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added random obstacles

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This commit is contained in:
Kevin Dee
2026-01-27 23:54:55 -08:00
parent 03fae7077c
commit bf875cbfce
3 changed files with 84 additions and 18 deletions
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94
test/parametricTestSuite.m
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@@ -3,10 +3,9 @@ classdef parametricTestSuite < matlab.unittest.TestCase
% System under test
testClass = miSim;
domain = rectangularPrism;
obstacles = cell(1, 0);
% RNG control
seed = 1234567890;
seed = 1;
%% Diagnostic Parameters
% No effect on simulation dynamics
@@ -41,7 +40,7 @@ classdef parametricTestSuite < matlab.unittest.TestCase
% Read file
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"];
csv.Properties.VariableNames = ["timestep", "maxIter", "minAlt", "discretizationStep", "protectedRange", "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));
@@ -49,7 +48,7 @@ classdef parametricTestSuite < matlab.unittest.TestCase
% 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, ...
"protectedRange", csv.protectedRange, "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
@@ -70,7 +69,7 @@ classdef parametricTestSuite < matlab.unittest.TestCase
for ii = 1:size(params.timestep, 1)
% Set up square domain
tc.domain = tc.domain.initialize([zeros(1, 3); l * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([.75 * l, 0.75 * l]), tc.domain, params.discretizationStep(ii), tc.protectedRange, params.sensorPerformanceMinimum(ii));
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([.75 * l, 0.75 * l]), tc.domain, params.discretizationStep(ii), params.protectedRange(ii), params.sensorPerformanceMinimum(ii));
% Initialize agents
agents = cell(params.numAgents(ii), 1);
@@ -105,29 +104,27 @@ classdef parametricTestSuite < matlab.unittest.TestCase
retry = true;
while retry
agentPos = agents{baseAgentIdx}.commsGeometry.random();
retry = false;
% Check that the agent's greatest sensor
% performance clears the threshold for partitioning
if sensorModel.sensorPerformance(agentPos, [agentPos(1:2), 0]) < params.sensorPerformanceMinimum(ii)
retry = true;
continue;
end
% Check that candidate position is well inside the domain
bounds = [params.collisionRadius(ii, jj) * ones(1, 2), max([params.collisionRadius(ii, jj), params.minAlt(ii)]); l / 2 * ones(1, 2), l - params.collisionRadius(ii, jj)];
if ~isequal(agentPos < bounds, [false, false, false; true, true, true])
if ~retry && ~isequal(agentPos < bounds, [false, false, false; true, true, true])
retry = true;
continue;
end
% Check that candidate position does not collide with existing agents
for kk = 1:(jj - 1)
if norm(agents{kk}.pos - agentPos, 2) < agents{kk}.collisionGeometry.radius + params.collisionRadius(ii, jj)
if ~retry && norm(agents{kk}.pos - agentPos, 2) < agents{kk}.collisionGeometry.radius + params.collisionRadius(ii, jj)
retry = true;
continue;
break;
end
end
retry = false;
end
% Initialize agent
@@ -135,9 +132,66 @@ classdef parametricTestSuite < matlab.unittest.TestCase
agents{jj} = agents{jj}.initialize(agentPos, collisionGeometry, sensorModel, params.comRange(ii, jj), params.maxIter(ii), params.initialStepSize(ii), sprintf("Agent %d", jj), tc.plotCommsGeometry);
end
% randomly shuffle agents to make the network more interesting (probably)
agents = agents(randperm(numel(agents)));
% Set up obstacles
obstacles = cell(params.numObstacles(ii), 1);
[obstacles{:}] = deal(rectangularPrism);
% Define ranges to permit obstacles (relies on certain
% assumptions about agent and objective placement)
bounds = [max(cell2mat(cellfun(@(x) x.pos(1:2), agents, "UniformOutput", false))) + max(cellfun(@(x) x.collisionGeometry.radius, agents)); ...
tc.domain.objective.groundPos - tc.domain.objective.protectedRange];
for jj = 1:size(obstacles, 1)
% randomly place obstacles in at least the X or Y or X
% and Y range defined by the objective region and the
% agents initial region
retry = true;
while retry
retry = false;
% candidate corners for obstacle
corners = [sort(tc.domain.maxCorner(1, 1:2) .* rand(2), 1, "ascend"), [params.minAlt(ii); params.minAlt(ii) + rand * (tc.domain.maxCorner(3) - params.minAlt(ii))]];
% Check X falls into bucket in at least one vertex
if ~retry && ~(any(corners(:, 1) > bounds(1, 1) & corners(:, 1) < bounds(2, 1)) || any(corners(:, 2) > bounds(1, 2) & corners(:, 2) < bounds(2, 2)))
retry = true;
end
% Initialize obstacle using proposed coordinates
if ~retry
obstacles{jj} = obstacles{jj}.initialize(corners, REGION_TYPE.OBSTACLE, sprintf("Obstacle %d", jj));
end
% Make sure the obstacle doesn't crowd the objective
if ~retry && obstacles{jj}.distance([tc.domain.objective.groundPos, params.minAlt(ii)]) <= tc.domain.objective.protectedRange
retry = true;
end
% Check if the obstacle collides with an existing obstacle
if ~retry && jj > 1 && tc.obstacleCollisionCheck(obstacles(1:(jj - 1)), obstacles{jj})
retry = true;
end
% Check if the obstacle collides with an agent
if ~retry
AO_collisions = cellfun(@(a) cellfun(@(o) o.contains(a.pos), obstacles), agents, "UniformOutput", false);
if any(vertcat(AO_collisions{:}))
retry = true;
end
end
if retry
continue;
end
end
end
% Set up simulation
agents = agents(randperm(numel(agents))); % randomly shuffle agents to make the network more interesting (probably)
tc.testClass = tc.testClass.initialize(tc.domain, agents, params.barrierGain(ii), params.barrierExponent(ii), params.minAlt(ii), params.timestep(ii), params.maxIter(ii), tc.obstacles, tc.makePlots, tc.makeVideo);
tc.testClass = tc.testClass.initialize(tc.domain, agents, params.barrierGain(ii), params.barrierExponent(ii), params.minAlt(ii), params.timestep(ii), params.maxIter(ii), obstacles, tc.makePlots, tc.makeVideo);
% Save simulation parameters to output file
tc.testClass.writeParams();
@@ -148,7 +202,19 @@ classdef parametricTestSuite < matlab.unittest.TestCase
% Cleanup
tc.testClass = tc.testClass.teardown();
end
end
end
methods
function c = obstacleCollisionCheck(~, obstacles, obstacle)
% Check if the obstacle intersects with any other obstacles
c = false;
for ii = 1:size(obstacles, 1)
if geometryIntersects(obstacles{ii}, obstacle)
c = true;
return;
end
end
end
end
end

6
test/testIterations.csv
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@@ -1,3 +1,3 @@
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"
timestep, maxIter, minAlt, discretizationStep, protectedRange, sensorPerformanceMinimum, initialStepSize, barrierGain, barrierExponent, numObstacles, numAgents, collisionRadius, comRange, alphaDist, betaDist, alphaTilt, betaTilt
1, 10, 1, 0.01, 1, 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, 1, 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 protectedRange sensorPerformanceMinimum initialStepSize barrierGain barrierExponent numObstacles numAgents collisionRadius comRange alphaDist betaDist alphaTilt betaTilt
2 1 10 1 0.01 1 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 1 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