kdee/miSim
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

'

Browse Source
This commit is contained in:
Kevin Dee
2026-01-27 21:46:40 -08:00
parent a68690a5cf
commit 03fae7077c
58 changed files with 214 additions and 213 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
test/parametricTestSuite.m
View File

@@ -16,7 +16,7 @@ classdef parametricTestSuite < matlab.unittest.TestCase
protectedRange = 0;
%% Test iterations
csvPath = fullfile(matlab.project.rootProject().RootFolder, 'test', 'testIterations.csv');
csvPath = fullfile(matlab.project.rootProject().RootFolder, "test", "testIterations.csv");
end
methods (TestMethodSetup)
@@ -37,20 +37,21 @@ classdef parametricTestSuite < matlab.unittest.TestCase
% File input validation
assert(isfile(csvPath), "%s is not a valid filepath.");
assert(endsWith(csvPath, '.csv'), "%s is not a CSV file.");
assert(endsWith(csvPath, ".csv"), "%s is not a CSV file.");
% Read file
csv = readtable(csvPath, 'TextType', 'string', 'NumHeaderLines', 0, "VariableNamingRule", "Preserve");
csv.Properties.VariableNames = ["timestep", "maxIter", "minAlt", "discretizationStep", "sensorPerformanceMinimum", "initialStepSize", "barrierGain", "barrierExponent", "numAgents", "collisionRadius", "comRange", "alphaDist", "betaDist", "alphaTilt", "betaTilt"];
csv = readtable(csvPath, "TextType", "String", "NumHeaderLines", 0, "VariableNamingRule", "Preserve");
csv.Properties.VariableNames = ["timestep", "maxIter", "minAlt", "discretizationStep", "sensorPerformanceMinimum", "initialStepSize", "barrierGain", "barrierExponent", "numObstacles", "numAgents", "collisionRadius", "comRange", "alphaDist", "betaDist", "alphaTilt", "betaTilt"];
for ii = 1:size(csv.Properties.VariableNames, 2)
csv.(csv.Properties.VariableNames{ii}) = cell2mat(cellfun(@(x) str2num(x), csv.(csv.Properties.VariableNames{ii}), 'UniformOutput', false));
csv.(csv.Properties.VariableNames{ii}) = cell2mat(cellfun(@(x) str2num(x), csv.(csv.Properties.VariableNames{ii}), "UniformOutput", false));
end
% Put params into standard structure
params = struct('timestep', csv.timestep, 'maxIter', csv.maxIter, 'minAlt', csv.minAlt, 'discretizationStep', csv.discretizationStep, ...
'sensorPerformanceMinimum', csv.sensorPerformanceMinimum, 'initialStepSize', csv.initialStepSize, 'barrierGain', csv.barrierGain, 'barrierExponent', csv.barrierExponent, ...
'numAgents', csv.numAgents, 'collisionRadius', csv.collisionRadius, 'comRange', csv.comRange, 'alphaDist', csv.alphaDist, 'betaDist', csv.betaDist, 'alphaTilt', csv.alphaTilt, 'betaTilt', csv.betaTilt);
params = struct("timestep", csv.timestep, "maxIter", csv.maxIter, "minAlt", csv.minAlt, "discretizationStep", csv.discretizationStep, ...
"sensorPerformanceMinimum", csv.sensorPerformanceMinimum, "initialStepSize", csv.initialStepSize, ...
"barrierGain", csv.barrierGain, "barrierExponent", csv.barrierExponent, "numObstacles", csv.numObstacles,...
"numAgents", csv.numAgents, "collisionRadius", csv.collisionRadius, "comRange", csv.comRange, "alphaDist", csv.alphaDist, "betaDist", csv.betaDist, "alphaTilt", csv.alphaTilt, "betaTilt", csv.betaTilt);
end
end

6
test/testIterations.csv
View File

@@ -1,3 +1,3 @@
timestep, maxIter, minAlt, discretizationStep, sensorPerformanceMinimum, initialStepSize, barrierGain, barrierExponent, numAgents, collisionRadius, comRange, alphaDist, betaDist, alphaTilt, betaTilt
1, 10, 1, 0.01, 1e-6, 0.2, 100, 3, 5, "0.1, 0.1, 0.1, 0.1, 0.1", "2.5, 2.5, 2.5, 2.5, 2.5", "3, 3, 3, 3, 3", "15, 15, 15, 15, 15", "3, 3, 3, 3, 3", "3, 3, 3, 3, 3"
1, 10, 1, 0.01, 1e-6, 0.2, 100, 3, 5, "0.1, 0.1, 0.1, 0.1, 0.1", "3.5, 3.5, 3.5, 3.5, 3.5", "15, 15, 15, 15, 15", "30, 30, 30, 30, 30", "15, 15, 15, 15, 15", "3, 3, 3, 3, 3"
timestep, maxIter, minAlt, discretizationStep, sensorPerformanceMinimum, initialStepSize, barrierGain, barrierExponent, numObstacles, numAgents, collisionRadius, comRange, alphaDist, betaDist, alphaTilt, betaTilt
1, 10, 1, 0.01, 1e-6, 0.2, 100, 3, 4, 5, "0.1, 0.1, 0.1, 0.1, 0.1", "2.5, 2.5, 2.5, 2.5, 2.5", "3, 3, 3, 3, 3", "15, 15, 15, 15, 15", "3, 3, 3, 3, 3", "3, 3, 3, 3, 3"
1, 10, 1, 0.01, 1e-6, 0.2, 100, 3, 4, 5, "0.1, 0.1, 0.1, 0.1, 0.1", "3.5, 3.5, 3.5, 3.5, 3.5", "15, 15, 15, 15, 15", "30, 30, 30, 30, 30", "15, 15, 15, 15, 15", "3, 3, 3, 3, 3"
1 timestep maxIter minAlt discretizationStep sensorPerformanceMinimum initialStepSize barrierGain barrierExponent numObstacles numAgents collisionRadius comRange alphaDist betaDist alphaTilt betaTilt
2 1 10 1 0.01 1e-6 0.2 100 3 4 5 0.1, 0.1, 0.1, 0.1, 0.1 2.5, 2.5, 2.5, 2.5, 2.5 3, 3, 3, 3, 3 15, 15, 15, 15, 15 3, 3, 3, 3, 3 3, 3, 3, 3, 3
3 1 10 1 0.01 1e-6 0.2 100 3 4 5 0.1, 0.1, 0.1, 0.1, 0.1 3.5, 3.5, 3.5, 3.5, 3.5 15, 15, 15, 15, 15 30, 30, 30, 30, 30 15, 15, 15, 15, 15 3, 3, 3, 3, 3

24
test/test_miSim.m
View File

@@ -88,7 +88,7 @@ classdef test_miSim < matlab.unittest.TestCase
methods (Test)
% Test methods
function misim_initialization(tc)
function miSim_initialization(tc)
% randomly create obstacles
nGeom = tc.minNumObstacles + randi(tc.maxNumObstacles - tc.minNumObstacles);
tc.obstacles = cell(nGeom, 1);
@@ -123,7 +123,7 @@ classdef test_miSim < matlab.unittest.TestCase
candidatePos(3) = tc.minAlt + rand * 3; % place agents at decent altitudes for sensing
end
else
candidatePos = tc.agents{randi(ii - 1)}.pos + sign(randn([1, 3])) .* (rand(1, 3) .* tc.comRange/sqrt(2));
candidatePos = tc.agents{randi(ii - 1)}.pos + sign(randn([1, 3])) .* (rand(1, 3) .* tc.commsRanges(ii)/sqrt(2));
candidatePos(3) = tc.minAlt + rand * 3; % place agents at decent altitudes for sensing
end
@@ -144,7 +144,7 @@ classdef test_miSim < matlab.unittest.TestCase
% graph between the agents
connections = false(1, ii - 1);
for jj = 1:(ii - 1)
if norm(tc.agents{jj}.pos - candidatePos) <= tc.comRange
if norm(tc.agents{jj}.pos - candidatePos) <= min(tc.commsRanges([ii, jj]))
% Check new agent position against all existing
% agent positions for communications range
connections(jj) = true;
@@ -170,7 +170,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.sensor = tc.sensor.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
% Initialize candidate agent
newAgent = tc.agents{ii}.initialize(candidatePos, candidateGeometry, tc.sensor, tc.comRange, tc.maxIter, tc.initialStepSize);
newAgent = tc.agents{ii}.initialize(candidatePos, candidateGeometry, tc.sensor, tc.commsRanges(ii), tc.maxIter, tc.initialStepSize);
% Make sure candidate agent doesn't collide with
% domain
@@ -220,7 +220,7 @@ classdef test_miSim < matlab.unittest.TestCase
% Initialize the simulation
tc.testClass = tc.testClass.initialize(tc.domain, tc.agents, tc.barrierGain, tc.barrierExponent, tc.minAlt, tc.timestep, tc.maxIter, tc.obstacles, tc.makePlots, tc.makeVideo);
end
function misim_run(tc)
function miSim_run(tc)
% randomly create obstacles
nGeom = tc.minNumObstacles + randi(tc.maxNumObstacles - tc.minNumObstacles);
tc.obstacles = cell(nGeom, 1);
@@ -254,7 +254,7 @@ classdef test_miSim < matlab.unittest.TestCase
candidatePos(3) = min([tc.domain.maxCorner(3) * 0.95, tc.minAlt + rand * (tc.alphaDistMax * (1.1) - 0.5)]); % place agents at decent altitudes for sensing
end
else
candidatePos = tc.agents{randi(ii - 1)}.pos + sign(randn([1, 3])) .* (rand(1, 3) .* tc.comRange/sqrt(2));
candidatePos = tc.agents{randi(ii - 1)}.pos + sign(randn([1, 3])) .* (rand(1, 3) .* tc.commsRanges(ii)/sqrt(2));
candidatePos(3) = min([tc.domain.maxCorner(3) * 0.95, tc.minAlt + rand * (tc.alphaDistMax * (1.1) - 0.5)]); % place agents at decent altitudes for sensing
end
@@ -275,7 +275,7 @@ classdef test_miSim < matlab.unittest.TestCase
% graph between the agents
connections = false(1, ii - 1);
for jj = 1:(ii - 1)
if norm(tc.agents{jj}.pos - candidatePos) <= tc.comRange
if norm(tc.agents{jj}.pos - candidatePos) <= min(tc.commsRanges([ii, jj]))
% Check new agent position against all existing
% agent positions for communications range
connections(jj) = true;
@@ -303,7 +303,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.sensor = tc.sensor.initialize(tc.alphaDistMin + rand * (tc.alphaDistMax - tc.alphaDistMin), tc.betaDistMin + rand * (tc.betaDistMax - tc.betaDistMin), tc.alphaTiltMin + rand * (tc.alphaTiltMax - tc.alphaTiltMin), tc.betaTiltMin + rand * (tc.betaTiltMax - tc.betaTiltMin));
% Initialize candidate agent
newAgent = tc.agents{ii}.initialize(candidatePos, candidateGeometry, tc.sensor, tc.comRange, tc.maxIter, tc.initialStepSize);
newAgent = tc.agents{ii}.initialize(candidatePos, candidateGeometry, tc.sensor, tc.commsRanges(ii), tc.maxIter, tc.initialStepSize);
% Make sure candidate agent doesn't collide with
% domain
@@ -389,9 +389,9 @@ classdef test_miSim < matlab.unittest.TestCase
centerIdx = floor(size(tc.testClass.partitioning, 1) / 2);
tc.verifyEqual(tc.testClass.partitioning(centerIdx, centerIdx:(centerIdx + 2)), [2, 3, 1]); % all three near center
tc.verifyLessThan(sum(tc.testClass.partitioning == 1, 'all'), sum(tc.testClass.partitioning == 0, 'all')); % more non-assignments than partition 1 assignments
tc.verifyLessThan(sum(tc.testClass.partitioning == 2, 'all'), sum(tc.testClass.partitioning == 1, 'all')); % more partition 1 assignments than partition 2 assignments
tc.verifyLessThan(sum(tc.testClass.partitioning == 3, 'all'), sum(tc.testClass.partitioning == 2, 'all')); % more partition 3 assignments than partition 2 assignments
tc.verifyLessThan(sum(tc.testClass.partitioning == 1, "all"), sum(tc.testClass.partitioning == 0, "all")); % more non-assignments than partition 1 assignments
tc.verifyLessThan(sum(tc.testClass.partitioning == 2, "all"), sum(tc.testClass.partitioning == 1, "all")); % more partition 1 assignments than partition 2 assignments
tc.verifyLessThan(sum(tc.testClass.partitioning == 3, "all"), sum(tc.testClass.partitioning == 2, "all")); % more partition 3 assignments than partition 2 assignments
tc.verifyEqual(unique(tc.testClass.partitioning), [0; 1; 2; 3;]);
end
function test_single_partition(tc)
@@ -414,7 +414,7 @@ classdef test_miSim < matlab.unittest.TestCase
close(tc.testClass.fPerf);
tc.verifyEqual(unique(tc.testClass.partitioning), [0; 1]);
tc.verifyLessThan(sum(tc.testClass.partitioning == 1, 'all'), sum(tc.testClass.partitioning == 0, 'all'));
tc.verifyLessThan(sum(tc.testClass.partitioning == 1, "all"), sum(tc.testClass.partitioning == 0, "all"));
end
function test_single_agent_gradient_ascent(tc)
% make basic domain

10
test/test_sigmoidSensor.m
View File

@@ -41,21 +41,21 @@ classdef test_sigmoidSensor < matlab.unittest.TestCase
% Anticipate perfect performance for a point directly below and
% extremely close
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], 0, [0, 0, 0]), 1, 'RelTol', 1e-3);
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], [0, 0, 0]), 1, "RelTol", 1e-3);
% It looks like mu_t can max out at really low values like 0.37
% when alphaTilt and betaTilt are small, which seems wrong
% Performance at nadir point, distance alphaDist should be 1/2 exactly
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, alphaDist], 0, [0, 0, 0]), 1/2);
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, alphaDist],[0, 0, 0]), 1/2);
% Performance at (almost) 0 distance, alphaTilt should be 1/2
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], 0, [tand(alphaTilt)*h, 0, 0]), 1/2, 'RelTol', 1e-3);
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], [tand(alphaTilt)*h, 0, 0]), 1/2, "RelTol", 1e-3);
% Performance at great distance should be 0
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, 10], 0, [0, 0, 0]), 0, 'AbsTol', 1e-9);
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, 10], [0, 0, 0]), 0, "AbsTol", 1e-9);
% Performance at great tilt should be 0
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], 0, [5, 5, 0]), 0, 'AbsTol', 1e-9);
tc.verifyEqual(tc.testClass.sensorPerformance([0, 0, h], [5, 5, 0]), 0, "AbsTol", 1e-9);
end
end