gradient ascent works now?
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@@ -36,22 +36,42 @@ function obj = initialize(obj, pos, vel, pan, tilt, collisionGeometry, sensorMod
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axes(obj.debugFig.Children(1).Children(1));
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axis(obj.debugFig.Children(1).Children(1), "image");
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xlabel(obj.debugFig.Children(1).Children(1), "X"); ylabel(obj.debugFig.Children(1).Children(1), "Y");
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title(obj.debugFig.Children(1).Children(1), "Objective View");
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title(obj.debugFig.Children(1).Children(1), "Objective");
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nexttile;
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axes(obj.debugFig.Children(1).Children(1));
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axis(obj.debugFig.Children(1).Children(1), "image");
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xlabel(obj.debugFig.Children(1).Children(1), "X"); ylabel(obj.debugFig.Children(1).Children(1), "Y");
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title(obj.debugFig.Children(1).Children(1), "Sensor Performance View");
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title(obj.debugFig.Children(1).Children(1), "Sensor Performance");
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nexttile;
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axes(obj.debugFig.Children(1).Children(1));
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axis(obj.debugFig.Children(1).Children(1), "image");
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xlabel(obj.debugFig.Children(1).Children(1), "X"); ylabel(obj.debugFig.Children(1).Children(1), "Y");
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title(obj.debugFig.Children(1).Children(1), "Gradient Objective View");
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title(obj.debugFig.Children(1).Children(1), "Gradient Objective");
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nexttile;
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axes(obj.debugFig.Children(1).Children(1));
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axis(obj.debugFig.Children(1).Children(1), "image");
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xlabel(obj.debugFig.Children(1).Children(1), "X"); ylabel(obj.debugFig.Children(1).Children(1), "Y");
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title(obj.debugFig.Children(1).Children(1), "Gradient Sensor Performance View");
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title(obj.debugFig.Children(1).Children(1), "Gradient Sensor Performance");
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nexttile;
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axes(obj.debugFig.Children(1).Children(1));
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axis(obj.debugFig.Children(1).Children(1), "image");
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xlabel(obj.debugFig.Children(1).Children(1), "X"); ylabel(obj.debugFig.Children(1).Children(1), "Y");
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title(obj.debugFig.Children(1).Children(1), "Sensor Performance x Gradient Objective");
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nexttile;
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axes(obj.debugFig.Children(1).Children(1));
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axis(obj.debugFig.Children(1).Children(1), "image");
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xlabel(obj.debugFig.Children(1).Children(1), "X"); ylabel(obj.debugFig.Children(1).Children(1), "Y");
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title(obj.debugFig.Children(1).Children(1), "Gradient Sensor Performance x Objective");
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nexttile;
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axes(obj.debugFig.Children(1).Children(1));
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axis(obj.debugFig.Children(1).Children(1), "image");
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xlabel(obj.debugFig.Children(1).Children(1), "X"); ylabel(obj.debugFig.Children(1).Children(1), "Y");
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title(obj.debugFig.Children(1).Children(1), "Agent Performance (C)");
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nexttile;
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axes(obj.debugFig.Children(1).Children(1));
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axis(obj.debugFig.Children(1).Children(1), "image");
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xlabel(obj.debugFig.Children(1).Children(1), "X"); ylabel(obj.debugFig.Children(1).Children(1), "Y");
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title(obj.debugFig.Children(1).Children(1), "Gradient Agent Performance (del C)");
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end
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% Initialize FOV cone
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85
@agent/run.m
85
@agent/run.m
@@ -18,14 +18,14 @@ function obj = run(obj, domain, partitioning, t)
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maskedY = domain.objective.Y(partitionMask);
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sensorValues = obj.sensorModel.sensorPerformance(obj.pos, obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
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% Put the values back into the form of the partition
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% Put the values back into the form of the partition to enable basic operations on this data
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F = NaN(size(partitionMask));
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F(partitionMask) = objectiveValues;
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S = NaN(size(partitionMask));
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S(partitionMask) = sensorValues;
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% Find agent's performance
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C = S.* F;
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C = S.* F; % try gradient on this directly
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obj.performance = [obj.performance sum(C(~isnan(C)))];
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% Compute gradient on agent's performance
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@@ -35,31 +35,78 @@ function obj = run(obj, domain, partitioning, t)
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gradS = cat(3, gradSensorPerformanceX, gradSensorPerformanceY, zeros(size(gradSensorPerformanceX))); % grad S_n
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gradF = cat(3, gradObjectiveX, gradObjectiveY, zeros(size(gradObjectiveX))); % grad f
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[gradCX, gradCY] = gradient(C, domain.objective.discretizationStep); % grad C;
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gradC = cat(3, gradCX, gradCY, zeros(size(gradCX))); % temp zeros for gradCZ
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nGradC = vecnorm(gradC, 2, 3);
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if obj.debug
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hold(obj.debugFig.Children(1).Children(4), "on");
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imagesc(obj.debugFig.Children(1).Children(4), F);
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hold(obj.debugFig.Children(1).Children(4), "off");
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hold(obj.debugFig.Children(1).Children(3), "on");
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imagesc(obj.debugFig.Children(1).Children(3), S);
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hold(obj.debugFig.Children(1).Children(3), "off");
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hold(obj.debugFig.Children(1).Children(2), "on");
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imagesc(obj.debugFig.Children(1).Children(2), gradF./max(gradF, [], 'all'));
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hold(obj.debugFig.Children(1).Children(2), "off");
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hold(obj.debugFig.Children(1).Children(1), "on");
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imagesc(obj.debugFig.Children(1).Children(1), abs(gradS)./max(gradS, [], 'all'));
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hold(obj.debugFig.Children(1).Children(1), "off");
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ii = 8;
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hold(obj.debugFig.Children(1).Children(ii), "on");
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imagesc(obj.debugFig.Children(1).Children(ii), F./max(F, [], 'all'));
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hold(obj.debugFig.Children(1).Children(ii), "off");
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ii = ii - 1;
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hold(obj.debugFig.Children(1).Children(ii), "on");
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imagesc(obj.debugFig.Children(1).Children(ii), S./max(S, [], 'all'));
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hold(obj.debugFig.Children(1).Children(ii), "off");
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ii = ii - 1;
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hold(obj.debugFig.Children(1).Children(ii), "on");
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imagesc(obj.debugFig.Children(1).Children(ii), vecnorm(gradF, 2, 3)./max(vecnorm(gradF, 2, 3), [], 'all'));
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hold(obj.debugFig.Children(1).Children(ii), "off");
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ii = ii - 1;
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hold(obj.debugFig.Children(1).Children(ii), "on");
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imagesc(obj.debugFig.Children(1).Children(ii), vecnorm(gradS, 2, 3)./max(vecnorm(gradS, 2, 3), [], 'all'));
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hold(obj.debugFig.Children(1).Children(ii), "off");
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ii = ii - 1;
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hold(obj.debugFig.Children(1).Children(ii), "on");
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imagesc(obj.debugFig.Children(1).Children(ii), S .* vecnorm(gradF, 2, 3)./max(vecnorm(gradF, 2, 3), [], 'all'));
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hold(obj.debugFig.Children(1).Children(ii), "off");
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ii = ii - 1;
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hold(obj.debugFig.Children(1).Children(ii), "on");
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imagesc(obj.debugFig.Children(1).Children(ii), F .* vecnorm(gradS, 2, 3)./max(vecnorm(gradS, 2, 3), [], 'all')./(max(F .* vecnorm(gradS, 2, 3)./max(vecnorm(gradS, 2, 3), [], 'all'))));
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hold(obj.debugFig.Children(1).Children(ii), "off");
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ii = ii - 1;
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hold(obj.debugFig.Children(1).Children(ii), "on");
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imagesc(obj.debugFig.Children(1).Children(ii), C./max(C, [], 'all'));
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hold(obj.debugFig.Children(1).Children(ii), "off");
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ii = ii - 1;
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hold(obj.debugFig.Children(1).Children(ii), "on");
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imagesc(obj.debugFig.Children(1).Children(ii), nGradC./max(nGradC, [], 'all'));
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hold(obj.debugFig.Children(1).Children(ii), "off");
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[x, y] = find(nGradC == max(nGradC, [], "all"));
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% just pick one
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r = randi([1, size(x, 1)]);
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x = x(r); y = y(r);
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% find objective location in discrete domain
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[~, xIdx] = find(domain.objective.groundPos(1) == domain.objective.X);
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xIdx = unique(xIdx);
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[yIdx, ~] = find(domain.objective.groundPos(2) == domain.objective.Y);
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yIdx = unique(yIdx);
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for ii = 8:-1:1
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hold(obj.debugFig.Children(1).Children(ii), "on");
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% plot GA selection
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scatter(obj.debugFig.Children(1).Children(ii), x, y, 'go');
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scatter(obj.debugFig.Children(1).Children(ii), x, y, 'g+');
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% plot objective center
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scatter(obj.debugFig.Children(1).Children(ii), xIdx, yIdx, 'ro');
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scatter(obj.debugFig.Children(1).Children(ii), xIdx, yIdx, 'r+');
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hold(obj.debugFig.Children(1).Children(ii), "off");
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end
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end
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% grad(s*f) = grad(f) * s + f * grad(s) - product rule (f scalar field, s vector field)
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gradC = S .* gradF + F .* abs(gradS); % second term provides altitude
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% gradC = S .* abs(gradF) + F .* abs(gradS); % second term provides altitude
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% normalize in x3 dimension and find the direction which maximizes ascent
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nGradC = vecnorm(gradC, 2, 3);
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% nGradC = vecnorm(gradC, 2, 3);
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[xNextIdx, yNextIdx] = find(nGradC == max(nGradC, [], 'all')); % find direction of steepest increase
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pNext = [floor(mean(unique(domain.objective.X(:, xNextIdx)))), floor(mean(unique(domain.objective.Y(yNextIdx, :)))), obj.pos(3)]; % have to do some unfortunate rounding here soemtimes
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roundingScale = 10^-log10(domain.objective.discretizationStep);
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pNext = [floor(roundingScale .* mean(unique(domain.objective.X(:, xNextIdx))))./roundingScale, floor(roundingScale .* mean(unique(domain.objective.Y(yNextIdx, :))))./roundingScale, obj.pos(3)]; % have to do some unfortunate rounding here soemtimes
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vDir = (pNext - obj.pos)./norm(pNext - obj.pos, 2);
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rate = 0.1 - 0.004 * t;
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nextPos = obj.pos + vDir * rate;
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rate = 0.2 - 0.004 * t;
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nextPos = obj.pos + vDir * rate;
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% Move to next position
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% (dynamics not modeled at this time)
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@@ -418,7 +418,7 @@ classdef test_miSim < matlab.unittest.TestCase
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tc.domain = tc.domain.initialize([zeros(1, 3); l * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
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% make basic sensing objective
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tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2) + rand(1, 2) * 6 - 3), tc.domain, tc.discretizationStep, tc.protectedRange);
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tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2)), tc.domain, tc.discretizationStep, tc.protectedRange);
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% Initialize agent collision geometry
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geometry1 = rectangularPrism;
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