diff --git a/@agent/initialize.m b/@agent/initialize.m index 5e8f00c..208014c 100644 --- a/@agent/initialize.m +++ b/@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], tan(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], tand(obj.sensorModel.alphaTilt) * obj.pos(3), obj.pos(3), REGION_TYPE.FOV, sprintf("%s FOV", obj.label)); end \ No newline at end of file diff --git a/@miSim/partition.m b/@miSim/partition.m index 56e2cb4..d9e9115 100644 --- a/@miSim/partition.m +++ b/@miSim/partition.m @@ -20,15 +20,20 @@ function obj = partition(obj) agentInds{end + 1} = zeros(size(agentInds{end})); % index for no assignment agentInds = cat(3, agentInds{:}); - % Get highest performing agent's index - [m,n,~] = size(agentInds); - [jj,kk] = ndgrid(1:m, 1:n); + % Use highest performing agent's index to form partitions + [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) - obj.perf(ii, nowIdx) = sum(agentPerformances(sub2ind(size(agentInds), jj, kk, ii)), 'all'); + idx = obj.partitioning == ii; + agentPerformance = squeeze(agentPerformances(:, :, ii)); + obj.perf(ii, nowIdx) = sum(agentPerformance(idx) .* obj.objective.values(idx)); end % Current total performance diff --git a/@sensingObjective/initialize.m b/@sensingObjective/initialize.m index 20bc42a..8b67a89 100644 --- a/@sensingObjective/initialize.m +++ b/@sensingObjective/initialize.m @@ -28,9 +28,12 @@ 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 == max(obj.values, [], "all"); + idx = obj.values == 1; obj.groundPos = [obj.X(idx), obj.Y(idx)]; assert(domain.distance([obj.groundPos, domain.center(3)]) > protectedRange, "Domain is crowding the sensing objective") diff --git a/resources/project/9I1NhbvmbTiuVshItfSCHZiDxxI/Dqu7zYrAehYF43bzPjgGlZpWKucd.xml b/resources/project/9ucwbDs2BkA9ZqIHQI_XVIGSH5A/isiBrcsGCu0pq9djBUrtow7oYN8d.xml similarity index 100% rename from resources/project/9I1NhbvmbTiuVshItfSCHZiDxxI/Dqu7zYrAehYF43bzPjgGlZpWKucd.xml rename to resources/project/9ucwbDs2BkA9ZqIHQI_XVIGSH5A/isiBrcsGCu0pq9djBUrtow7oYN8d.xml diff --git a/resources/project/9I1NhbvmbTiuVshItfSCHZiDxxI/Dqu7zYrAehYF43bzPjgGlZpWKucp.xml 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similarity index 100% rename from sensingModels/@sigmoidSensor/distanceMembership.m rename to sensorModels/@sigmoidSensor/distanceMembership.m diff --git a/sensingModels/@sigmoidSensor/initialize.m b/sensorModels/@sigmoidSensor/initialize.m similarity index 100% rename from sensingModels/@sigmoidSensor/initialize.m rename to sensorModels/@sigmoidSensor/initialize.m diff --git a/sensingModels/@sigmoidSensor/plotParameters.m b/sensorModels/@sigmoidSensor/plotParameters.m similarity index 95% rename from sensingModels/@sigmoidSensor/plotParameters.m rename to sensorModels/@sigmoidSensor/plotParameters.m index 902caf4..09b622f 100644 --- a/sensingModels/@sigmoidSensor/plotParameters.m +++ b/sensorModels/@sigmoidSensor/plotParameters.m @@ -12,7 +12,7 @@ function f = plotParameters(obj) % Sample membership functions d_x = obj.distanceMembership(d); - t_x = obj.tiltMembership(deg2rad(t)); + t_x = obj.tiltMembership(t); % Plot resultant sigmoid curves f = figure; diff --git a/sensingModels/@sigmoidSensor/sense.m b/sensorModels/@sigmoidSensor/sense.m similarity index 100% rename from sensingModels/@sigmoidSensor/sense.m rename to sensorModels/@sigmoidSensor/sense.m diff --git a/sensingModels/@sigmoidSensor/sensorPerformance.m b/sensorModels/@sigmoidSensor/sensorPerformance.m similarity index 89% rename from sensingModels/@sigmoidSensor/sensorPerformance.m rename to sensorModels/@sigmoidSensor/sensorPerformance.m index 882959c..f4e4fd1 100644 --- a/sensingModels/@sigmoidSensor/sensorPerformance.m +++ b/sensorModels/@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 = atan2(targetPos(:, 3) - agentPos(3), x) - agentTilt; + tiltAngle = (180 - atan2d(x, targetPos(:, 3) - agentPos(3))) - agentTilt; % degrees % Membership functions mu_d = obj.distanceMembership(d); diff --git a/sensingModels/@sigmoidSensor/sigmoidSensor.m b/sensorModels/@sigmoidSensor/sigmoidSensor.m similarity index 95% rename from sensingModels/@sigmoidSensor/sigmoidSensor.m rename to sensorModels/@sigmoidSensor/sigmoidSensor.m index b5bc4a3..fb5e874 100644 --- a/sensingModels/@sigmoidSensor/sigmoidSensor.m +++ b/sensorModels/@sigmoidSensor/sigmoidSensor.m @@ -5,7 +5,7 @@ classdef sigmoidSensor betaDist = NaN; alphaPan = NaN; betaPan = NaN; - alphaTilt = NaN; + alphaTilt = NaN; % degrees betaTilt = NaN; end diff --git a/sensingModels/@sigmoidSensor/tiltMembership.m b/sensorModels/@sigmoidSensor/tiltMembership.m similarity index 89% rename from sensingModels/@sigmoidSensor/tiltMembership.m rename to sensorModels/@sigmoidSensor/tiltMembership.m index 0551aa2..a7a78e4 100644 --- a/sensingModels/@sigmoidSensor/tiltMembership.m +++ b/sensorModels/@sigmoidSensor/tiltMembership.m @@ -1,7 +1,7 @@ function x = tiltMembership(obj, t) arguments (Input) obj (1, 1) {mustBeA(obj, 'sigmoidSensor')}; - t (:, 1) double; + t (:, 1) double; % degrees end arguments (Output) x (:, 1) double; diff --git a/test/test_miSim.m b/test/test_miSim.m index 7424898..813a602 100644 --- a/test/test_miSim.m +++ b/test/test_miSim.m @@ -42,8 +42,8 @@ classdef test_miSim < matlab.unittest.TestCase betaTiltMax = 15; alphaDistMin = 2.5; alphaDistMax = 3; - alphaTiltMin = deg2rad(15); - alphaTiltMax = deg2rad(30); + alphaTiltMin = 15; % degrees + alphaTiltMax = 30; % degrees % Communications comRange = 8; % Maximum range between agents that forms a communications link @@ -349,36 +349,38 @@ 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 + [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)); + 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)); % Initialize agent sensor model sensor = sigmoidSensor; % Homogeneous sensor model parameters - sensor = sensor.initialize(2.5, 3, NaN, NaN, deg2rad(15), 3); + sensor = sensor.initialize(2.75, 9, NaN, NaN, 22.5, 9); 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 + [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)); + 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)); % Optional third agent along the +Y axis geometry3 = rectangularPrism; - 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)); + 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)); tc.agents{3} = agent; - 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)); + 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)); % Initialize the simulation tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter); end - function test_annular_partition(tc) + function test_single_partition(tc) % make basic domain - tc.domain = tc.domain.initialize([zeros(1, 3); 10 * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain"); + l = 10; % domain size + tc.domain = tc.domain.initialize([zeros(1, 3); l * 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); @@ -390,7 +392,9 @@ 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, 0.3641, 13); + % 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); f = sensor.plotParameters(); % Initialize agents @@ -399,6 +403,7 @@ 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 diff --git a/test/test_sigmoidSensor.m b/test/test_sigmoidSensor.m index d5e3862..5d5bde0 100644 --- a/test/test_sigmoidSensor.m +++ b/test/test_sigmoidSensor.m @@ -13,8 +13,8 @@ classdef test_sigmoidSensor < matlab.unittest.TestCase betaTiltMax = 15; alphaDistMin = 2.5; alphaDistMax = 3; - alphaTiltMin = deg2rad(15); - alphaTiltMax = deg2rad(30); + alphaTiltMin = 15; % degrees + alphaTiltMax = 30; % degrees end methods (TestMethodSetup) @@ -31,7 +31,7 @@ classdef test_sigmoidSensor < matlab.unittest.TestCase tc.testClass = sigmoidSensor; alphaDist = 2.5; betaDist = 3; - alphaTilt = deg2rad(15); + alphaTilt = 15; % degrees betaTilt = 3; tc.testClass = tc.testClass.initialize(alphaDist, betaDist, NaN, NaN, alphaTilt, betaTilt);