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flawed GA implementation

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This commit is contained in:
Kevin Dee
2025-11-30 09:52:17 -08:00
parent 352d2ed1de
commit f9f070e2d0
4 changed files with 43 additions and 10 deletions
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4
@agent/agent.m
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@@ -27,13 +27,15 @@ classdef agent
% Communication
comRange = NaN;
performance = 0;
% Plotting
scatterPoints;
end
methods (Access = public)
[obj] = initialize(obj, pos, vel, pan, tilt, collisionGeometry, sensorModel, guidanceModel, comRange, index, label);
[obj] = run(obj, sensingObjective, domain, partitioning);
[obj] = run(obj, domain, partitioning);
[obj, f] = plot(obj, ind, f);
updatePlots(obj);
end

41
@agent/run.m
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@@ -1,7 +1,6 @@
function obj = run(obj, sensingObjective, domain, partitioning)
function obj = run(obj, domain, partitioning)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'agent')};
sensingObjective (1, 1) {mustBeA(sensingObjective, 'sensingObjective')};
domain (1, 1) {mustBeGeometry};
partitioning (:, :) double;
end
@@ -9,11 +8,41 @@ function obj = run(obj, sensingObjective, domain, partitioning)
obj (1, 1) {mustBeA(obj, 'agent')};
end
% Do sensing
[sensedValues, sensedPositions] = obj.sensorModel.sense(obj, sensingObjective, domain, partitioning);
% Collect objective function values across partition
partitionMask = partitioning == obj.index;
objectiveValues = domain.objective.values(partitionMask); % f(omega) on W_n
% Determine next planned position
nextPos = obj.guidanceModel(sensedValues, sensedPositions, obj.pos);
% Compute sensor performance across partition
maskedX = domain.objective.X(partitionMask);
maskedY = domain.objective.Y(partitionMask);
sensorValues = obj.sensorModel.sensorPerformance(obj.pos, obj.pan, obj.tilt, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
% Find agent's performance
obj.performance = [obj.performance; sum(objectiveValues .* sensorValues, 'all')];
%%
% Put the values back into the form of the partition
F = NaN(size(partitionMask));
F(partitionMask) = objectiveValues;
S = NaN(size(partitionMask));
S(partitionMask) = sensorValues;
% Compute gradient on agent's performance
[gradSensorPerformanceX, gradSensorPerformanceY] = gradient(S, domain.objective.discretizationStep); % grad S_n
[gradObjectiveX, gradObjectiveY] = gradient(F, domain.objective.discretizationStep); % grad f
gradS = cat(3, gradSensorPerformanceX, gradSensorPerformanceY, zeros(size(gradSensorPerformanceX))); % grad S_n
gradF = cat(3, gradObjectiveX, gradObjectiveY, zeros(size(gradObjectiveX))); % grad f
% grad(s*f) = grad(f) * s + f * grad(s) - product rule (f scalar field, s vector field)
gradC = S .* gradF + F .* gradS; % second term provides altitude
% normalize in x3 dimension and find the direction which maximizes ascent
nGradC = vecnorm(gradC, 2, 3);
[xNextIdx, yNextIdx] = find(nGradC == max(nGradC, [], 'all')); % find direction of steepest increase
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
vDir = (pNext - obj.pos)./norm(pNext - obj.pos, 2);
nextPos = obj.pos + vDir * 0.2;
% Move to next position
% (dynamics not modeled at this time)

2
@miSim/run.m
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@@ -25,7 +25,7 @@ function [obj] = run(obj)
% Iterate over agents to simulate their motion
for jj = 1:size(obj.agents, 1)
obj.agents{jj} = obj.agents{jj}.run(obj.objective, obj.domain, obj.partitioning);
obj.agents{jj} = obj.agents{jj}.run(obj.domain, obj.partitioning);
end
% Update adjacency matrix

6
@sensingObjective/initialize.m
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@@ -10,6 +10,8 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
obj (1,1) {mustBeA(obj, 'sensingObjective')};
end
obj.discretizationStep = discretizationStep;
obj.groundAlt = domain.minCorner(3);
obj.protectedRange = protectedRange;
@@ -19,8 +21,8 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
yMin = min(domain.footprint(:, 2));
yMax = max(domain.footprint(:, 2));
xGrid = unique([xMin:discretizationStep:xMax, xMax]);
yGrid = unique([yMin:discretizationStep:yMax, yMax]);
xGrid = unique([xMin:obj.discretizationStep:xMax, xMax]);
yGrid = unique([yMin:obj.discretizationStep:yMax, yMax]);
% Store grid points for plotting later
[obj.X, obj.Y] = meshgrid(xGrid, yGrid);