better random agent placement with respect to sensor capabilities

.gitignore

@@ -45,6 +45,3 @@ sandbox/*
 # Videos
 *.mp4
 *.avi
-
-# Figures
-*.fig

@agent/initialize.m

@@ -29,5 +29,5 @@ function obj = initialize(obj, pos, vel, pan, tilt, collisionGeometry, sensorMod
 
     % Initialize FOV cone
     obj.fovGeometry = cone;
-    obj.fovGeometry = obj.fovGeometry.initialize([obj.pos(1:2), 0], tand(obj.sensorModel.alphaTilt) * obj.pos(3), obj.pos(3), REGION_TYPE.FOV, sprintf("%s FOV", obj.label));
+    obj.fovGeometry = obj.fovGeometry.initialize([obj.pos(1:2), 0], tan(obj.sensorModel.alphaTilt) * obj.pos(3), obj.pos(3), REGION_TYPE.FOV, sprintf("%s FOV", obj.label));
 end

@miSim/partition.m

@@ -20,20 +20,15 @@ function obj = partition(obj)
     agentInds{end + 1} = zeros(size(agentInds{end})); % index for no assignment
     agentInds = cat(3, agentInds{:});
 
-    % Use highest performing agent's index to form partitions
-    [m, n, ~] = size(agentInds);
-    [jj, kk] = ndgrid(1:m, 1:n);
+    % Get highest performing agent's index
+    [m,n,~] = size(agentInds);
+    [jj,kk] = ndgrid(1:m, 1:n);
     obj.partitioning = agentInds(sub2ind(size(agentInds), jj, kk, idx));
 
     % Get individual agent sensor performance
     nowIdx = [0; obj.partitioningTimes] == obj.t;
-    if isnan(obj.t)
-        nowIdx = 1;
-    end
     for ii = 1:size(obj.agents, 1)
-        idx = obj.partitioning == ii;
-        agentPerformance = squeeze(agentPerformances(:, :, ii));
-        obj.perf(ii, nowIdx) = sum(agentPerformance(idx) .* obj.objective.values(idx));
+        obj.perf(ii, nowIdx) = sum(agentPerformances(sub2ind(size(agentInds), jj, kk, ii)), 'all');
     end
 
     % Current total performance

@sensingObjective/initialize.m

@@ -28,12 +28,9 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
     % Evaluate function over grid points
     obj.objectiveFunction = objectiveFunction;
     obj.values = reshape(obj.objectiveFunction(obj.X, obj.Y), size(obj.X));
-    
-    % Normalize
-    obj.values = obj.values ./ max(obj.values, [], "all");
 
     % store ground position
-    idx = obj.values == 1;
+    idx = obj.values == max(obj.values, [], "all");
     obj.groundPos = [obj.X(idx), obj.Y(idx)];
 
     assert(domain.distance([obj.groundPos, domain.center(3)]) > protectedRange, "Domain is crowding the sensing objective")

resources/project/EEtUlUb-dLAdf0KpMVivaUlztwA/4-Vh5fQn4oEziz1w72O3jpEw0yMd.xml

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

resources/project/EEtUlUb-dLAdf0KpMVivaUlztwA/4-Vh5fQn4oEziz1w72O3jpEw0yMp.xml

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Info location="420d04e4-3880-4a45-8609-11cb30d87302" type="Reference"/>

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/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/9ucwbDs2BkA9ZqIHQI_XVIGSH5Ap.xml

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

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/uhUDZwLuHiPGihzjzILZj0glbh8p.xml

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

sensingModels/@sigmoidSensor/plotParameters.m

@@ -12,7 +12,7 @@ function f = plotParameters(obj)
 
     % Sample membership functions
     d_x = obj.distanceMembership(d);
-    t_x = obj.tiltMembership(t);
+    t_x = obj.tiltMembership(deg2rad(t));
 
     % Plot resultant sigmoid curves
     f = figure;

sensingModels/@sigmoidSensor/sensorPerformance.m

@@ -12,7 +12,7 @@ function value = sensorPerformance(obj, agentPos, agentPan, agentTilt, targetPos
 
     d = vecnorm(agentPos - targetPos, 2, 2); % distance from sensor to target
     x = vecnorm(agentPos(1:2) - targetPos(:, 1:2), 2, 2); % distance from sensor nadir to target nadir (i.e. distance ignoring height difference)
-    tiltAngle = (180 - atan2d(x, targetPos(:, 3) - agentPos(3))) - agentTilt; % degrees
+    tiltAngle = atan2(targetPos(:, 3) - agentPos(3), x) - agentTilt;
 
     % Membership functions
     mu_d = obj.distanceMembership(d);

sensingModels/@sigmoidSensor/sigmoidSensor.m

@@ -5,7 +5,7 @@ classdef sigmoidSensor
         betaDist = NaN;
         alphaPan = NaN;
         betaPan = NaN;
-        alphaTilt = NaN; % degrees
+        alphaTilt = NaN;
         betaTilt = NaN;
     end
 

sensingModels/@sigmoidSensor/tiltMembership.m

@@ -1,7 +1,7 @@
 function x = tiltMembership(obj, t)
     arguments (Input)
         obj (1, 1) {mustBeA(obj, 'sigmoidSensor')};
-        t (:, 1) double; % degrees
+        t (:, 1) double;
     end
     arguments (Output)
         x (:, 1) double;

test/test_miSim.m

@@ -25,8 +25,8 @@ classdef test_miSim < matlab.unittest.TestCase
         objective = sensingObjective;
 
         % Agents
-        minAgents = 2; % Minimum number of agents to be randomly generated
-        maxAgents = 4; % Maximum number of agents to be randomly generated
+        minAgents = 3; % Minimum number of agents to be randomly generated
+        maxAgents = 6; % Maximum number of agents to be randomly generated
         sensingLength = 0.05; % length parameter used by sensing function
         agents = cell(0, 1);
         
@@ -42,11 +42,11 @@ classdef test_miSim < matlab.unittest.TestCase
         betaTiltMax = 15;
         alphaDistMin = 2.5;
         alphaDistMax = 3;
-        alphaTiltMin = 15; % degrees
-        alphaTiltMax = 30; % degrees
+        alphaTiltMin = deg2rad(15);
+        alphaTiltMax = deg2rad(30);
 
         % Communications
-        comRange = 8; % Maximum range between agents that forms a communications link
+        comRange = 5; % Maximum range between agents that forms a communications link
     end
 
     % Setup for each test
@@ -104,7 +104,7 @@ classdef test_miSim < matlab.unittest.TestCase
                     if ii == 1
                         while agentsCrowdObjective(tc.domain.objective, candidatePos, mean(tc.domain.dimensions) / 2)
                             candidatePos = tc.domain.random();
-                            candidatePos(3) = 2  + rand * 2; % place agents at decent altitudes for sensing
+                            candidatePos(3) = min([tc.domain.maxCorner(3) * 0.95, 0.5 + rand * (tc.alphaDistMax * (1.1)  - 0.5)]); % place agents at decent altitudes for sensing
                         end
                     else
                         candidatePos = tc.agents{randi(ii - 1)}.pos + sign(randn([1, 3])) .* (rand(1, 3) .* tc.comRange/sqrt(2));
@@ -349,38 +349,36 @@ classdef test_miSim < matlab.unittest.TestCase
             tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2)), tc.domain, tc.discretizationStep, tc.protectedRange);
         
             % Initialize agent collision geometry
-            dh = [0,0,-1]; % bias agent altitude from domain center
             geometry1 = rectangularPrism;
             geometry2 = geometry1;
-            geometry1 = geometry1.initialize([tc.domain.center + dh + [d, 0, 0] - tc.collisionRanges(1) * ones(1, 3); tc.domain.center + dh + [d, 0, 0] + tc.collisionRanges(1) * ones(1, 3)], REGION_TYPE.COLLISION, sprintf("Agent %d collision volume", 1));
-            geometry2 = geometry2.initialize([tc.domain.center + dh - [d, 0, 0] - tc.collisionRanges(1) * ones(1, 3); tc.domain.center + dh - [d, 0, 0] + tc.collisionRanges(1) * ones(1, 3)], REGION_TYPE.COLLISION, sprintf("Agent %d collision volume", 2));
+            geometry1 = geometry1.initialize([tc.domain.center + [d, 0, 0] - tc.collisionRanges(1) * ones(1, 3); tc.domain.center + [d, 0, 0] + tc.collisionRanges(1) * ones(1, 3)], REGION_TYPE.COLLISION, sprintf("Agent %d collision volume", 1));
+            geometry2 = geometry2.initialize([tc.domain.center - [d, 0, 0] - tc.collisionRanges(1) * ones(1, 3); tc.domain.center - [d, 0, 0] + tc.collisionRanges(1) * ones(1, 3)], REGION_TYPE.COLLISION, sprintf("Agent %d collision volume", 2));
             
             % Initialize agent sensor model
             sensor = sigmoidSensor;
             % Homogeneous sensor model parameters
-            sensor = sensor.initialize(2.75, 9, NaN, NaN, 22.5, 9);
+            sensor = sensor.initialize(2.5, 3, NaN, NaN, deg2rad(15), 3);
             f = sensor.plotParameters();
             % 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));
 
             % 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, @gradientAscent, 3*d, 1, sprintf("Agent %d", 1));
-            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center + dh - [d, 0, 0], zeros(1,3), 0, 0, geometry2, sensor, @gradientAscent, 3*d, 2, sprintf("Agent %d", 2));
+            tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + [d, 0, 0], zeros(1,3), 0, 0, geometry1, sensor, @gradientAscent, 3*d, 1, sprintf("Agent %d", 1));
+            tc.agents{2} = tc.agents{2}.initialize(tc.domain.center - [d, 0, 0], zeros(1,3), 0, 0, geometry2, sensor, @gradientAscent, 3*d, 2, sprintf("Agent %d", 2));
 
             % 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, sprintf("Agent %d collision volume", 3));
+            geometry3 = geometry3.initialize([tc.domain.center - [0, d, 0] - tc.collisionRanges(1) * ones(1, 3); tc.domain.center - [0, d, 0] + tc.collisionRanges(1) * ones(1, 3)], REGION_TYPE.COLLISION, sprintf("Agent %d collision volume", 3));
             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, @gradientAscent, 3*d, 3, sprintf("Agent %d", 3));
+            tc.agents{3} = tc.agents{3}.initialize(tc.domain.center - [0, d, 0], zeros(1, 3), 0, 0, geometry3, sensor, @gradientAscent, 3*d, 3, sprintf("Agent %d", 3));
 
             % Initialize the simulation
             tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter);
         end
-        function test_single_partition(tc)
+        function test_annular_partition(tc)
             % make basic domain
-            l = 10; % domain size
-            tc.domain = tc.domain.initialize([zeros(1, 3); l * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
+            tc.domain = tc.domain.initialize([zeros(1, 3); 10 * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
 
             % make basic sensing objective
             tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2)), tc.domain, tc.discretizationStep, tc.protectedRange);
@@ -392,9 +390,7 @@ 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
-            alphaDist = l/2; % half of domain length/width
-            sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 20, 3);
+            sensor = sensor.initialize(2.5666, 5.0807, NaN, NaN, 0.3641, 13);
             f = sensor.plotParameters();
 
             % Initialize agents
@@ -403,7 +399,6 @@ classdef test_miSim < matlab.unittest.TestCase
 
             % Initialize the simulation
             tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter);
-
         end
     end
 

test/test_sigmoidSensor.m

@@ -13,8 +13,8 @@ classdef test_sigmoidSensor < matlab.unittest.TestCase
         betaTiltMax = 15;
         alphaDistMin = 2.5;
         alphaDistMax = 3;
-        alphaTiltMin = 15; % degrees
-        alphaTiltMax = 30; % degrees
+        alphaTiltMin = deg2rad(15);
+        alphaTiltMax = deg2rad(30);
     end
 
     methods (TestMethodSetup)
@@ -31,7 +31,7 @@ classdef test_sigmoidSensor < matlab.unittest.TestCase
             tc.testClass = sigmoidSensor;
             alphaDist = 2.5;
             betaDist = 3;
-            alphaTilt = 15; % degrees
+            alphaTilt = deg2rad(15);
             betaTilt = 3;
             tc.testClass = tc.testClass.initialize(alphaDist, betaDist, NaN, NaN, alphaTilt, betaTilt);