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added basic obstacle avoidance test case

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Kevin Dee
2025-12-23 12:37:53 -08:00
parent 6632c9885d
commit 4fa942564a
1 changed files with 43 additions and 9 deletions
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52
test/test_miSim.m
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@@ -386,7 +386,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.agents{3} = tc.agents{3}.initialize(tc.domain.center + dh - [0, d, 0], zeros(1, 3), 0, 0, geometry3, sensor, @gradientAscent, 3*d, 3, sprintf("Agent %d", 3)); tc.agents{3} = tc.agents{3}.initialize(tc.domain.center + dh - [0, d, 0], zeros(1, 3), 0, 0, geometry3, sensor, @gradientAscent, 3*d, 3, sprintf("Agent %d", 3));
% Initialize the simulation % Initialize the simulation
tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.partitoningFreq, tc.maxIter, tc.makeVideo); tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.partitoningFreq, tc.maxIter, cell(0, 1), tc.makeVideo);
close(tc.testClass.fPerf); close(tc.testClass.fPerf);
end end
function test_single_partition(tc) function test_single_partition(tc)
@@ -416,7 +416,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.agents{1} = tc.agents{1}.initialize([tc.domain.center(1:2), 3], zeros(1,3), 0, 0, geometry1, sensor, @gradientAscent, 3, 1, sprintf("Agent %d", 1)); tc.agents{1} = tc.agents{1}.initialize([tc.domain.center(1:2), 3], zeros(1,3), 0, 0, geometry1, sensor, @gradientAscent, 3, 1, sprintf("Agent %d", 1));
% Initialize the simulation % Initialize the simulation
tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.partitoningFreq, tc.maxIter, tc.makeVideo); tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.partitoningFreq, tc.maxIter, cell(0, 1), tc.makeVideo);
close(tc.testClass.fPerf); close(tc.testClass.fPerf);
end end
function test_single_partition_basic_GA(tc) function test_single_partition_basic_GA(tc)
@@ -446,7 +446,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.agents{1} = tc.agents{1}.initialize([tc.domain.center(1:2)-tc.domain.dimensions(1)/3, 3], zeros(1,3), 0, 0, geometry1, sensor, @gradientAscent, 3, 1, sprintf("Agent %d", 1), true); tc.agents{1} = tc.agents{1}.initialize([tc.domain.center(1:2)-tc.domain.dimensions(1)/3, 3], zeros(1,3), 0, 0, geometry1, sensor, @gradientAscent, 3, 1, sprintf("Agent %d", 1), true);
% Initialize the simulation % Initialize the simulation
tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.partitoningFreq, tc.maxIter, tc.makeVideo); tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.partitoningFreq, tc.maxIter, cell(0, 1), tc.makeVideo);
% Run the simulation % Run the simulation
tc.testClass.run(); tc.testClass.run();
@@ -477,16 +477,50 @@ classdef test_miSim < matlab.unittest.TestCase
alphaDist = l/2; % half of domain length/width alphaDist = l/2; % half of domain length/width
sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 15, 3); sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 15, 3);
% Plot sensor parameters (optional)
f = sensor.plotParameters();
% Initialize agents % Initialize agents
tc.agents = {agent; agent}; tc.agents = {agent; agent};
tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + d, zeros(1,3), 0, 0, geometry1, sensor, @gradientAscent, 3, 1, sprintf("Agent %d", 1)); tc.agents{1} = tc.agents{1}.initialize(tc.domain.center + d, zeros(1,3), 0, 0, geometry1, sensor, @gradientAscent, 3, 1, sprintf("Agent %d", 1), false);
tc.agents{2} = tc.agents{2}.initialize(tc.domain.center - d, zeros(1,3), 0, 0, geometry2, sensor, @gradientAscent, 3, 2, sprintf("Agent %d", 2)); tc.agents{2} = tc.agents{2}.initialize(tc.domain.center - d, zeros(1,3), 0, 0, geometry2, sensor, @gradientAscent, 3, 2, sprintf("Agent %d", 2), false);
% Initialize the simulation % Initialize the simulation
tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.partitoningFreq, tc.maxIter, tc.makeVideo); tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.partitoningFreq, cell(0, 1), tc.maxIter, tc.makeVideo);
% Run the simulation
tc.testClass.run();
end
function test_obstacle_avoidance(tc)
% Fixed single obstacle
% Fixed single agent initial conditions
% Exaggerated large collision geometries
% make basic domain
l = 10; % domain size
tc.domain = tc.domain.initialize([zeros(1, 3); l * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
% make basic sensing objective
tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], [8, 5]), tc.domain, tc.discretizationStep, tc.protectedRange);
% Initialize agent collision geometry
radius = 1.5;
d = [3, 0, 0];
geometry1 = spherical;
geometry1 = geometry1.initialize(tc.domain.center - d, radius, REGION_TYPE.COLLISION, sprintf("Agent %d collision volume", 1));
% Initialize agent sensor model
sensor = sigmoidSensor;
alphaDist = l/2; % half of domain length/width
sensor = sensor.initialize(alphaDist, 3, NaN, NaN, 15, 3);
% Initialize agents
tc.agents = {agent};
tc.agents{1} = tc.agents{1}.initialize(tc.domain.center - d, zeros(1,3), 0, 0, geometry1, sensor, @gradientAscent, 3, 1, sprintf("Agent %d", 1), false);
% Initialize obstacles
obstacleLength = 1;
tc.obstacles{1} = rectangularPrism;
tc.obstacles{1} = tc.obstacles{1}.initialize([tc.domain.center(1:2) - obstacleLength, 0; tc.domain.center(1:2) + obstacleLength, tc.domain.maxCorner(3)], REGION_TYPE.OBSTACLE, "Obstacle 1");
% Initialize the simulation
tc.testClass = tc.testClass.initialize(tc.domain, tc.domain.objective, tc.agents, tc.minAlt, tc.timestep, tc.partitoningFreq, tc.maxIter, tc.obstacles, tc.makeVideo);
% Run the simulation % Run the simulation
tc.testClass.run(); tc.testClass.run();