263 lines
11 KiB
Matlab
263 lines
11 KiB
Matlab
classdef test_miSim < matlab.unittest.TestCase
|
|
properties (Access = private)
|
|
testClass = miSim;
|
|
|
|
% Domain
|
|
domain = rectangularPrism; % domain geometry
|
|
|
|
% Obstacles
|
|
minNumObstacles = 1; % Minimum number of obstacles to be randomly generated
|
|
maxNumObstacles = 3; % Maximum number of obstacles to be randomly generated
|
|
minObstacleSize = 1; % Minimum size of a randomly generated obstacle
|
|
maxObstacleSize = 6; % Maximum size of a randomly generated obstacle
|
|
obstacles = cell(1, 0);
|
|
|
|
% Objective
|
|
objectiveDiscretizationStep = 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;
|
|
|
|
% Agents
|
|
minAgents = 3; % Minimum number of agents to be randomly generated
|
|
maxAgents = 9; % Maximum number of agents to be randomly generated
|
|
agents = cell(0, 1);
|
|
|
|
% Collision
|
|
minCollisionRange = 0.1; % Minimum randomly generated collision geometry size
|
|
maxCollisionRange = 0.5; % Maximum randomly generated collision geometry size
|
|
collisionRanges = NaN;
|
|
|
|
% Communications
|
|
comRange = 5; % Maximum range between agents that forms a communications link
|
|
end
|
|
|
|
% Setup for each test
|
|
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.interiorDistance(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);
|
|
end
|
|
% Instantiate agents
|
|
function tc = setAgents(tc)
|
|
% Agents will be initialized under different parameters in
|
|
% individual test cases
|
|
|
|
% Instantiate a random number of agents according to parameters
|
|
for ii = 1:randi([tc.minAgents, tc.maxAgents])
|
|
tc.agents{ii, 1} = agent;
|
|
end
|
|
|
|
% Define random collision ranges for each agent
|
|
tc.collisionRanges = tc.minCollisionRange + rand(size(tc.agents, 1), 1) * (tc.maxCollisionRange - tc.minCollisionRange);
|
|
end
|
|
end
|
|
|
|
methods (Test)
|
|
% Test methods
|
|
function misim_initialization(tc)
|
|
% randomly create 2-3 obstacles
|
|
nGeom = tc.minNumObstacles + randi(tc.maxNumObstacles - tc.minNumObstacles);
|
|
tc.obstacles = cell(nGeom, 1);
|
|
|
|
% Iterate over obstacles to initialize
|
|
for ii = 1:size(tc.obstacles, 1)
|
|
badCandidate = true;
|
|
while badCandidate
|
|
% Instantiate a rectangular prism obstacle
|
|
tc.obstacles{ii} = rectangularPrism;
|
|
|
|
% Randomly generate min corner for the obstacle
|
|
candidateMinCorner = tc.domain.random();
|
|
candidateMinCorner = [candidateMinCorner(1:2), 0]; % bind obstacles to floor of domain
|
|
|
|
% Randomly select a corresponding maximum corner that
|
|
% satisfies min/max obstacle size specifications
|
|
candidateMaxCorner = candidateMinCorner + tc.minObstacleSize + rand(1, 3) * (tc.maxObstacleSize - tc.minObstacleSize);
|
|
|
|
% Initialize obstacle
|
|
tc.obstacles{ii} = tc.obstacles{ii}.initialize([candidateMinCorner; candidateMaxCorner], REGION_TYPE.OBSTACLE, sprintf("Column obstacle %d", ii));
|
|
|
|
% Check if the obstacle intersects with any existing
|
|
% obstacles
|
|
violation = false;
|
|
for kk = 1:(ii - 1)
|
|
if geometryIntersects(tc.obstacles{kk}, tc.obstacles{ii})
|
|
violation = true;
|
|
break;
|
|
end
|
|
end
|
|
if violation
|
|
continue;
|
|
end
|
|
|
|
% Make sure that the obstacles are fully contained by
|
|
% the domain
|
|
if ~domainContainsObstacle(tc.domain, tc.obstacles{ii})
|
|
continue;
|
|
end
|
|
|
|
% Make sure that the obstacles don't cover the sensing
|
|
% objective
|
|
if obstacleCoversObjective(tc.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)
|
|
continue;
|
|
end
|
|
|
|
badCandidate = false;
|
|
end
|
|
end
|
|
|
|
% Add agents individually, ensuring that each addition does not
|
|
% invalidate the initialization setup
|
|
for ii = 1:size(tc.agents, 1)
|
|
initInvalid = true;
|
|
while initInvalid
|
|
candidatePos = [tc.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)
|
|
candidatePos = tc.domain.random();
|
|
end
|
|
else
|
|
candidatePos = tc.agents{randi(ii - 1)}.pos + sign(randn([1, 3])) .* (rand(1, 3) .* tc.comRange/sqrt(2));
|
|
end
|
|
|
|
% Make sure that the candidate position is within the
|
|
% domain
|
|
if ~tc.domain.contains(candidatePos)
|
|
continue;
|
|
end
|
|
|
|
% 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)
|
|
continue;
|
|
end
|
|
|
|
% Make sure that there exist unobstructed lines of sight at
|
|
% appropriate ranges to form a connected communications
|
|
% graph between the agents
|
|
connections = false(1, ii - 1);
|
|
for jj = 1:(ii - 1)
|
|
if norm(tc.agents{jj}.pos - candidatePos) <= tc.comRange
|
|
% Check new agent position against all existing
|
|
% agent positions for communications range
|
|
connections(jj) = true;
|
|
for kk = 1:size(tc.obstacles, 1)
|
|
if tc.obstacles{kk}.containsLine(tc.agents{jj}.pos, candidatePos)
|
|
connections(jj) = false;
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
% New agent must be connected to an existing agent to
|
|
% be valid
|
|
if ii ~= 1 && ~any(connections)
|
|
continue;
|
|
end
|
|
|
|
% Initialize candidate agent
|
|
candidateGeometry = rectangularPrism;
|
|
newAgent = tc.agents{ii}.initialize(candidatePos, zeros(1,3), eye(3),candidateGeometry.initialize([candidatePos - tc.collisionRanges(ii) * ones(1, 3); candidatePos + tc.collisionRanges(ii) * ones(1, 3)], REGION_TYPE.COLLISION, sprintf("Agent %d collision volume", ii)), tc.comRange, ii, sprintf("Agent %d", ii));
|
|
|
|
% Make sure candidate agent doesn't collide with
|
|
% domain
|
|
violation = false;
|
|
for jj = 1:size(newAgent.collisionGeometry.vertices, 1)
|
|
% Check if collision geometry exits domain
|
|
if ~tc.domain.contains(newAgent.collisionGeometry.vertices(jj, 1:3))
|
|
violation = true;
|
|
break;
|
|
end
|
|
end
|
|
if violation
|
|
continue;
|
|
end
|
|
|
|
% Make sure candidate doesn't collide with obstacles
|
|
violation = false;
|
|
for kk = 1:size(tc.obstacles, 1)
|
|
if geometryIntersects(tc.obstacles{kk}, newAgent.collisionGeometry)
|
|
violation = true;
|
|
break;
|
|
end
|
|
end
|
|
if violation
|
|
continue;
|
|
end
|
|
|
|
% Make sure candidate doesn't collide with existing
|
|
% agents
|
|
violation = false;
|
|
for kk = 1:(ii - 1)
|
|
if geometryIntersects(tc.agents{kk}.collisionGeometry, newAgent.collisionGeometry)
|
|
violation = true;
|
|
break;
|
|
end
|
|
end
|
|
if violation
|
|
continue;
|
|
end
|
|
|
|
% Candidate agent is valid, store to pass in to sim
|
|
initInvalid = false;
|
|
tc.agents{ii} = newAgent;
|
|
end
|
|
end
|
|
|
|
% Initialize the simulation
|
|
tc.testClass = tc.testClass.initialize(tc.domain, tc.objective, tc.agents, tc.obstacles);
|
|
|
|
% Plot domain
|
|
f = tc.testClass.domain.plotWireframe;
|
|
|
|
% Set plotting limits to focus on the domain
|
|
xlim([tc.testClass.domain.minCorner(1), tc.testClass.domain.maxCorner(1)]);
|
|
ylim([tc.testClass.domain.minCorner(2), tc.testClass.domain.maxCorner(2)]);
|
|
zlim([tc.testClass.domain.minCorner(3), tc.testClass.domain.maxCorner(3)]);
|
|
|
|
% Plot obstacles
|
|
for ii = 1:size(tc.testClass.obstacles, 1)
|
|
tc.testClass.obstacles{ii}.plotWireframe(f);
|
|
end
|
|
|
|
% Plot objective gradient
|
|
f = tc.testClass.objective.plot(f);
|
|
|
|
% Plot agents and their collision geometries
|
|
for ii = 1:size(tc.testClass.agents, 1)
|
|
f = tc.testClass.agents{ii}.plot(f);
|
|
f = tc.testClass.agents{ii}.collisionGeometry.plotWireframe(f);
|
|
end
|
|
|
|
% Plot communication links
|
|
f = tc.testClass.plotNetwork(f);
|
|
end
|
|
end
|
|
end |