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adjusted partitioning to allow non-assignment

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Kevin D
2025-11-15 17:02:04 -08:00
parent 175a0e02a1
commit c3a840bae2
4 changed files with 12 additions and 3 deletions
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1
@miSim/miSim.m
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@@ -11,6 +11,7 @@ classdef miSim
obstacles = cell(0, 1); % geometries that define obstacles within the domain
agents = cell(0, 1); % agents that move within the domain
adjacency = NaN; % Adjacency matrix representing communications network graph
sensorPerformanceMinimum = 1e-6; % minimum sensor performance to allow assignment of a point in the domain to a partition
partitioning = NaN;
end

2
@miSim/partition.m
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@@ -9,6 +9,7 @@ function obj = partition(obj)
% 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, [obj.objective.X(:), obj.objective.Y(:), zeros(size(obj.objective.X(:)))]), size(obj.objective.X)), obj.agents, 'UniformOutput', false);
agentPerformances{end + 1} = obj.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{:});
% Get highest performance value at each point
@@ -16,6 +17,7 @@ function obj = partition(obj)
% Collect agent indices in the same way
agentInds = cellfun(@(x) x.index * ones(size(obj.objective.X)), obj.agents, 'UniformOutput', false);
agentInds{end + 1} = zeros(size(agentInds{end})); % index for no assignment
agentInds = cat(3, agentInds{:});
% Get highest performing agent's index

4
sensingModels/@sigmoidSensor/sensorPerformance.m
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@@ -17,7 +17,7 @@ function value = sensorPerformance(obj, agentPos, agentPan, agentTilt, targetPos
% Membership functions
mu_d = 1 - (1 ./ (1 + exp(-obj.betaDist .* (d - obj.alphaDist)))); % distance
mu_p = 1; % pan
mu_t = (1 ./ (1 + exp(-obj.betaPan .* (tiltAngle + obj.alphaPan)))) - (1 ./ (1 + exp(-obj.betaPan .* (tiltAngle - obj.alphaPan)))); % tilt
mu_t = (1 ./ (1 + exp(-obj.betaTilt .* (tiltAngle + obj.alphaTilt)))) - (1 ./ (1 + exp(-obj.betaTilt .* (tiltAngle - obj.alphaTilt)))); % tilt
value = mu_d .* mu_p .* mu_t * 1e12;
value = mu_d .* mu_p .* mu_t;
end

8
test/test_miSim.m
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@@ -411,7 +411,7 @@ classdef test_miSim < matlab.unittest.TestCase
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), eye(2)), tc.domain.footprint, tc.domain.minCorner(3), tc.discretizationStep);
tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2)), tc.domain, tc.discretizationStep);
% Initialize agent collision geometry
geometry1 = rectangularPrism;
@@ -428,6 +428,12 @@ classdef test_miSim < matlab.unittest.TestCase
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 - [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 - [0, d, 0], zeros(1, 3), 0, 0, geometry3, sensor, @gradientAscent, 3*d, 3, sprintf("Agent %d", 3));
% Initialize the simulation
[tc.testClass, f] = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter);
end