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@@ -1,6 +1,7 @@
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classdef results < matlab.unittest.TestCase
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properties (Constant, Access = private)
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seed = 1;
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domainSize = [150, 150, 100]; % fixed domain size [X, Y, Z]
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end
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properties (Access = private)
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@@ -25,16 +26,16 @@ classdef results < matlab.unittest.TestCase
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%% Fixed Test Parameters
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useFixedTopology = true; % No lesser neighbor, fixed network instead
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minDimension = 50; % minimum domain size
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maxDimension = 100; % maximum domain size
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discretizationStep = 0.1;
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discretizationStep = 0.5;
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protectedRange = 5;
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collisionRadius = 5;
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sensorPerformanceMinimum = 0.005;
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comRange = 20;
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maxIter = 250;
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maxIter = 400;
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initialStepSize = 1;
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numObstacles = 3;
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% Each row: [minX minY minZ maxX maxY maxZ]
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obstacleCorners = [results.domainSize(1)/2, results.domainSize(2)*5/8, 0, results.domainSize(1)*5/8, results.domainSize(2), 35;
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results.domainSize(1)/3, 0, 0, results.domainSize(1)/2, results.domainSize(2)*3/8, 40];
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barrierGain = 1;
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barrierExponent = 1;
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timestep = 0.5;
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@@ -54,35 +55,35 @@ classdef results < matlab.unittest.TestCase
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end
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end
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methods (Static, Access = private)
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methods (Static, Access = public)
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function c = makeConfigs()
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rng(results.seed);
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abMin = 6; % alpha*beta >= 6 ensures membership(0) = tanh(3) >= 0.995
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alphaDist = rand(1, 2) .* [100, 100];
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alphaDist = rand(1, 2) .* [75, 40];
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betaDist = abMin ./ alphaDist + rand(1, 2) .* (20 - abMin ./ alphaDist);
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alphaTilt = 10 + rand(1, 2) .* [20, 20];
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betaTilt = abMin ./ alphaTilt + rand(1, 2) .* (50 - abMin ./ alphaTilt);
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sensors = struct('alphaDist', num2cell(alphaDist), 'alphaTilt', num2cell(alphaTilt), 'betaDist', num2cell(betaDist), 'betaTilt', num2cell(betaTilt));
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sensor1 = sigmoidSensor;
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sensor2 = sigmoidSensor;
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sensor1 = sensor1.initialize(sensors(1).alphaDist, sensors(1).betaDist, sensors(1).alphaTilt, sensors(1).betaTilt);
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sensor2 = sensor2.initialize(sensors(2).alphaDist, sensors(2).betaDist, sensors(2).alphaTilt, sensors(2).betaTilt);
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sensor1.plotParameters;
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sensor2.plotParameters;
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c = struct('A_1_alpha', struct('numDist', 1, 'sensor', sensors(1), 'doubleIntegrator', false), ...
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'A_1_beta', struct('numDist', 1, 'sensor', sensors(1), 'doubleIntegrator', true), ...
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'A_2_alpha', struct('numDist', 1, 'sensor', sensors(2), 'doubleIntegrator', false), ...
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'B_1_beta', struct('numDist', 2, 'sensor', sensors(1), 'doubleIntegrator', true));
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% sensor1 = sigmoidSensor;
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% sensor2 = sigmoidSensor;
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% sensor1 = sensor1.initialize(sensors(1).alphaDist, sensors(1).betaDist, sensors(1).alphaTilt, sensors(1).betaTilt);
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% sensor2 = sensor2.initialize(sensors(2).alphaDist, sensors(2).betaDist, sensors(2).alphaTilt, sensors(2).betaTilt);
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% sensor1.plotParameters;
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% sensor2.plotParameters;
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c = struct('A_1_alpha', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(1), 'doubleIntegrator', false), ...
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'A_1_beta', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(1), 'doubleIntegrator', true), ...
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'A_2_alpha', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(2), 'doubleIntegrator', false), ...
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'B_1_beta', struct('objectivePos', [[3, 1] / 4 .* results.domainSize(1:2); [3, 1] / 4 .* results.domainSize(1:2) + 12.5 .* [-1, 1] ./ sqrt(2)], 'sensor', sensors(1), 'doubleIntegrator', true));
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end
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end
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methods (Test)
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function plot1_runs(tc, n, config)
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% OVERRIDES
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% function plot1_runs(tc)
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% n = 3;
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% config = struct('numDist', 1, 'sensor', struct('alphaDist', 100, 'alphaTilt', 2, 'betaDist', 10, 'betaTilt', 0.5), 'doubleIntegrator', false);
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% if n == 5 && config.doubleIntegrator == true
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% tc.makePlots = true;
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% else
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% tc.makePlots = false;
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% end
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% Compute test case index for reinit lookup
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nKeys = fieldnames(tc.n);
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configKeys = fieldnames(tc.config);
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@@ -95,27 +96,21 @@ classdef results < matlab.unittest.TestCase
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for reroll = 0:reinitCount
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% Set up random cube domain
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minAlt = tc.minDimension(1) * rand() * 0.5;
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tc.testClass.domain = tc.testClass.domain.initializeRandom(REGION_TYPE.DOMAIN, "Domain", tc.minDimension, tc.maxDimension, tc.testClass.domain, minAlt);
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% Place sensing objective(s)
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objectiveMu = [];
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% Set up fixed-size domain
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minAlt = tc.domainSize(3)/10 + rand * 1/10 * tc.domainSize(3);
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% Place sensing objective(s) at fixed positions from config
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objectiveMu = config.objectivePos;
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numDist = size(objectiveMu, 1);
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objectiveSigma = [];
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for ii = 1:config.numDist
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mu = tc.testClass.domain.minCorner;
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while tc.testClass.domain.distance(mu) < tc.protectedRange * 1.01
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mu = tc.testClass.domain.random();
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for ii = 1:numDist
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sig = [200, 140; 140, 280];
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if ~mod(ii, 2)
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sig = rot90(sig, 2);
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end
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notPosDef = true;
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while notPosDef
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sig = reshape(sort(rand(1, 4) * min(tc.testClass.domain.dimensions(1:2))), [1, 2, 2]);
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sig(1, 2, 1) = max([sig(1, 1, 2), sig(1, 2, 1)]);
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sig(1, 1, 2) = sig(1, 2, 1);
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[~, notPosDef] = chol(squeeze(sig));
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end
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objectiveMu = [objectiveMu; mu(1:2)];
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sig = reshape(sig, [1, 2, 2]);
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objectiveSigma = cat(1, objectiveSigma, sig);
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end
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tc.testClass.domain = tc.testClass.domain.initialize([zeros(1, 3); tc.domainSize], REGION_TYPE.DOMAIN, "Domain");
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tc.testClass.domain.objective = tc.testClass.domain.objective.initialize(objectiveFunctionWrapper(objectiveMu, objectiveSigma), tc.testClass.domain, tc.discretizationStep, tc.protectedRange, tc.sensorPerformanceMinimum, objectiveMu, objectiveSigma);
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% Initialize agents
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@@ -126,28 +121,44 @@ classdef results < matlab.unittest.TestCase
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sensorModel = sigmoidSensor;
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sensorModel = sensorModel.initialize(config.sensor.alphaDist, config.sensor.betaDist, config.sensor.alphaTilt, config.sensor.betaTilt);
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% Place agents in a quadrant that contains no objective peaks
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% Initialize fixed obstacles from corner coordinates
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nObs = size(tc.obstacleCorners, 1);
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obstacles = cell(nObs, 1);
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for jj = 1:nObs
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corners = [tc.obstacleCorners(jj, 1:3); tc.obstacleCorners(jj, 4:6)];
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obstacles{jj} = rectangularPrism;
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obstacles{jj} = obstacles{jj}.initialize(corners, REGION_TYPE.OBSTACLE, sprintf("Obstacle %d", jj));
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end
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% Place agents in small-x, large-y quadrant (opposite objectives)
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% with chain topology: each agent connected only to its neighbors
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midXY = (tc.testClass.domain.minCorner(1:2) + tc.testClass.domain.maxCorner(1:2)) / 2;
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occupied = false(2, 2);
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for ii = 1:size(objectiveMu, 1)
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occupied(1 + (objectiveMu(ii, 1) >= midXY(1)), ...
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1 + (objectiveMu(ii, 2) >= midXY(2))) = true;
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quadrantSize = tc.testClass.domain.maxCorner(1:2) / 2;
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margin = quadrantSize / 6;
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agentBounds = [tc.testClass.domain.minCorner(1) + margin(1), ...
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midXY(2) + margin(2); ...
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midXY(1) - margin(1), ...
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tc.testClass.domain.maxCorner(2) - margin(2)];
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% Find a fixed altitude where sensor performance passes at ALL
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% corners of the placement bounds (worst-case XY)
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corners = [agentBounds(1,1), agentBounds(1,2);
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agentBounds(2,1), agentBounds(1,2);
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agentBounds(1,1), agentBounds(2,2);
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agentBounds(2,1), agentBounds(2,2)];
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agentAlt = tc.testClass.domain.maxCorner(3) - tc.collisionRadius;
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while agentAlt > minAlt + 2 * tc.collisionRadius
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worstPerf = inf;
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for cc = 1:4
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p = sensorModel.sensorPerformance([corners(cc,:), agentAlt], [corners(cc,:), 0]);
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worstPerf = min(worstPerf, p);
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end
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if worstPerf >= tc.sensorPerformanceMinimum * 10
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break;
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end
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agentAlt = agentAlt - 1;
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end
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freeQ = find(~occupied);
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if isempty(freeQ)
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qi = 1;
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else
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qi = freeQ(randi(numel(freeQ)));
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end
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[xi, yi] = ind2sub([2, 2], qi);
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xLim = [tc.testClass.domain.minCorner(1), midXY(1), tc.testClass.domain.maxCorner(1)];
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yLim = [tc.testClass.domain.minCorner(2), midXY(2), tc.testClass.domain.maxCorner(2)];
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agentBounds = [max(xLim(xi), tc.testClass.domain.minCorner(1) + tc.collisionRadius), ...
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max(yLim(yi), tc.testClass.domain.minCorner(2) + tc.collisionRadius), ...
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minAlt + tc.collisionRadius; ...
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min(xLim(xi+1), tc.testClass.domain.maxCorner(1) - tc.collisionRadius), ...
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min(yLim(yi+1), tc.testClass.domain.maxCorner(2) - tc.collisionRadius), ...
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tc.testClass.domain.maxCorner(3) - tc.collisionRadius];
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chainSpacingMin = 0.7 * tc.comRange;
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chainSpacingMax = 0.9 * tc.comRange;
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collisionGeometry = spherical;
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for jj = 1:n
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retry = true;
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@@ -155,54 +166,66 @@ classdef results < matlab.unittest.TestCase
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retry = false;
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if jj == 1
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% First agent: uniform random within placement bounds
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agentPos = agentBounds(1, :) + (agentBounds(2, :) - agentBounds(1, :)) .* rand(1, 3);
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% First agent: random XY within bounds, fixed altitude
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agentPos = [agentBounds(1, :) + (agentBounds(2, :) - agentBounds(1, :)) .* rand(1, 2), agentAlt];
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else
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% Sample near centroid of existing agents to maximize
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% probability of being within comRange of all others
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positions = cell2mat(cellfun(@(x) x.pos, agents(1:(jj-1)), 'UniformOutput', false));
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centroid = mean(positions, 1);
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maxSpread = max(vecnorm(positions - centroid, 2, 2));
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safeRadius = tc.comRange - maxSpread;
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if safeRadius > 2 * tc.collisionRadius
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% Uniform random within guaranteed-connected sphere
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dir = randn(1, 3);
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dir = dir / norm(dir);
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r = safeRadius * rand()^(1/3);
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agentPos = centroid + r * dir;
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else
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% Safe sphere too small; sample within comms sphere
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% of random existing agent (comRange check below)
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baseIdx = randi(jj - 1);
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agentPos = agents{baseIdx}.commsGeometry.random();
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end
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% Place at 0.7-0.9 * comRange in XY from previous agent, same altitude
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dir = randn(1, 2);
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dir = dir / norm(dir);
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r = chainSpacingMin + rand * (chainSpacingMax - chainSpacingMin);
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agentPos = [agents{jj-1}.pos(1:2) + r * dir, agentAlt];
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end
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% Check within placement bounds
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if any(agentPos <= agentBounds(1, :)) || any(agentPos >= agentBounds(2, :))
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% Check within placement bounds (XY only, Z is fixed)
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if any(agentPos(1:2) <= agentBounds(1, :)) || any(agentPos(1:2) >= agentBounds(2, :))
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retry = true;
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continue;
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end
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% Check sensor performance threshold
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% Check sensor performance threshold; lower altitude if it fails
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if sensorModel.sensorPerformance(agentPos, [agentPos(1:2), 0]) < tc.sensorPerformanceMinimum * 10
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agentAlt = max(agentAlt - tc.collisionRadius, minAlt + 1.1 * tc.collisionRadius);
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agentPos(3) = agentAlt;
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% If we've hit the floor and still failing, widen XY search
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if agentAlt <= minAlt + 2 * tc.collisionRadius
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agentBounds = [tc.testClass.domain.minCorner(1) + tc.collisionRadius, ...
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tc.testClass.domain.minCorner(2) + tc.collisionRadius; ...
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tc.testClass.domain.maxCorner(1) - tc.collisionRadius, ...
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tc.testClass.domain.maxCorner(2) - tc.collisionRadius];
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end
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retry = true;
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continue;
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end
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% Check within comRange of ALL existing agents (complete graph)
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% Must be within comRange of previous agent (chain link)
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if jj > 1 && norm(agents{jj-1}.pos - agentPos) >= tc.comRange
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retry = true;
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continue;
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end
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% Must be BEYOND comRange of all non-adjacent agents (sparsity)
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% for kk = 1:(jj - 2)
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% if norm(agents{kk}.pos - agentPos) < tc.comRange
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% retry = true;
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% break;
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% end
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% end
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% if retry, continue; end
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% No collision with any existing agent
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for kk = 1:(jj - 1)
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if norm(agents{kk}.pos - agentPos) >= tc.comRange
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if norm(agents{kk}.pos - agentPos) < agents{kk}.collisionGeometry.radius + tc.collisionRadius
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retry = true;
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break;
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end
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end
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if retry, continue; end
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% Check collision with ALL existing agents
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for kk = 1:(jj - 1)
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if norm(agents{kk}.pos - agentPos) < agents{kk}.collisionGeometry.radius + tc.collisionRadius
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% No collision with any obstacle
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for kk = 1:nObs
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P = min(max(agentPos, obstacles{kk}.minCorner), obstacles{kk}.maxCorner);
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d = agentPos - P;
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if dot(d, d) <= tc.collisionRadius^2
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retry = true;
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break;
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end
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@@ -217,71 +240,6 @@ classdef results < matlab.unittest.TestCase
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% Randomly shuffle agents to vary index-based topology
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agents = agents(randperm(numel(agents)));
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% Add random obstacles (each limited to 1/4 domain size in X and Y)
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obstacles = cell(tc.numObstacles, 1);
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[obstacles{:}] = deal(rectangularPrism);
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% Define target region for obstacles (between agents and objective)
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agentExtent = max(cell2mat(cellfun(@(x) x.pos(1:2), agents, "UniformOutput", false))) + max(cellfun(@(x) x.collisionGeometry.radius, agents));
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objExtent = tc.testClass.domain.objective.groundPos - tc.testClass.domain.objective.protectedRange;
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obsMin = zeros(1, 2);
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obsMax = zeros(1, 2);
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for dim = 1:2
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if agentExtent(dim) < objExtent(dim)
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obsMin(dim) = agentExtent(dim);
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obsMax(dim) = objExtent(dim);
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else
|
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|
obsMin(dim) = tc.testClass.domain.minCorner(dim);
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obsMax(dim) = tc.testClass.domain.maxCorner(dim);
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end
|
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|
end
|
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|
|
maxObsSize = 3 * tc.collisionRadius * ones(1, 3);
|
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|
|
|
|
|
|
|
|
for jj = 1:size(obstacles, 1)
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|
retry = true;
|
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|
|
|
while retry
|
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|
|
|
retry = false;
|
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|
|
|
|
|
|
|
|
% Generate random anchor point, then random size up to 3x collision radius
|
|
|
|
|
anchor = [obsMin + rand(1, 2) .* (obsMax - obsMin), minAlt];
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|
|
|
obsSize = rand(1, 3) .* maxObsSize;
|
|
|
|
|
corners = [anchor; anchor + obsSize];
|
|
|
|
|
|
|
|
|
|
% 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
|
|
|
|
|
|
|
|
|
|
end % reroll loop
|
|
|
|
|
|
|
|
|
|
% Inspect scenario if enabled
|
|
|
|
@@ -305,6 +263,11 @@ classdef results < matlab.unittest.TestCase
|
|
|
|
|
|
|
|
|
|
% Cleanup
|
|
|
|
|
tc.testClass = tc.testClass.teardown();
|
|
|
|
|
close all;
|
|
|
|
|
end
|
|
|
|
|
function AIIbeta_plots_3_4(tc)
|
|
|
|
|
configs = results.makeConfigs();
|
|
|
|
|
config = configs.A_2_alpha;
|
|
|
|
|
end
|
|
|
|
|
end
|
|
|
|
|
|
|
|
|
|
