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