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

refactored sensing objective into domain, random inits

Browse Source
This commit is contained in:
Kevin D
2025-11-15 16:01:18 -08:00
parent e0f365b21b
commit afa5d79c1d
13 changed files with 110 additions and 51 deletions
Download Patch File Download Diff File Expand all files Collapse all files

61
test/test_miSim.m
View File

@@ -2,12 +2,15 @@ classdef test_miSim < matlab.unittest.TestCase
properties (Access = private)
testClass = miSim;
% Domain
domain = rectangularPrism; % domain geometry
% Sim
maxIter = 250;
timestep = 0.05
partitoningFreq = 5;
% Domain
domain = rectangularPrism; % domain geometry
minDimension = 10;
% Obstacles
minNumObstacles = 1; % Minimum number of obstacles to be randomly generated
maxNumObstacles = 3; % Maximum number of obstacles to be randomly generated
@@ -16,7 +19,7 @@ classdef test_miSim < matlab.unittest.TestCase
obstacles = cell(1, 0);
% Objective
objectiveDiscretizationStep = 0.01; % Step at which the objective function is solved in X and Y space
discretizationStep = 0.01; % Step at which the objective function is solved in X and Y space
protectedRange = 1; % Minimum distance between the sensing objective and the edge of the domain
objective = sensingObjective;
@@ -39,26 +42,10 @@ classdef test_miSim < matlab.unittest.TestCase
methods (TestMethodSetup)
% Generate a random domain
function tc = setDomain(tc)
% random integer-sized cube domain ranging from [0, 5 -> 25]
% in all dimensions
L = ceil(5 + rand * 10 + rand * 10);
tc.domain = tc.domain.initialize([zeros(1, 3); L * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
end
% Generate a random sensing objective within that domain
function tc = setSensingObjective(tc)
% Using a bivariate normal distribution
% Set peak position (mean)
mu = tc.domain.minCorner;
while tc.domain.distance(mu) < tc.protectedRange
mu = tc.domain.random();
end
mu(3) = 0;
% Set standard deviations of bivariate distribution
sig = [2 + rand * 2, 1; 1, 2 + rand * 2];
% Define objective
tc.objective = tc.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], mu(1:2), sig), tc.domain.footprint, tc.domain.minCorner(3), tc.objectiveDiscretizationStep);
% random integer-dimensioned cubic domain
tc.domain = tc.domain.initializeRandom(tc.minDimension, REGION_TYPE.DOMAIN, "Domain");
% Random bivariate normal PDF objective
tc.domain.objective = tc.domain.objective.initializeRandomMvnpdf(tc.domain, tc.protectedRange, tc.discretizationStep);
end
% Instantiate agents
function tc = setAgents(tc)
@@ -121,13 +108,13 @@ classdef test_miSim < matlab.unittest.TestCase
% Make sure that the obstacles don't cover the sensing
% objective
if obstacleCoversObjective(tc.objective, tc.obstacles{ii})
if obstacleCoversObjective(tc.domain.objective, tc.obstacles{ii})
continue;
end
% Make sure that the obstacles aren't too close to the
% sensing objective
if obstacleCrowdsObjective(tc.objective, tc.obstacles{ii}, tc.protectedRange)
if obstacleCrowdsObjective(tc.domain.objective, tc.obstacles{ii}, tc.protectedRange)
continue;
end
@@ -140,11 +127,11 @@ classdef test_miSim < matlab.unittest.TestCase
for ii = 1:size(tc.agents, 1)
initInvalid = true;
while initInvalid
candidatePos = [tc.objective.groundPos, 0];
candidatePos = [tc.domain.objective.groundPos, 0];
% Generate a random position for the agent based on
% existing agent positions
if ii == 1
while agentsCrowdObjective(tc.objective, candidatePos, mean(tc.domain.dimensions) / 2)
while agentsCrowdObjective(tc.domain.objective, candidatePos, mean(tc.domain.dimensions) / 2)
candidatePos = tc.domain.random();
end
else
@@ -159,7 +146,7 @@ classdef test_miSim < matlab.unittest.TestCase
% Make sure that the candidate position does not crowd
% the sensing objective and create boring scenarios
if agentsCrowdObjective(tc.objective, candidatePos, mean(tc.domain.dimensions) / 2)
if agentsCrowdObjective(tc.domain.objective, candidatePos, mean(tc.domain.dimensions) / 2)
continue;
end
@@ -243,7 +230,7 @@ classdef test_miSim < matlab.unittest.TestCase
end
% Initialize the simulation
[tc.testClass, f] = tc.testClass.initialize(tc.domain, tc.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter, tc.obstacles);
[tc.testClass, f] = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter, tc.obstacles);
end
function misim_run(tc)
% randomly create obstacles
@@ -289,13 +276,13 @@ classdef test_miSim < matlab.unittest.TestCase
% Make sure that the obstacles don't cover the sensing
% objective
if obstacleCoversObjective(tc.objective, tc.obstacles{ii})
if obstacleCoversObjective(tc.domain.objective, tc.obstacles{ii})
continue;
end
% Make sure that the obstacles aren't too close to the
% sensing objective
if obstacleCrowdsObjective(tc.objective, tc.obstacles{ii}, tc.protectedRange)
if obstacleCrowdsObjective(tc.domain.objective, tc.obstacles{ii}, tc.protectedRange)
continue;
end
@@ -308,11 +295,11 @@ classdef test_miSim < matlab.unittest.TestCase
for ii = 1:size(tc.agents, 1)
initInvalid = true;
while initInvalid
candidatePos = [tc.objective.groundPos, 0];
candidatePos = [tc.domain.objective.groundPos, 0];
% Generate a random position for the agent based on
% existing agent positions
if ii == 1
while agentsCrowdObjective(tc.objective, candidatePos, mean(tc.domain.dimensions) / 2)
while agentsCrowdObjective(tc.domain.objective, candidatePos, mean(tc.domain.dimensions) / 2)
candidatePos = tc.domain.random();
end
else
@@ -327,7 +314,7 @@ classdef test_miSim < matlab.unittest.TestCase
% Make sure that the candidate position does not crowd
% the sensing objective and create boring scenarios
if agentsCrowdObjective(tc.objective, candidatePos, mean(tc.domain.dimensions) / 2)
if agentsCrowdObjective(tc.domain.objective, candidatePos, mean(tc.domain.dimensions) / 2)
continue;
end
@@ -411,7 +398,7 @@ classdef test_miSim < matlab.unittest.TestCase
end
% Initialize the simulation
[tc.testClass, f] = tc.testClass.initialize(tc.domain, tc.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter, tc.obstacles);
[tc.testClass, f] = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter, tc.obstacles);
% Run simulation loop
[tc.testClass, f] = tc.testClass.run(f);
@@ -424,7 +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.objective = tc.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2), eye(2)), tc.domain.footprint, tc.domain.minCorner(3), tc.objectiveDiscretizationStep);
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);
% Initialize agent collision geometry
geometry1 = rectangularPrism;
@@ -442,7 +429,7 @@ classdef test_miSim < matlab.unittest.TestCase
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));
% Initialize the simulation
[tc.testClass, f] = tc.testClass.initialize(tc.domain, tc.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter);
[tc.testClass, f] = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.timestep, tc.partitoningFreq, tc.maxIter);
end
end
end