensor.initialize(alphaDist, 3,  20, 3);
 
             % Plot sensor parameters (optional)
             % f = sensor.plotParameters();
@@ -452,10 +452,10 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agents
             nIter = 100;
             tc.agents = {agent};
-            tc.agents{1} = tc.agents{1}.initialize([tc.domain.center(1:2)-tc.domain.dimensions(1)/4, 3], zeros(1,3), 0, 0, geometry1, sensor, 3, nIter);
+            tc.agents{1} = tc.agents{1}.initialize([tc.domain.center(1:2)-tc.domain.dimensions(1)/4, 3], geometry1, sensor, 3, nIter);
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, nIter, cell(0, 1));
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, tc.minAlt, tc.timestep, nIter, cell(0, 1));
             
             % Run the simulation
             tc.testClass = tc.testClass.run();
@@ -484,18 +484,18 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             % Homogeneous sensor model parameters
-            % sensor = sensor.initialize(2.5666, 5.0807, NaN, NaN, 20.8614, 13); % 13
+            % sensor = sensor.initialize(2.5666, 5.0807, 20.8614, 13); % 13
             alphaDist = l/2; % half of domain length/width
-            sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 15, 3);
+            sensor = sensor.initialize(alphaDist, 3, 15, 3);
 
             % Initialize agents
             nIter = 50;
             tc.agents = {agent; agent};
-            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + d, zeros(1,3), 0, 0, geometry1, sensor, 5, nIter);
+            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + d, geometry1, sensor, 5, nIter);
-            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center - d, zeros(1,3), 0, 0, geometry2, sensor, 5, nIter);
+            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center - d, geometry2, sensor, 5, nIter);
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, nIter, cell(0, 1), tc.makeVideo, tc.makePlots);
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, tc.minAlt, tc.timestep, nIter, cell(0, 1), tc.makeVideo, tc.makePlots);
             
             % Run the simulation
             tc.testClass.run();
@@ -529,7 +529,7 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             alphaDist = l/2; % half of domain length/width
-            sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 15, 3);
+            sensor = sensor.initialize(alphaDist, 3, 15, 3);
             
             % Initialize obstacles
             obstacleLength = 1;
@@ -539,11 +539,10 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agents
             commsRadius = (2*radius + obstacleLength) * 0.9; % defined such that they cannot go around the obstacle on both sides
             tc.agents = {agent; agent;};
-            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center - d + [0, radius * 1.1 - yOffset, 0], zeros(1,3), 0, 0, geometry1, sensor, commsRadius, tc.maxIter);
+            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center - d + [0, radius * 1.1 - yOffset, 0], geometry1, sensor, commsRadius, tc.maxIter);
-            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center - d - [0, radius  *1.1 + yOffset, 0], zeros(1,3), 0, 0, geometry2, sensor, commsRadius, tc.maxIter);
+            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center - d - [0, radius  *1.1 + yOffset, 0], geometry2, sensor, commsRadius, tc.maxIter);
-
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, tc.obstacles, tc.makeVideo);
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, tc.minAlt, tc.timestep, tc.maxIter, tc.obstacles, tc.makeVideo);
             
             % Run the simulation
             tc.testClass.run();
@@ -571,7 +570,7 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             alphaDist = l/2; % half of domain length/width
-            sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 15, 3);
+            sensor = sensor.initialize(alphaDist, 3, 15, 3);
             
             % Initialize obstacles
             tc.obstacles = {};
@@ -580,11 +579,11 @@ classdef test_miSim < matlab.unittest.TestCase
             nIter = 75;
             commsRadius = 4; % defined such that they cannot reach their objective without breaking connectivity
             tc.agents = {agent; agent;};
-            tc.agents{1} = tc.agents{1}.initialize(dom.center + d, zeros(1,3), 0, 0, geometry1, sensor, commsRadius, nIter);
+            tc.agents{1} = tc.agents{1}.initialize(dom.center + d, geometry1, sensor, commsRadius, nIter);
-            tc.agents{2} = tc.agents{2}.initialize(dom.center - d, zeros(1,3), 0, 0, geometry2, sensor, commsRadius, nIter);
+            tc.agents{2} = tc.agents{2}.initialize(dom.center - d, geometry2, sensor, commsRadius, nIter);
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(dom, dom.objective, tc.agents, tc.minAlt, tc.timestep, nIter, tc.obstacles, true, false);
+            tc.testClass = tc.testClass.initialize(dom, tc.agents, tc.minAlt, tc.timestep, nIter, tc.obstacles, true, false);
             
             % Run the simulation
             tc.testClass = tc.testClass.run();
@@ -611,14 +610,14 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             alphaDist = l/2; % half of domain length/width
-            sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 15, 3);
+            sensor = sensor.initialize(alphaDist, 3, 15, 3);
 
             % Initialize agents
             nIter = 125;
             commsRadius = 5;
             tc.agents = {agent; agent;};
-            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center - [d, 0, 0], zeros(1,3), 0, 0, geometry1, sensor, commsRadius, nIter);
+            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center - [d, 0, 0], geometry1, sensor, commsRadius, nIter);
-            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center - [0, d, 0], zeros(1,3), 0, 0, geometry2, sensor, commsRadius, nIter);
+            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center - [0, d, 0], geometry2, sensor, commsRadius, nIter);
             
             % Initialize obstacles
             obstacleLength = 1.5;
@@ -626,7 +625,7 @@ classdef test_miSim < matlab.unittest.TestCase
             tc.obstacles{1} = tc.obstacles{1}.initialize([tc.domain.center(1:2) - obstacleLength, 0; tc.domain.center(1:2) + obstacleLength, tc.domain.maxCorner(3)], REGION_TYPE.OBSTACLE, "Obstacle 1");
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, 0, tc.timestep, nIter, tc.obstacles, false, false);
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, 0, tc.timestep, nIter, tc.obstacles, false, false);
 
             % No communications link should be established
             tc.assertEqual(tc.testClass.adjacency, logical(true(2)));
@@ -657,20 +656,20 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             alphaDist = l/2; % half of domain length/width
-            sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 15, 3);
+            sensor = sensor.initialize(alphaDist, 3, 15, 3);
 
             % Initialize agents
             nIter = 125;
             commsRadius = d;
             tc.agents = {agent; agent; agent; agent; agent;};
-            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + [d, 0, 0], zeros(1,3), 0, 0, geometry1, sensor, commsRadius, nIter);
+            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + [d, 0, 0], geometry1, sensor, commsRadius, nIter);
-            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center, zeros(1,3), 0, 0, geometry2, sensor, commsRadius, nIter);
+            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center, 0, 0, geometry2, sensor, commsRadius, nIter);
-            tc.agents{3} = tc.agents{3}.initialize(tc.domain.center + [-d, d, 0], zeros(1,3), 0, 0, geometry3, sensor, commsRadius, nIter);
+            tc.agents{3} = tc.agents{3}.initialize(tc.domain.center + [-d, d, 0], geometry3, sensor, commsRadius, nIter);
-            tc.agents{4} = tc.agents{4}.initialize(tc.domain.center + [-2*d, d, 0], zeros(1,3), 0, 0, geometry4, sensor, commsRadius, nIter);
+            tc.agents{4} = tc.agents{4}.initialize(tc.domain.center + [-2*d, d, 0], geometry4, sensor, commsRadius, nIter);
-            tc.agents{5} = tc.agents{5}.initialize(tc.domain.center + [0, d, 0], zeros(1,3), 0, 0, geometry5, sensor, commsRadius, nIter);
+            tc.agents{5} = tc.agents{5}.initialize(tc.domain.center + [0, d, 0], geometry5, sensor, commsRadius, nIter);
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, 0, tc.timestep, nIter, tc.obstacles, false, false);
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, 0, tc.timestep, nIter, tc.obstacles, false, false);
 
             % Constraint adjacency matrix defined by LNA should be as follows
             tc.assertEqual(tc.testClass.constraintAdjacencyMatrix, logical( ...
@@ -708,22 +707,22 @@ classdef test_miSim < matlab.unittest.TestCase
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             alphaDist = l/2; % half of domain length/width
-            sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 15, 3);
+            sensor = sensor.initialize(alphaDist, 3, 15, 3);
 
             % Initialize agents
             nIter = 125;
             commsRadius = d;
             tc.agents = {agent; agent; agent; agent; agent; agent; agent;};
-            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + [-0.9 * d/sqrt(2), 0.9 * d/sqrt(2), 0], zeros(1,3), 0, 0, geometry1, sensor, commsRadius, nIter);
+            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + [-0.9 * d/sqrt(2), 0.9 * d/sqrt(2), 0], geometry1, sensor, commsRadius, nIter);
-            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center + [-0.5 * d, 0.25 * d, 0], zeros(1,3), 0, 0, geometry2, sensor, commsRadius, nIter);
+            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center + [-0.5 * d, 0.25 * d, 0], geometry2, sensor, commsRadius, nIter);
-            tc.agents{3} = tc.agents{3}.initialize(tc.domain.center + [0.9 * d, 0, 0], zeros(1,3), 0, 0, geometry3, sensor, commsRadius, nIter);
+            tc.agents{3} = tc.agents{3}.initialize(tc.domain.center + [0.9 * d, 0, 0], geometry3, sensor, commsRadius, nIter);
-            tc.agents{4} = tc.agents{4}.initialize(tc.domain.center + [0.9 * d/sqrt(2), -0.9 * d/sqrt(2), 0], zeros(1,3), 0, 0, geometry4, sensor, commsRadius, nIter);
+            tc.agents{4} = tc.agents{4}.initialize(tc.domain.center + [0.9 * d/sqrt(2), -0.9 * d/sqrt(2), 0], geometry4, sensor, commsRadius, nIter);
-            tc.agents{5} = tc.agents{5}.initialize(tc.domain.center + [0, 0.9 * d, 0], zeros(1,3), 0, 0, geometry5, sensor, commsRadius, nIter);
+            tc.agents{5} = tc.agents{5}.initialize(tc.domain.center + [0, 0.9 * d, 0], geometry5, sensor, commsRadius, nIter);
-            tc.agents{6} = tc.agents{6}.initialize(tc.domain.center, zeros(1,3), 0, 0, geometry6, sensor, commsRadius, nIter);
+            tc.agents{6} = tc.agents{6}.initialize(tc.domain.center, geometry6, sensor, commsRadius, nIter);
-            tc.agents{7} = tc.agents{7}.initialize(tc.domain.center + [d/2, d/2, 0], zeros(1,3), 0, 0, geometry7, sensor, commsRadius, nIter);
+            tc.agents{7} = tc.agents{7}.initialize(tc.domain.center + [d/2, d/2, 0], geometry7, sensor, commsRadius, nIter);
 
             % Initialize the simulation
-            tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, 0, tc.timestep, nIter, tc.obstacles, false, false);
+            tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, 0, tc.timestep, nIter, tc.obstacles, false, false);
 
             % Constraint adjacency matrix defined by LNA should be as follows
             tc.assertEqual(tc.testClass.constraintAdjacencyMatrix, logical( ...

test/test_sigmoidSensor.m

@@ -21,7 +21,7 @@ classdef test_sigmoidSensor < matlab.unittest.TestCase
         function tc = setup(tc)
             % Reinitialize sensor with random parameters
             tc.testClass = sigmoidSensor;
-            tc.testClass = tc.testClass.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), NaN, NaN, tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
+            tc.testClass = tc.testClass.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
         end
     end
 
@@ -34,28 +34,28 @@ classdef test_sigmoidSensor < matlab.unittest.TestCase
             alphaTilt = 15; % degrees
             betaTilt = 3;
             h = 1e-6;
-            tc.testClass = tc.testClass.initialize(alphaDist, betaDist, NaN, NaN, alphaTilt, betaTilt);
+            tc.testClass = tc.testClass.initialize(alphaDist, betaDist, alphaTilt, betaTilt);
             
             % Plot (optional)
             % tc.testClass.plotParameters();
 
             % Anticipate perfect performance for a point directly below and
             % extremely close
-            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], NaN, 0, [0, 0, 0]), 1, 'RelTol', 1e-3); 
+            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], 0, [0, 0, 0]), 1, 'RelTol', 1e-3); 
             % It looks like mu_t can max out at really low values like 0.37
             % when alphaTilt and betaTilt are small, which seems wrong
 
             % Performance at nadir point, distance alphaDist should be 1/2 exactly
-            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, alphaDist], NaN, 0, [0, 0, 0]), 1/2);
+            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, alphaDist], 0, [0, 0, 0]), 1/2);
 
             % Performance at (almost) 0 distance, alphaTilt should be 1/2
-            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], NaN, 0, [tand(alphaTilt)*h, 0, 0]), 1/2, 'RelTol', 1e-3);
+            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], 0, [tand(alphaTilt)*h, 0, 0]), 1/2, 'RelTol', 1e-3);
 
             % Performance at great distance should be 0
-            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, 10], NaN, 0, [0, 0, 0]), 0, 'AbsTol', 1e-9);
+            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, 10], 0, [0, 0, 0]), 0, 'AbsTol', 1e-9);
 
             % Performance at great tilt should be 0
-            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], NaN, 0, [5, 5, 0]), 0, 'AbsTol', 1e-9);
+            tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], 0, [5, 5, 0]), 0, 'AbsTol', 1e-9);
         end
     end
 

util/objectiveFunctionWrapper.m

@@ -0,0 +1,13 @@
+function f = objectiveFunctionWrapper(center)
+    % Convenience function to generate MVNPDFs at a point
+    % Makes it look a lot neater to instantiate and sum these to make
+    % composite objectives in particular
+    arguments (Input)
+        center (1, 2) double;
+    end
+    arguments (Output)
+        f (1, 1) {mustBeA(f, 'function_handle')};
+    end
+
+    f = @(x, y) mvnpdf([x(:), y(:)], center);
+end