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base: kdee/miSim:789aecc560d35d2df1a7dc7ddb12d016cbe22806
Branches Tags
kdee/miSim:main kdee/miSim:AERPAW-experiments kdee/miSim:spawc-2026-results kdee/miSim:aerpaw2 kdee/miSim:parametric-testing kdee/miSim:gradient-ascent kdee/miSim:more-cleanup kdee/miSim:tessellation-fixes kdee/miSim:sensor-model-tessellation
..
compare: kdee/miSim:spawc-2026-results
Branches Tags
kdee/miSim:AERPAW-experiments kdee/miSim:main kdee/miSim:spawc-2026-results kdee/miSim:aerpaw2 kdee/miSim:parametric-testing kdee/miSim:gradient-ascent kdee/miSim:more-cleanup kdee/miSim:tessellation-fixes kdee/miSim:sensor-model-tessellation

10 Commits
789aecc560 .. spawc-2026-results

Author SHA1 Message Date
kdee b753f05d77 organizing 2026-03-18 16:09:00 -07:00
kdee 2ca0c286cd last plot updates 2026-03-18 16:05:23 -07:00
kdee f23675a54c results 2026-03-17 22:15:42 -07:00
kdee 8c3b853895 plot1 for multiple trials 2026-03-17 12:21:14 -07:00
kdee e77b05bc0f plots 3 and 4 2026-03-16 19:22:31 -07:00
kdee 6b74347411 started plot3 work 2026-03-16 16:19:38 -07:00
kdee a3837a6ef4 second attempt at plot1 2026-03-16 14:35:52 -07:00
kdee 01f2af9102 added second plot - pairwise distances 2026-03-15 17:43:45 -07:00
kdee 0d02e5d1f5 plot1 kinda works 2026-03-15 15:15:48 -07:00
kdee 2a0e2e500f results plot1 WIP 2026-03-15 13:42:35 -07:00
87 changed files with 937 additions and 620 deletions
Expand all files Collapse all files
@miSim/constrainMotion.m
+10 -14
View File
@@ -61,9 +61,7 @@ function [obj] = constrainMotion(obj)
end
idx = length(h(triu(true(size(h)), 1)));
if coder.target('MATLAB')
obj.barriers(1:idx, obj.timestepIndex) = h(triu(true(size(h)), 1));
end
obj.barriers(1:idx, obj.timestepIndex) = h(triu(true(size(h)), 1));
idx = idx + 1;
hObs = NaN(nAgents, size(obj.obstacles, 1));
@@ -86,9 +84,7 @@ function [obj] = constrainMotion(obj)
end
end
if coder.target('MATLAB')
obj.barriers(idx:(idx + numel(hObs) - 1), obj.timestepIndex) = reshape(hObs, [], 1);
end
obj.barriers(idx:(idx + numel(hObs) - 1), obj.timestepIndex) = reshape(hObs, [], 1);
idx = idx + numel(hObs);
% Set up domain constraints (walls and ceiling only)
@@ -132,10 +128,13 @@ function [obj] = constrainMotion(obj)
b(kk) = obj.barrierGain * max(0, h_zMax)^obj.barrierExponent;
kk = kk + 1;
if coder.target('MATLAB')
obj.barriers(idx:(idx + 5), obj.timestepIndex) = [h_xMin; h_xMax; h_yMin; h_yMax; h_zMin; h_zMax];
end
idx = idx + 6;
obj.barriers(idx:(idx + 5), obj.timestepIndex) = [h_xMin; h_xMax; h_yMin; h_yMax; h_zMin; h_zMax];
idx = idx + 6;
end
if coder.target('MATLAB')
% Save off h function values (logging only — not needed in compiled mode)
obj.h(:, obj.timestepIndex) = [h(triu(true(nAgents), 1)); reshape(hObs, [], 1); h_xMin; h_xMax; h_yMin; h_yMax; h_zMin; h_zMax;];
end
% Add communication network constraints
@@ -165,10 +164,7 @@ function [obj] = constrainMotion(obj)
end
end
end
if coder.target('MATLAB')
obj.barriers(idx:(idx + length(hComms(triu(true(size(hComms)), 1))) - 1), obj.timestepIndex) = hComms(triu(true(size(hComms)), 1));
end
obj.barriers(idx:(idx + length(hComms(triu(true(size(hComms)), 1))) - 1), obj.timestepIndex) = hComms(triu(true(size(hComms)), 1));
% Double-integrator: transform QP from velocity to acceleration space.
% Single-integrator constraint: A * v <= b
@miSim/initialize.m
+2 -1
View File
@@ -109,6 +109,8 @@ function [obj] = initialize(obj, domain, agents, barrierGain, barrierExponent, m
% Prepare performance data store (at t = 0, all have 0 performance)
obj.perf = [zeros(size(obj.agents, 1) + 1, 1), NaN(size(obj.agents, 1) + 1, size(obj.partitioningTimes, 1) - 1)];
% Prepare h function data store
obj.h = NaN(size(obj.agents, 1) * (size(obj.agents, 1) - 1) / 2 + size(obj.agents, 1) * size(obj.obstacles, 1) + 6, size(obj.times, 1));
end
% Create initial partitioning
@@ -141,7 +143,6 @@ function [obj] = initialize(obj, domain, agents, barrierGain, barrierExponent, m
obj.constraintAdjacencyHist = false(nAgents, nAgents, size(obj.times, 1));
obj.constraintAdjacencyHist(:, :, 1) = obj.constraintAdjacencyMatrix;
% Set up plots showing initialized state
obj = obj.plot();
@miSim/initializeFromInits.m
-87
View File
@@ -1,87 +0,0 @@
function obj = initializeFromInits(obj, initsPath)
% INITIALIZEFROMINITS Initialize miSim from a saved simInits matfile.
%
% Loads all simulation parameters and initial agent states written by
% writeInits(), reconstructs domain, objective, agents, and obstacles, then
% calls the standard obj.initialize() method. Plots and video are disabled.
%
% Usage:
% sim = sim.initializeFromInits('sandbox/2025_01_01_12_00_00_miSimInits.mat');
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
initsPath (1, 1) string;
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
inits = load(initsPath);
% ---- Build domain ------------------------------------------------------------
dom = rectangularPrism;
dom = dom.initialize([inits.domainMin; inits.domainMax], REGION_TYPE.DOMAIN, "Domain");
% ---- Build sensing objective -------------------------------------------------
dom.objective = sensingObjective;
% reshape guards against MATLAB flattening the 1×2×2 singleton dimension on load
objSigma = reshape(inits.objectiveSigma, [1 2 2]);
objFcn = objectiveFunctionWrapper(inits.objectivePos, objSigma);
dom.objective = dom.objective.initialize(objFcn, dom, ...
inits.discretizationStep, inits.protectedRange, inits.sensorPerformanceMinimum, ...
inits.objectivePos, objSigma);
% ---- Build agents ------------------------------------------------------------
numAgents = inits.numAgents;
agentList = cell(numAgents, 1);
for ii = 1:numAgents
pos = inits.pos(ii, :);
sensor = sigmoidSensor;
sensor = sensor.initialize(inits.alphaDist(ii), inits.betaDist(ii), ...
inits.alphaTilt(ii), inits.betaTilt(ii));
geom = spherical;
geom = geom.initialize(pos, inits.collisionRadius(ii), REGION_TYPE.COLLISION, ...
sprintf("UAV %d Collision", ii));
ag = agent;
ag = ag.initialize(pos, geom, sensor, inits.comRange(ii), inits.maxIter, ...
inits.initialStepSize(ii), sprintf("UAV %d", ii));
agentList{ii} = ag;
end
% ---- Build obstacles ---------------------------------------------------------
numObstacles = inits.numObstacles;
obstacleList = cell(numObstacles, 1);
if numObstacles > 0
for ii = 1:numObstacles
obs = rectangularPrism;
obs = obs.initialize([inits.obsMinCorners(ii, :); inits.obsMaxCorners(ii, :)], ...
REGION_TYPE.OBSTACLE, sprintf("Obstacle %d", ii));
obstacleList{ii} = obs;
end
end
% ---- Optional backward-compat fields -----------------------------------------
if isfield(inits, 'useDoubleIntegrator')
useDoubleIntegrator = logical(inits.useDoubleIntegrator);
else
useDoubleIntegrator = false;
end
if isfield(inits, 'dampingCoeff')
dampingCoeff = inits.dampingCoeff;
else
dampingCoeff = 2.0;
end
if isfield(inits, 'useFixedTopology')
useFixedTopology = logical(inits.useFixedTopology);
else
useFixedTopology = false;
end
% ---- Initialize simulation (plots and video disabled) ------------------------
obj = obj.initialize(dom, agentList, inits.barrierGain, inits.barrierExponent, ...
inits.minAlt, inits.timestep, inits.maxIter, obstacleList, ...
false, false, useDoubleIntegrator, dampingCoeff, useFixedTopology);
end
@miSim/miSim.m
+1 -2
View File
@@ -54,6 +54,7 @@ classdef miSim
partitionGraphIndex = 1;
% CBF plotting
h; % h function values
hf; % h function plotting figure
caPlot; % objects for collision avoidance h function plot
obsPlot; % objects for obstacle h function plot
@@ -71,8 +72,6 @@ classdef miSim
end
[obj] = initialize(obj, domain, agents, barrierGain, barrierExponent, minAlt, timestep, maxIter, obstacles, makePlots, makeVideo);
[obj] = initializeFromCsv(obj, csvPath);
[obj] = initializeFromInits(obj, initsPath);
[obj] = plotFromSimHist(obj, initsPath, histPath);
[obj] = run(obj);
[obj] = lesserNeighbor(obj);
[obj] = constrainMotion(obj);
@miSim/plotFromSimHist.m
-93
View File
@@ -1,93 +0,0 @@
function obj = plotFromSimHist(obj, initsPath, histPath)
% PLOTFROMSIMHIST Reconstruct all three miSim plots from saved matfiles.
%
% Loads the simInits matfile to rebuild domain/obstacle/objective/agent
% geometry, then loads the simHist matfile to restore the full time-history
% arrays. Produces the same three figures that a live run would generate:
% 1. Sensor performance vs. time (obj.fPerf)
% 2. Barrier function values vs. time (obj.hf)
% 3. 3-D spatial figure with domain, obstacles, objective, agent trails,
% and final-timestep communications topology (obj.f)
%
% Usage:
% sim = miSim;
% sim = sim.plotFromSimHist( ...
% 'sandbox/2025_01_01_12_00_00_miSimHist.mat', ...
% 'sandbox/2025_01_01_12_00_00_miSimInits.mat');
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
initsPath (1, 1) string;
histPath (1, 1) string;
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
% ---- Reconstruct geometry from inits (plots disabled) --------------------
obj = obj.initializeFromInits(initsPath);
nAgents = size(obj.agents, 1);
% ---- Load history data ---------------------------------------------------
data = load(histPath);
out = data.out;
nHistTimesteps = size(out.barriers, 2);
nPosTimesteps = size(out.agent(1).pos, 1);
% ---- Populate barrier history --------------------------------------------
% out.barriers may be narrower than the pre-allocated obj.barriers if the
% run was shorter than maxIter; fill what we have and leave the rest NaN.
obj.barriers(:, 1:nHistTimesteps) = out.barriers;
% ---- Populate position history and advance agents to final positions -----
for ii = 1:nAgents
agentPos = out.agent(ii).pos; % (nPosTimesteps × 3)
nPts = size(agentPos, 1);
obj.posHist(ii, 1:nPts, :) = reshape(agentPos, [1, nPts, 3]);
obj.agents{ii}.pos = agentPos(end, :); % show final position in spatial plot
end
% ---- Set final constraint topology ---------------------------------------
obj.constraintAdjacencyMatrix = out.constraintAdjacency(:, :, end);
% ---- Recompute partitioning at final agent positions ---------------------
obj.partitioning = obj.agents{1}.partition(obj.agents, obj.domain.objective);
% ---- Enable plotting and produce spatial + barrier figures ---------------
obj.makePlots = true;
obj = obj.plot();
% ---- Performance figure (built directly — live machinery is incremental) -
nPerfTimesteps = numel(out.perf);
times = (0:nPerfTimesteps - 1) * obj.timestep;
normFactor = 1 / max(out.perf);
obj.fPerf = figure;
ax = axes(obj.fPerf);
hold(ax, "on");
title(ax, "Sensor Performance");
xlabel(ax, "Time (s)");
ylabel(ax, "Sensor Performance");
grid(ax, "on");
legendStrings = strings(nAgents + 1, 1);
legendStrings(1) = "Total";
plot(ax, times, out.perf * normFactor, "LineWidth", 1.5);
for ii = 1:nAgents
agentPerf = out.agent(ii).perf;
agentTimes = times(1:numel(agentPerf));
plot(ax, agentTimes, agentPerf * normFactor);
if isfield(out.agent(ii), 'label')
legendStrings(ii + 1) = string(out.agent(ii).label);
else
legendStrings(ii + 1) = sprintf("Agent %d", ii);
end
end
legend(ax, legendStrings, "Location", "northwest");
hold(ax, "off");
% Bring spatial figure to the front
figure(obj.f);
end
@miSim/plotH.m
+4 -13
View File
@@ -6,10 +6,6 @@ function obj = plotH(obj)
obj (1, 1) {mustBeA(obj, "miSim")};
end
nCA = size(obj.agents, 1) * (size(obj.agents, 1) - 1) / 2;
nObs = size(obj.agents, 1) * size(obj.obstacles, 1);
nDom = size(obj.agents, 1) * 6;
obj.hf = figure;
tiledlayout(obj.hf, 4, 1, "TileSpacing", "tight", "Padding", "compact");
@@ -19,7 +15,7 @@ function obj = plotH(obj)
xlabel(obj.hf.Children(1).Children(1), "Time (s)");
title(obj.hf.Children(1).Children(1), "Collision Avoidance");
hold(obj.hf.Children(1).Children(1), "on");
obj.caPlot = plot(obj.barriers(1:nCA, :)');
obj.caPlot = plot(obj.h(1:(size(obj.agents, 1) * (size(obj.agents, 1) - 1) / 2), :)');
legendStrings = [];
for ii = 2:size(obj.agents, 1)
for jj = 1:(ii - 1)
@@ -35,7 +31,7 @@ function obj = plotH(obj)
xlabel(obj.hf.Children(1).Children(1), "Time (s)");
title(obj.hf.Children(1).Children(1), "Obstacles");
hold(obj.hf.Children(1).Children(1), "on");
obj.obsPlot = plot(obj.barriers((nCA + 1):(nCA + nObs), :)');
obj.obsPlot = plot(obj.h((1 + (size(obj.agents, 1) * (size(obj.agents, 1) - 1) / 2)):(((size(obj.agents, 1) * (size(obj.agents, 1) - 1) / 2)) + size(obj.agents, 1) * size(obj.obstacles, 1)), :)');
legendStrings = [];
for ii = 1:size(obj.obstacles, 1)
for jj = 1:size(obj.agents, 1)
@@ -51,13 +47,8 @@ function obj = plotH(obj)
xlabel(obj.hf.Children(1).Children(1), "Time (s)");
title(obj.hf.Children(1).Children(1), "Domain");
hold(obj.hf.Children(1).Children(1), "on");
obj.domPlot = plot(obj.barriers((nCA + nObs + 1):(nCA + nObs + nDom), :)');
domLabels = ["X Min", "X Max", "Y Min", "Y Max", "Z Min", "Z Max"];
legendStrings = strings(nDom, 1);
for ii = 1:size(obj.agents, 1)
legendStrings((ii - 1) * 6 + (1:6)) = sprintf("A%d ", ii) + domLabels;
end
legend(obj.hf.Children(1).Children(1), legendStrings, "Location", "bestoutside");
obj.domPlot = plot(obj.h((1 + (((size(obj.agents, 1) * (size(obj.agents, 1) - 1) / 2)) + size(obj.agents, 1) * size(obj.obstacles, 1))):size(obj.h, 1), 1:end)');
legend(obj.hf.Children(1).Children(1), ["X Min"; "X Max"; "Y Min"; "Y Max"; "Z Min"; "Z Max";], "Location", "bestoutside");
hold(obj.hf.Children(1).Children(2), "off");
nexttile(obj.hf.Children(1));
@miSim/updatePlots.m
+5 -7
View File
@@ -61,15 +61,13 @@ function [obj] = updatePlots(obj)
end
% Update h function plots
nCA = size(obj.caPlot, 1);
nObs = size(obj.obsPlot, 1);
for ii = 1:nCA
obj.caPlot(ii).YData(obj.timestepIndex) = obj.barriers(ii, obj.timestepIndex);
for ii = 1:size(obj.caPlot, 1)
obj.caPlot(ii).YData(obj.timestepIndex) = obj.h(ii, obj.timestepIndex);
end
for ii = 1:nObs
obj.obsPlot(ii).YData(obj.timestepIndex) = obj.barriers(nCA + ii, obj.timestepIndex);
for ii = 1:size(obj.obsPlot, 1)
obj.obsPlot(ii).YData(obj.timestepIndex) = obj.h(ii + size(obj.caPlot, 1), obj.timestepIndex);
end
for ii = 1:size(obj.domPlot, 1)
obj.domPlot(ii).YData(obj.timestepIndex) = obj.barriers(nCA + nObs + ii, obj.timestepIndex);
obj.domPlot(ii).YData(obj.timestepIndex) = obj.h(ii + size(obj.caPlot, 1) + size(obj.obsPlot, 1), obj.timestepIndex);
end
end
@miSim/writeInits.m
+7 -13
View File
@@ -5,35 +5,29 @@ function writeInits(obj)
arguments (Output)
end
% User-supplied obstacles only: initialize() appends a floor obstacle at
% the end when minAlt > 0, so exclude it here to avoid double-counting on
% reconstruction (initializeFromInits re-adds the floor via minAlt).
numInputObs = size(obj.obstacles, 1) - (obj.minAlt > 0);
userObstacles = obj.obstacles(1:numInputObs);
% Collect agent parameters
collisionRadii = cellfun(@(x) x.collisionGeometry.radius, obj.agents);
alphaDist = cellfun(@(x) x.sensorModel.alphaDist, obj.agents);
betaDist = cellfun(@(x) x.sensorModel.betaDist, obj.agents);
alphaTilt = cellfun(@(x) x.sensorModel.alphaTilt, obj.agents);
betaTilt = cellfun(@(x) x.sensorModel.betaTilt, obj.agents);
betaTilt = cellfun(@(x) x.sensorModel.alphaDist, obj.agents);
comRanges = cellfun(@(x) x.commsGeometry.radius, obj.agents);
initialStepSize = cellfun(@(x) x.initialStepSize, obj.agents);
pos = cell2mat(cellfun(@(x) x.pos, obj.agents, 'UniformOutput', false));
obsMinCorners = cell2mat(cellfun(@(x) x.minCorner, userObstacles, 'UniformOutput', false));
obsMaxCorners = cell2mat(cellfun(@(x) x.maxCorner, userObstacles, 'UniformOutput', false));
obsMinCorners = cell2mat(cellfun(@(x) x.minCorner, obj.obstacles, 'UniformOutput', false));
obsMaxCorners = cell2mat(cellfun(@(x) x.maxCorner, obj.obstacles, 'UniformOutput', false));
% Combine with simulation parameters
inits = struct("timestep", obj.timestep, "maxIter", obj.maxIter + 1, "minAlt", obj.minAlt, ...
inits = struct("timestep", obj.timestep, "maxIter", obj.maxIter, "minAlt", obj.obstacles{end}.maxCorner(3), ...
"discretizationStep", obj.domain.objective.discretizationStep, "protectedRange", obj.domain.objective.protectedRange, ...
"sensorPerformanceMinimum", obj.domain.objective.sensorPerformanceMinimum, "initialStepSize", initialStepSize, ...
"barrierGain", obj.barrierGain, "barrierExponent", obj.barrierExponent, "numObstacles", numInputObs, ...
"barrierGain", obj.barrierGain, "barrierExponent", obj.barrierExponent, "numObstacles", size(obj.obstacles, 1), ...
"numAgents", size(obj.agents, 1), "collisionRadius", collisionRadii, "comRange", comRanges, ...
"useDoubleIntegrator", obj.useDoubleIntegrator, "dampingCoeff", obj.dampingCoeff, "useFixedTopology", obj.useFixedTopology, ...
"useDoubleIntegrator", obj.useDoubleIntegrator, "dampingCoeff", obj.dampingCoeff, ...
"alphaDist", alphaDist, "betaDist", betaDist, "alphaTilt", alphaTilt, "betaTilt", betaTilt, ...
... % ^^^ PARAMETERS ^^^ | vvv STATES vvv
"pos", pos, "objectivePos", obj.domain.objective.groundPos, "objectiveSigma", obj.domain.objective.objectiveSigma, ...
"domainMin", obj.domain.minCorner, "domainMax", obj.domain.maxCorner, ...
"obsMinCorners", obsMinCorners, "obsMaxCorners", obsMaxCorners, ...
"objectiveIntegral", sum(obj.domain.objective.values(:)));
@sensingObjective/initialize.m
+1 -1
View File
@@ -47,5 +47,5 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
end
obj.objectiveSigma = objectiveSigma;
assert(domain.distance([obj.groundPos, ones(size(obj.groundPos, 1), 1) .* domain.center(3)]) > protectedRange, "Domain is crowding the sensing objective");
assert(domain.distance([obj.groundPos, ones(size(obj.groundPos, 1), 1) .* domain.center(3)]) > protectedRange, "Domain is crowding the sensing objective")
end
@sensingObjective/initializeRandomMvnpdf.m
+2 -2
View File
@@ -16,11 +16,11 @@ function obj = initializeRandomMvnpdf(obj, domain, discretizationStep, protected
end
% Set random distribution parameters
sig = reshape([2 + rand * 2, 1; 1, 2 + rand * 2], [1 2 2]);
sig = [2 + rand * 2, 1; 1, 2 + rand * 2];
% Set up random bivariate normal distribution function
objectiveFunction = objectiveFunctionWrapper(mu(1:2), sig);
% Regular initialization
obj = obj.initialize(objectiveFunction, domain, discretizationStep, protectedRange, 1e-6, mu(1:2), sig);
obj = obj.initialize(objectiveFunction, domain, discretizationStep, protectedRange);
end
aerpaw/client/uav_runner.py
+1 -1
View File
@@ -164,7 +164,7 @@ class UAVRunner(BasicRunner):
# Retry connection up to 10 times (~30 seconds total)
reader, writer = None, None
for attempt in range(100):
for attempt in range(10):
try:
reader, writer = await asyncio.wait_for(
asyncio.open_connection(self.server_ip, self.server_port),
aerpaw/config/client1.yaml
+3 -3
View File
@@ -28,11 +28,11 @@ environments:
port: 5000
testbed:
# AERPAW testbed: E-VM listens, MAVLink Filter connects to us (UDP)
# AERPAW testbed: E-VM listens, MAVLink Filter connects TO us (UDP)
mavlink:
ip: "192.168.32.26"
port: 14550
# Controller runs on host machine (192.168.X.1, generally)
# Controller runs on host machine (192.168.109.1 from E-VM perspective)
controller:
ip: "192.168.112.1"
ip: "192.168.109.1"
port: 5000
aerpaw/config/client2.yaml
+3 -3
View File
@@ -28,11 +28,11 @@ environments:
port: 5000
testbed:
# AERPAW testbed: E-VM listens, MAVLink Filter connects to us (UDP)
# AERPAW testbed: E-VM listens, MAVLink Filter connects TO us (UDP)
mavlink:
ip: "192.168.32.26"
port: 14550
# Controller runs on host machine (192.168.X.1, generally)
# Controller runs on host machine (192.168.109.1 from E-VM perspective)
controller:
ip: "192.168.112.1"
ip: "192.168.109.1"
port: 5000
aerpaw/config/scenario.csv
+1 -1
View File
@@ -1,2 +1,2 @@
timestep, maxIter, minAlt, discretizationStep, protectedRange, initialStepSize, barrierGain, barrierExponent, collisionRadius, comRange, alphaDist, betaDist, alphaTilt, betaTilt, domainMin, domainMax, objectivePos, objectiveVar, sensorPerformanceMinimum, initialPositions, numObstacles, obstacleMin, obstacleMax, useDoubleIntegrator, dampingCoeff, useFixedTopology
1, 100, 30.0, 0.1, 2.0, 5, 1, 1, "6.0, 6.0", "25.0, 25.0", "80.0, 80.0", "0.25, 0.25", "5.0, 5.0", "0.1, 0.1", "0.0, 0.0, 0.0", "80.0, 80.0, 80.0", "55.0, 55.0", "40, 25, 25, 40", 0.15, "15.0, 10.0, 40.0, 5.0, 10.0, 45.0", 1, "1.0, 25.0, 0.0", "30.0, 30.0, 50.0", 1, 2.0, 1
1, 150, 30.0, 0.1, 2.0, 1, 1, 1, "5.0, 5.0", "25.0, 25.0", "80.0, 80.0", "0.25, 0.25", "5.0, 5.0", "0.1, 0.1", "0.0, 0.0, 0.0", "80.0, 80.0, 80.0", "55.0, 55.0", "40, 25, 25, 40", 0.15, "15.0, 10.0, 40.0, 5.0, 10.0, 45.0", 1, "1.0, 25.0, 0.0", "30.0, 30.0, 50.0", 1, 2.0, 1
1 timestep maxIter minAlt discretizationStep protectedRange initialStepSize barrierGain barrierExponent collisionRadius comRange alphaDist betaDist alphaTilt betaTilt domainMin domainMax objectivePos objectiveVar sensorPerformanceMinimum initialPositions numObstacles obstacleMin obstacleMax useDoubleIntegrator dampingCoeff useFixedTopology
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@@ -1161,7 +1099,7 @@
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<Timestamp>2026-04-05T13:43:54</Timestamp>
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</MF0>
aerpaw/controller.m
+1 -24
View File
@@ -7,8 +7,7 @@ coder.extrinsic('disp', 'readScenarioCsv');
% Maximum clients supported (one initial position per UAV)
MAX_CLIENTS = 4;
% Two waypoints per UAV: altitude-staggered transit + final position
MAX_TARGETS = MAX_CLIENTS * 2;
MAX_TARGETS = MAX_CLIENTS;
% Allocate targets array (MAX_TARGETS x 3)
targets = zeros(MAX_TARGETS, 3);
@@ -34,28 +33,6 @@ else
numWaypoints = totalLoaded / int32(numClients);
end
% In the compiled path, inject altitude-staggered transit waypoints so UAVs
% are vertically separated while flying horizontally to their start positions.
% ArduPilot reaches target altitude before horizontal movement, so UAV i is at
% altitude (TRANSIT_ALT_BASE + (i-1)*TRANSIT_ALT_STEP) throughout its transit,
% preventing collisions regardless of horizontal path geometry.
% TRANSIT_ALT_STEP must exceed 2 * max(collisionRadius).
% Waypoint 1: each UAV flies to (finalX, finalY) at its unique transit altitude.
% Waypoint 2: each UAV adjusts to its actual target altitude.
if ~coder.target('MATLAB')
TRANSIT_ALT_BASE = 25.0; % must match drone.takeoff() altitude in uav_runner.py
TRANSIT_ALT_STEP = 12.1; % vertical separation per UAV (m); must exceed 2*collisionRadius
for ii = double(totalLoaded):-1:1
transitRow = (ii - 1) * 2 + 1;
finalRow = (ii - 1) * 2 + 2;
finalPos = targets(ii, :);
transitAlt = TRANSIT_ALT_BASE + (ii - 1) * TRANSIT_ALT_STEP;
targets(finalRow, :) = finalPos;
targets(transitRow, :) = [finalPos(1), finalPos(2), transitAlt];
end
numWaypoints = int32(2);
end
% Load guidance scenario from CSV (parameters for guidance_step)
NUM_SCENARIO_PARAMS = 45;
MAX_OBSTACLES_CTRL = int32(8);
aerpaw/radio/updateScripts.sh
-1
View File
@@ -13,4 +13,3 @@ cp ../scripts/startVehicle.sh /root/Profiles/ProfileScripts/Vehicle/.
echo "REMEMBER! Manually edit startexperiment.sh to point to the correct client.yaml"
echo "REMEMBER! Manually copy startexperiment_controller.sh to startexperiment.sh on the fixed node"
echo "REMEMBER! Manually copy startVehicle_controller.sh to ~/Profiles/ProfileScripts/Vehicle/startVehicle.sh on the fixed node"
aerpaw/results/plotGpsLogs.m
+3 -6
View File
@@ -1,8 +1,7 @@
function [f, G] = plotGpsLogs(logDirs, seaToGroundLevel, plotWholeFlight)
function [f, G] = plotGpsLogs(logDirs, seaToGroundLevel)
arguments (Input)
logDirs (1, 1) string;
seaToGroundLevel (1, 1) double = 110; % measured approximately from USGS national map viewer for the AERPAW test field
plotWholeFlight (1, 1) logical = false;
end
arguments (Output)
f (1, 1) matlab.ui.Figure;
@@ -49,10 +48,8 @@ function [f, G] = plotGpsLogs(logDirs, seaToGroundLevel, plotWholeFlight)
stopIdx = find(verticalSpeed <= prctile(verticalSpeed, pctThreshold), 1, "last");
% % Plot whole flight, including setup/cleanup
if plotWholeFlight
startIdx = 1;
stopIdx = length(verticalSpeed);
end
% startIdx = 1;
% stopIdx = length(verticalSpeed);
% Convert LLA trajectory data to ENU for external analysis
% NaN out entries outside the algorithm flight range so they don't plot
aerpaw/results/readRadioLogs.m
+4 -16
View File
@@ -31,26 +31,14 @@ function R = readRadioLogs(logPath)
end
fid = fopen(filepath, 'r');
% Skip header lines: some files have 2 tail-error lines + 1 column
% header ("tx_uav_id,value"), others start with data immediately.
% Read until a line that looks like a data record, then rewind to it.
dataPattern = '^\[\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\.\d+\] [-\d]';
linePos = ftell(fid);
while true
line = fgetl(fid);
if ~ischar(line)
break; % EOF
end
if ~isempty(regexp(line, dataPattern, 'once'))
fseek(fid, linePos, 'bof'); % rewind to start of this line
break;
end
linePos = ftell(fid);
% Skip 3 lines: 2 junk (tail errors) + 1 header (tx_uav_id,value)
for k = 1:3
fgetl(fid);
end
data = textscan(fid, '[%26c] %d,%f');
fclose(fid);
ts = datetime(cellstr(data{1}), 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
ts = datetime(data{1}, 'InputFormat', 'yyyy-MM-dd HH:mm:ss.SSSSSS');
txId = int32(data{2});
val = data{3};
aerpaw/results/resultsAnalysis.m
+2 -3
View File
@@ -3,8 +3,7 @@ resultsPath = fullfile(matlab.project.rootProject().RootFolder, "sandbox", "two_
% Plot GPS logged data and scenario information (domain, objective, obstacles)
seaToGroundLevel = 110; % measured approximately from USGS national map viewer
plotWholeFlight = true; % do not attempt to automatically trim initial and final positioning and landing from flight plot (buggy)
% [fGlobe, G] = plotGpsLogs(resultsPath, seaToGroundLevel, true);
[fGlobe, G] = plotGpsLogs(resultsPath, seaToGroundLevel);
% Plot radio statistics
[fRadio, R] = plotRadioLogs(resultsPath);
@@ -22,7 +21,7 @@ makeVideo = true;
% Define scenario according to CSV specification
domain = rectangularPrism;
domain = domain.initialize([params.domainMin; params.domainMax], REGION_TYPE.DOMAIN, "Domain");
domain.objective = domain.objective.initialize(objectiveFunctionWrapper(params.objectivePos, reshape(params.objectiveVar, [1, 2 2])), domain, params.discretizationStep, params.protectedRange, params.sensorPerformanceMinimum);
domain.objective = domain.objective.initialize(objectiveFunctionWrapper(params.objectivePos, reshape(params.objectiveVar, [2 2])), domain, params.discretizationStep, params.protectedRange, params.sensorPerformanceMinimum);
agents = cell(size(params.initialPositions, 2) / 3, 1);
for ii = 1:size(agents, 1)
plots_1_2.m
+174
View File
@@ -0,0 +1,174 @@
clear;
%% Load data
dataPath = fullfile('.', 'sandbox', 'plot1');
dataFiles = dir(dataPath);
dataFiles = dataFiles(~startsWith({dataFiles.name}, '.'));
simInits = dataFiles(endsWith({dataFiles.name}, 'miSimInits.mat'));
simHists = dataFiles(endsWith({dataFiles.name}, 'miSimHist.mat'));
assert(length(simHists) == length(simInits), "input data availability mismatch");
%% Aggregate run data
nRuns = length(simHists);
Cfinal = NaN(nRuns, 1);
nAgents = NaN(nRuns, 1);
doubleIntegrator = NaN(nRuns, 1);
numObjective = NaN(nRuns, 1);
commsRadius = NaN(nRuns, 1);
collisionRadius = NaN(nRuns, 1);
maxAgents = 6;
alphaDist = NaN(maxAgents, nRuns);
positions = cell(maxAgents, nRuns);
adjacency = cell(nRuns, 1);
for ii = 1:nRuns
initName = strrep(simInits(ii).name, "_miSimInits.mat", "");
histName = strrep(simHists(ii).name, "_miSimHist.mat", "");
assert(initName == histName);
init = load(fullfile(simInits(ii).folder, simInits(ii).name));
hist = load(fullfile(simHists(ii).folder, simHists(ii).name));
Cfinal(ii) = hist.out.perf(end) / init.objectiveIntegral;
nAgents(ii) = init.numAgents;
doubleIntegrator(ii) = init.useDoubleIntegrator;
numObjective(ii) = size(init.objectivePos, 1);
commsRadius(ii) = unique(init.comRange);
collisionRadius(ii) = unique(init.collisionRadius);
adjacency{ii} = hist.out.constraintAdjacency(:, :, 1);
for jj = 1:nAgents(ii)
alphaDist(jj, ii) = hist.out.agent(jj).sensor.alphaDist;
positions{jj, ii} = hist.out.agent(jj).pos;
assert(hist.out.agent(jj).commsRadius == commsRadius(ii));
assert(hist.out.agent(jj).collisionRadius == collisionRadius(ii));
end
end
commsRadius = unique(commsRadius); assert(isscalar(commsRadius));
collisionRadius = unique(collisionRadius); assert(isscalar(collisionRadius));
sensorTypes = flip(unique(alphaDist(1, :)));
nValues = sort(unique(nAgents));
nGroups = length(nValues);
%% Build config labels
baseConfig = strings(nRuns, 1);
for ii = 1:nRuns
s = "";
if numObjective(ii) == 1
s = s + "A";
elseif numObjective(ii) == 2
s = s + "B";
end
if alphaDist(1, ii) == sensorTypes(1)
s = s + "_I";
elseif alphaDist(1, ii) == sensorTypes(2)
s = s + "_II";
end
if ~doubleIntegrator(ii)
s = s + "_alpha";
else
s = s + "_beta";
end
baseConfig(ii) = s;
end
configOrder = unique(baseConfig(nAgents == nValues(1)), 'stable');
nConfigs = length(configOrder);
configLabels = ["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"];
%% Plot 1: Final normalized coverage
close all;
f1 = figure;
x1 = axes;
C_mean = NaN(nGroups, nConfigs);
C_var = NaN(nGroups, nConfigs);
for ii = 1:nGroups
for jj = 1:nConfigs
mask = (nAgents == nValues(ii)) & (baseConfig == configOrder(jj));
C_mean(ii, jj) = mean(Cfinal(mask));
C_var(ii, jj) = var(Cfinal(mask));
end
end
bar(x1, C_mean);
set(x1, 'XTickLabel', string(nValues));
xlabel(x1, "Number of agents");
ylabel(x1, "Final coverage (normalized)");
title(x1, "Final performance of parameterizations");
legend(x1, configLabels, "Interpreter", "latex", "Location", "northwest");
grid(x1, "on");
ylim(x1, [0, 1/2]);
savefig(f1, "plot1.fig");
exportgraphics(f1, "plot1.png");
%% Plot 2: Pairwise agent distances
f2 = figure;
x2 = axes;
% Compute pairwise distances only for connected agents (static topology)
maxPairs = nchoosek(maxAgents, 2);
pairDist = cell(maxPairs, nRuns);
for ii = 1:nRuns
A = adjacency{ii};
pp = 0;
for jj = 1:nAgents(ii)-1
for kk = jj+1:nAgents(ii)
pp = pp + 1;
if A(jj, kk)
pairDist{pp, ii} = vecnorm(positions{jj, ii} - positions{kk, ii}, 2, 2);
end
end
end
end
% Per-run statistics across all pairs and timesteps
meanPairDist = NaN(nRuns, 1);
minPairDist = NaN(nRuns, 1);
maxPairDist = NaN(nRuns, 1);
for ii = 1:nRuns
nPairs = nchoosek(nAgents(ii), 2);
D = vertcat(pairDist{1:nPairs, ii});
meanPairDist(ii) = mean(D, "omitmissing");
minPairDist(ii) = min(D);
maxPairDist(ii) = max(D);
end
% Aggregate across trials per (n, config) group
meanD = NaN(nGroups, nConfigs);
minD = NaN(nGroups, nConfigs);
maxD = NaN(nGroups, nConfigs);
for ii = 1:nGroups
for jj = 1:nConfigs
mask = (nAgents == nValues(ii)) & (baseConfig == configOrder(jj));
meanD(ii, jj) = mean(meanPairDist(mask));
minD(ii, jj) = min(minPairDist(mask));
maxD(ii, jj) = max(maxPairDist(mask));
end
end
% Plot whiskers (min to max) with mean markers
barWidth = 0.8;
groupWidth = barWidth / nConfigs;
hold(x2, 'on');
for jj = 1:nConfigs
xPos = (1:nGroups) + (jj - (nConfigs + 1) / 2) * groupWidth;
errorbar(x2, xPos, meanD(:, jj), meanD(:, jj) - minD(:, jj), maxD(:, jj) - meanD(:, jj), ...
'o', 'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 10);
end
hold(x2, 'off');
set(x2, 'XTick', 1:nGroups, 'XTickLabel', string(nValues));
xlabel(x2, "Number of agents");
ylabel(x2, "Pairwise distance");
title(x2, "Pairwise Agent Distances (min/mean/max)");
legend(x2, configLabels, "Interpreter", "latex");
grid(x2, "on");
yline(x2, collisionRadius, 'r--', "Label", "Collision Radius", ...
"LabelHorizontalAlignment", "left", "HandleVisibility", "off");
yline(x2, commsRadius, 'r--', "Label", "Communications Radius", ...
"LabelHorizontalAlignment", "left", "HandleVisibility", "off");
ylim(x2, [0, commsRadius + 5]);
savefig(f2, "plot2.fig");
exportgraphics(f2, "plot2.png");
plots_3_4.m
+120
View File
@@ -0,0 +1,120 @@
clear;
%% Load data
dataPath = fullfile('.', 'sandbox', 'plot3');
dataFiles = dir(dataPath);
dataFiles = dataFiles(~startsWith({dataFiles.name}, '.'));
simInits = dataFiles(endsWith({dataFiles.name}, 'miSimInits.mat'));
simHists = dataFiles(endsWith({dataFiles.name}, 'miSimHist.mat'));
assert(length(simHists) == length(simInits), "input data availability mismatch");
assert(isscalar(simHists));
init = load(fullfile(simInits(1).folder, simInits(1).name));
hist = load(fullfile(simHists(1).folder, simHists(1).name));
hist = hist.out;
%% Plot 3: Per-agent and cumulative normalized performance
assert(size(init.objectivePos, 1) == 1);
assert(hist.useDoubleIntegrator);
nAgents = length(hist.agent);
agentLabels = "Agent " + string(1:nAgents)';
f3 = figure;
x3 = axes;
hold(x3, 'on');
plot(x3, hist.perf ./ init.objectiveIntegral, "LineWidth", 2);
for ii = 1:nAgents
plot(x3, hist.agent(ii).perf ./ init.objectiveIntegral, "LineWidth", 2);
end
hold(x3, 'off');
grid(x3, "on");
ylabel(x3, "Performance (normalized)");
xlabel(x3, "Timestep");
legend(x3, ["Cumulative"; agentLabels], "Location", "northwest");
title(x3, "$AII\beta$ Performance", "Interpreter", "latex");
savefig(f3, "plot3.fig");
exportgraphics(f3, "plot3.png");
%% Plot 4: Pairwise distances and barrier functions
commsRadius = hist.agent(1).commsRadius;
collisionRadius = hist.agent(1).collisionRadius;
nPairs = nchoosek(nAgents, 2);
T = size(hist.agent(1).pos, 1);
% Compute pairwise distances over time
pairDistMat = NaN(T, nPairs);
pairLabels = strings(nPairs, 1);
pp = 0;
for jj = 1:nAgents-1
for kk = jj+1:nAgents
pp = pp + 1;
pairDistMat(:, pp) = vecnorm(hist.agent(jj).pos - hist.agent(kk).pos, 2, 2);
pairLabels(pp) = sprintf("Agents %d-%d Distance", jj, kk);
end
end
f4 = figure;
x4 = axes;
% Left Y-axis: pairwise distances
hold(x4, 'on');
hLeft = gobjects(nPairs, 1);
for pp = 1:nPairs
hLeft(pp) = plot(x4, pairDistMat(:, pp), 'LineWidth', 2);
end
yline(x4, collisionRadius, 'r--', "Label", "Collision Radius", ...
"LabelHorizontalAlignment", "left", "HandleVisibility", "off");
yline(x4, commsRadius, 'r--', "Label", "Communications Radius", ...
"LabelHorizontalAlignment", "left", "HandleVisibility", "off");
hold(x4, 'off');
xlabel(x4, "Timestep");
ylabel(x4, "Pairwise distance");
title(x4, "$AII\beta$ Pairwise Agent Distances and Barrier Function Values", "Interpreter", "latex");
grid(x4, "on");
savefig(f4, "plot4_distanceOnly.fig");
exportgraphics(f4, "plot4_distanceOnly.png");
% Right Y-axis: barrier function values
nObs = init.numObstacles;
nAA = nchoosek(nAgents, 2);
nAO = nAgents * nObs;
nAD = nAgents * 6;
nComms = size(hist.barriers, 1) - nAA - nAO - nAD;
colStart = 1;
comStart = colStart + nAA + nAO + nAD;
pairColors = lines(nAA);
yyaxis(x4, 'right');
hold(x4, 'on');
hRight = gobjects(nAA + nComms, 1);
rightLabels = strings(nAA + nComms, 1);
idx = 0;
for pp = 1:nAA
idx = idx + 1;
hRight(idx) = plot(x4, hist.barriers(colStart + pp - 1, :), '--', ...
'LineWidth', 1.5, 'Color', pairColors(pp, :));
rightLabels(idx) = sprintf('h_{col} %d', pp);
end
for pp = 1:nComms
idx = idx + 1;
hRight(idx) = plot(x4, hist.barriers(comStart + pp - 1, :), '-.', ...
'LineWidth', 1.5, 'Color', pairColors(pp, :));
rightLabels(idx) = sprintf('h_{com} %d', pp);
end
hold(x4, 'off');
ylabel(x4, "Barrier function $h$", "Interpreter", "latex");
% Y-axis limits
yyaxis(x4, 'left'); ylim(x4, [0, 25]);
yyaxis(x4, 'right'); ylim(x4, [0, 275]);
x4.YAxis(2).Color = 'k';
legend([hLeft(:); hRight(:)], [pairLabels(:); rightLabels(:)], "Location", "eastoutside");
savefig(f4, "plot4.fig");
exportgraphics(f4, "plot4.png");
resources/project/6V6Vr7QAsqxEmRj-ezOm1ygcTak/bJiTrGQ5GQ9DMI6whRMInBQY2yQd.xml
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<?xml version="1.0" encoding="UTF-8"?>
<Info>
<Category UUID="FileClassCategory">
<Label UUID="test"/>
</Category>
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test/parametricTestSuite.m
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@@ -34,8 +34,7 @@ classdef parametricTestSuite < matlab.unittest.TestCase
% Define scenario according to CSV specification
tc.domain = tc.domain.initialize([params.domainMin; params.domainMax], REGION_TYPE.DOMAIN, "Domain");
objectiveSigma = reshape(params.objectiveVar, [1 2 2]);
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper(params.objectivePos, objectiveSigma), tc.domain, params.discretizationStep, params.protectedRange, params.sensorPerformanceMinimum, params.objectivePos, objectiveSigma);
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper(params.objectivePos, reshape(params.objectiveVar, [2 2])), tc.domain, params.discretizationStep, params.protectedRange, params.sensorPerformanceMinimum);
agents = cell(size(params.initialPositions, 2) / 3, 1);
for ii = 1:size(agents, 1)
@@ -82,8 +81,7 @@ 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");
objectiveCenter = [.75 * l, 0.75 * l];
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper(objectiveCenter), tc.domain, params.discretizationStep(ii), params.protectedRange(ii), params.sensorPerformanceMinimum(ii), objectiveCenter);
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);
test/results.m
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classdef results < matlab.unittest.TestCase
properties (Constant, Access = private)
seed = 1;
domainSize = [150, 150, 100]; % fixed domain size [X, Y, Z]
end
properties (Access = private)
% System under test
testClass = miSim;
%% Diagnostic Parameters
% No effect on simulation dynamics
makeVideo = false; % disable video writing for big performance increase
makePlots = false; % disable plotting for big performance increase (also disables video)
plotCommsGeometry = false; % disable plotting communications geometries
%% Scenario Reinitialization
% Number of extra reinitializations per test case (3 n-values x 4 configs = 12).
% Order: n3/A_1_alpha, n3/A_1_beta, n3/A_2_alpha, n3/B_1_beta,
% n5/A_1_alpha, ..., n6/B_1_beta
% Set inspectScenarios = true to pause after init for manual review.
% At the keyboard prompt, type the number of reinits needed into
% the variable 'reinitCount', then 'dbcont' to continue.
inspectScenarios = false;
reinit = zeros(1, 12);
%% Fixed Test Parameters
useFixedTopology = true; % No lesser neighbor, fixed network instead
discretizationStep = 0.5;
protectedRange = 5;
collisionRadius = 5;
sensorPerformanceMinimum = 0.005;
comRange = 20;
maxIter = 400;
initialStepSize = 1;
% Each row: [minX minY minZ maxX maxY maxZ]
obstacleCorners = [results.domainSize(1)/2, results.domainSize(2)*5/8, 0, results.domainSize(1)*5/8, results.domainSize(2), 35;
results.domainSize(1)/3, 0, 0, results.domainSize(1)/2, results.domainSize(2)*3/8, 40];
barrierGain = 1;
barrierExponent = 1;
timestep = 0.5;
dampingCoeff = 2;
end
properties (TestParameter)
%% Test Iterations
% Specific parameter combos to run iterations on
n = struct('n3', 3, 'n5', 5, 'n6', 6); % number of agents
config = results.makeConfigs();
end
properties (MethodSetupParameter)
trials = struct('r1', 1, 'r2', 2, 'r3', 3, 'r4', 4, 'r5', 5);
end
methods (TestMethodSetup)
function setSeed(tc, trials)
rng(tc.seed + trials);
end
end
methods (Static, Access = public)
function c = makeConfigs()
rng(results.seed);
abMin = 6; % alpha*beta >= 6 ensures membership(0) = tanh(3) >= 0.995
alphaDist = rand(1, 2) .* [75, 45];
betaDist = abMin ./ alphaDist + rand(1, 2) .* [1, 1/8] .* (20 - abMin ./ alphaDist);
alphaTilt = 10 + rand(1, 2) .* [20, 20];
betaTilt = abMin ./ alphaTilt + rand(1, 2) .* (50 - abMin ./ alphaTilt);
sensors = struct('alphaDist', num2cell(alphaDist), 'alphaTilt', num2cell(alphaTilt), 'betaDist', num2cell(betaDist), 'betaTilt', num2cell(betaTilt));
sensor1 = sigmoidSensor;
sensor2 = sigmoidSensor;
sensor1 = sensor1.initialize(sensors(1).alphaDist, sensors(1).betaDist, sensors(1).alphaTilt, sensors(1).betaTilt);
sensor2 = sensor2.initialize(sensors(2).alphaDist, sensors(2).betaDist, sensors(2).alphaTilt, sensors(2).betaTilt);
% sensor1.plotParameters;
% sensor2.plotParameters;
c = struct('A_1_alpha', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(1), 'doubleIntegrator', false), ...
'A_1_beta', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(1), 'doubleIntegrator', true), ...
'A_2_alpha', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(2), 'doubleIntegrator', false), ...
'B_1_beta', struct('objectivePos', [[3, 1] / 4 .* results.domainSize(1:2); [3, 1] / 4 .* results.domainSize(1:2) + 25 .* [-1, 1] ./ sqrt(2)], 'sensor', sensors(1), 'doubleIntegrator', true));
end
end
methods (Test)
function plot1_runs(tc, n, config)
% if n == 5 && config.doubleIntegrator == true
% tc.makePlots = true;
% else
% tc.makePlots = false;
% end
% Compute test case index for reinit lookup
nKeys = fieldnames(tc.n);
configKeys = fieldnames(tc.config);
nIdx = find(cellfun(@(k) tc.n.(k) == n, nKeys));
configIdx = find(cellfun(@(k) isequal(tc.config.(k), config), configKeys));
testIdx = (nIdx - 1) * numel(configKeys) + configIdx;
% Determine number of reinitializations
reinitCount = tc.reinit(testIdx);
for reroll = 0:reinitCount
% Set up fixed-size domain
minAlt = tc.domainSize(3)/10 + rand * 1/10 * tc.domainSize(3);
% Place sensing objective(s) at fixed positions from config
objectiveMu = config.objectivePos;
numDist = size(objectiveMu, 1);
objectiveSigma = [];
for ii = 1:numDist
sig = [200, 140; 140, 280];
if ~mod(ii, 2)
sig = rot90(sig,2);
end
sig = reshape(sig, [1, 2, 2]);
objectiveSigma = cat(1, objectiveSigma, sig);
end
tc.testClass.domain = tc.testClass.domain.initialize([zeros(1, 3); tc.domainSize], REGION_TYPE.DOMAIN, "Domain");
tc.testClass.domain.objective = tc.testClass.domain.objective.initialize(objectiveFunctionWrapper(objectiveMu, objectiveSigma), tc.testClass.domain, tc.discretizationStep, tc.protectedRange, tc.sensorPerformanceMinimum, objectiveMu, objectiveSigma);
% Initialize agents
agents = cell(n, 1);
[agents{:}] = deal(agent);
% Initialize sensor model
sensorModel = sigmoidSensor;
sensorModel = sensorModel.initialize(config.sensor.alphaDist, config.sensor.betaDist, config.sensor.alphaTilt, config.sensor.betaTilt);
% Initialize fixed obstacles from corner coordinates
nObs = size(tc.obstacleCorners, 1);
obstacles = cell(nObs, 1);
for jj = 1:nObs
corners = [tc.obstacleCorners(jj, 1:3); tc.obstacleCorners(jj, 4:6)];
obstacles{jj} = rectangularPrism;
obstacles{jj} = obstacles{jj}.initialize(corners, REGION_TYPE.OBSTACLE, sprintf("Obstacle %d", jj));
end
% Place agents in small-x, large-y quadrant (opposite objectives)
% with chain topology: each agent connected only to its neighbors
midXY = (tc.testClass.domain.minCorner(1:2) + tc.testClass.domain.maxCorner(1:2)) / 2;
quadrantSize = tc.testClass.domain.maxCorner(1:2) / 2;
margin = quadrantSize / 6;
agentBounds = [tc.testClass.domain.minCorner(1) + margin(1), ...
midXY(2) + margin(2); ...
midXY(1) - margin(1), ...
tc.testClass.domain.maxCorner(2) - margin(2)];
% Find a fixed altitude where sensor performance passes at ALL
% corners of the placement bounds (worst-case XY)
corners = [agentBounds(1,1), agentBounds(1,2);
agentBounds(2,1), agentBounds(1,2);
agentBounds(1,1), agentBounds(2,2);
agentBounds(2,1), agentBounds(2,2)];
agentAlt = tc.testClass.domain.maxCorner(3) - tc.collisionRadius;
while agentAlt > minAlt + 2 * tc.collisionRadius
worstPerf = inf;
for cc = 1:4
p = sensorModel.sensorPerformance([corners(cc,:), agentAlt], [corners(cc,:), 0]);
worstPerf = min(worstPerf, p);
end
if worstPerf >= tc.sensorPerformanceMinimum * 10
break;
end
agentAlt = agentAlt - 1;
end
chainSpacingMin = 0.7 * tc.comRange;
chainSpacingMax = 0.9 * tc.comRange;
collisionGeometry = spherical;
for jj = 1:n
retry = true;
while retry
retry = false;
if jj == 1
% First agent: random XY within bounds, fixed altitude
agentPos = [agentBounds(1, :) + (agentBounds(2, :) - agentBounds(1, :)) .* rand(1, 2), agentAlt];
else
% Place at 0.7-0.9 * comRange in XY from previous agent, same altitude
dir = randn(1, 2);
dir = dir / norm(dir);
r = chainSpacingMin + rand * (chainSpacingMax - chainSpacingMin);
agentPos = [agents{jj-1}.pos(1:2) + r * dir, agentAlt];
end
% Check within placement bounds (XY only, Z is fixed)
if any(agentPos(1:2) <= agentBounds(1, :)) || any(agentPos(1:2) >= agentBounds(2, :))
retry = true;
continue;
end
% Check sensor performance threshold; lower altitude if it fails
if sensorModel.sensorPerformance(agentPos, [agentPos(1:2), 0]) < tc.sensorPerformanceMinimum * 10
agentAlt = max(agentAlt - tc.collisionRadius, minAlt + 1.1 * tc.collisionRadius);
agentPos(3) = agentAlt;
% If we've hit the floor and still failing, widen XY search
if agentAlt <= minAlt + 2 * tc.collisionRadius
agentBounds = [tc.testClass.domain.minCorner(1) + tc.collisionRadius, ...
tc.testClass.domain.minCorner(2) + tc.collisionRadius; ...
tc.testClass.domain.maxCorner(1) - tc.collisionRadius, ...
tc.testClass.domain.maxCorner(2) - tc.collisionRadius];
end
retry = true;
continue;
end
% Must be within comRange of previous agent (chain link)
if jj > 1 && norm(agents{jj-1}.pos - agentPos) >= tc.comRange
retry = true;
continue;
end
% Must be BEYOND comRange of all non-adjacent agents (sparsity)
% for kk = 1:(jj - 2)
% if norm(agents{kk}.pos - agentPos) < tc.comRange
% retry = true;
% break;
% end
% end
% if retry, continue; end
% No collision with any existing agent
for kk = 1:(jj - 1)
if norm(agents{kk}.pos - agentPos) < agents{kk}.collisionGeometry.radius + tc.collisionRadius
retry = true;
break;
end
end
if retry, continue; end
% No collision with any obstacle
for kk = 1:nObs
P = min(max(agentPos, obstacles{kk}.minCorner), obstacles{kk}.maxCorner);
d = agentPos - P;
if dot(d, d) <= tc.collisionRadius^2
retry = true;
break;
end
end
end
% Initialize agent
collisionGeometry = collisionGeometry.initialize(agentPos, tc.collisionRadius, REGION_TYPE.COLLISION, sprintf("Agent %d Collision Region", jj));
agents{jj} = agents{jj}.initialize(agentPos, collisionGeometry, sensorModel, tc.comRange, tc.maxIter, tc.initialStepSize, sprintf("Agent %d", jj), tc.plotCommsGeometry);
end
% Randomly shuffle agents to vary index-based topology
agents = agents(randperm(numel(agents)));
end % reroll loop
% Inspect scenario if enabled
if tc.inspectScenarios
tc.testClass = tc.testClass.initialize(tc.testClass.domain, agents, tc.barrierGain, tc.barrierExponent, minAlt, tc.timestep, tc.maxIter, obstacles, tc.makePlots, tc.makeVideo, config.doubleIntegrator, tc.dampingCoeff, tc.useFixedTopology);
fprintf("Test %d (n=%d, config=%s): reinit=%d. Inspect plot.\n", testIdx, n, configKeys{configIdx}, reinitCount);
fprintf("To add reinits, update tc.reinit(%d) and rerun.\n", testIdx);
keyboard;
tc.testClass = tc.testClass.teardown();
return;
end
% Set up simulation
tc.testClass = tc.testClass.initialize(tc.testClass.domain, agents, tc.barrierGain, tc.barrierExponent, minAlt, tc.timestep, tc.maxIter, obstacles, tc.makePlots, tc.makeVideo, config.doubleIntegrator, tc.dampingCoeff, tc.useFixedTopology);
% Save simulation parameters to output file
tc.testClass.writeInits();
% Run
tc.testClass = tc.testClass.run();
% Cleanup
tc.testClass = tc.testClass.teardown();
close all;
end
function AIIbeta_plots_3_4(tc)
% test-specific parameters
tc.makePlots = false;
tc.makeVideo = false;
maxIters = 400;
configs = results.makeConfigs();
c = configs.A_2_alpha;
c.doubleIntegrator = true; % make a2alpha into a2beta
% Set up fixed-size domain
minAlt = tc.domainSize(3)/10 + rand * 1/10 * tc.domainSize(3);
tc.testClass.domain = tc.testClass.domain.initialize([zeros(1, 3); tc.domainSize], REGION_TYPE.DOMAIN, "Domain");
% Set objective
objectiveMu = [tc.domainSize(1) * 2 / 3, tc.domainSize(2) * 3 / 4];
objectiveSigma = reshape([215, 100; 100, 175], [1, 2, 2]);
tc.testClass.domain.objective = tc.testClass.domain.objective.initialize(objectiveFunctionWrapper(objectiveMu, objectiveSigma), tc.testClass.domain, tc.discretizationStep, tc.protectedRange, tc.sensorPerformanceMinimum, objectiveMu, objectiveSigma);
% Set agent initial states (fully connected network of 4)
centerPos = [tc.domainSize(1) / 4, tc.domainSize(2) / 4];
d = tc.collisionRadius + (tc.comRange - tc.collisionRadius) / 4;
agentsPos = centerPos + [1, 1; 1, -1; -1, -1; -1, 1] / sqrt(2) * d;
agentAlt = minAlt * 1.5;
agentsPos = [agentsPos, agentAlt * ones(4, 1) + rand * 5 - 2.5];
agents = {agent, agent, agent, agent};
cg = spherical;
sensorModel = sigmoidSensor;
sensorModel = sensorModel.initialize(c.sensor.alphaDist, c.sensor.betaDist, c.sensor.alphaTilt, c.sensor.betaTilt);
agents{1} = agents{1}.initialize(agentsPos(1, :), cg.initialize(agentsPos(1, :), tc.collisionRadius, REGION_TYPE.COLLISION, "Agent 1 Collision Geometry"), sensorModel, tc.comRange, maxIters, tc.initialStepSize, "Agent 1", false);
agents{2} = agents{2}.initialize(agentsPos(2, :), cg.initialize(agentsPos(2, :), tc.collisionRadius, REGION_TYPE.COLLISION, "Agent 2 Collision Geometry"), sensorModel, tc.comRange, maxIters, tc.initialStepSize, "Agent 2", false);
agents{3} = agents{3}.initialize(agentsPos(3, :), cg.initialize(agentsPos(3, :), tc.collisionRadius, REGION_TYPE.COLLISION, "Agent 3 Collision Geometry"), sensorModel, tc.comRange, maxIters, tc.initialStepSize, "Agent 3", false);
agents{4} = agents{4}.initialize(agentsPos(4, :), cg.initialize(agentsPos(4, :), tc.collisionRadius, REGION_TYPE.COLLISION, "Agent 4 Collision Geometry"), sensorModel, tc.comRange, maxIters, tc.initialStepSize, "Agent 4", false);
obstacles = cell(1, 1);
obstacles{1} = rectangularPrism;
obstacles{1} = obstacles{1}.initialize([0, tc.domainSize(2)/2, 0; tc.domainSize(1) * 0.4, tc.domainSize(2), 40],REGION_TYPE.OBSTACLE, "Obstacle 1");
% Set up simulation
tc.testClass = tc.testClass.initialize(tc.testClass.domain, agents, tc.barrierGain, tc.barrierExponent, minAlt, tc.timestep, maxIters, obstacles, tc.makePlots, tc.makeVideo, c.doubleIntegrator, tc.dampingCoeff, tc.useFixedTopology);
% Save simulation parameters to output file
tc.testClass.writeInits();
% Run
tc.testClass = tc.testClass.run();
% Cleanup
tc.testClass = tc.testClass.teardown();
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
test/test_miSim.m
+10 -132
View File
@@ -166,8 +166,8 @@ classdef test_miSim < matlab.unittest.TestCase
end
% Initialize candidate agent collision geometry
candidateGeometry = spherical;
candidateGeometry = candidateGeometry.initialize(candidatePos, tc.collisionRanges(ii), REGION_TYPE.COLLISION);
candidateGeometry = rectangularPrism;
candidateGeometry = candidateGeometry.initialize([candidatePos - tc.collisionRanges(ii) * ones(1, 3); candidatePos + tc.collisionRanges(ii) * ones(1, 3)], REGION_TYPE.COLLISION);
% Initialize candidate agent sensor model
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));
@@ -437,12 +437,12 @@ classdef test_miSim < matlab.unittest.TestCase
tc.domain = tc.domain.initialize([zeros(1, 3);tc.minDimension* ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
% make basic sensing objective
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([7, 6]), tc.domain, tc.discretizationStep, tc.protectedRange, 1e-6, [7, 6]);
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([7, 6]), tc.domain, tc.discretizationStep, tc.protectedRange);
% Initialize agent collision geometry
tc.agents = {agent};
geometry1 = spherical;
geometry1 = geometry1.initialize([tc.domain.center(1:2)-tc.domain.dimensions(1)/4, 3], tc.collisionRanges(1), REGION_TYPE.COLLISION);
geometry1 = rectangularPrism;
geometry1 = geometry1.initialize([[tc.domain.center(1:2)-tc.domain.dimensions(1)/4, 3] - tc.collisionRanges(1) * ones(1, 3); [tc.domain.center(1:2)-tc.domain.dimensions(1)/4, 3] + tc.collisionRanges(1) * ones(1, 3)], REGION_TYPE.COLLISION);
% Initialize agent sensor model with fixed parameters
tc.sensor = tc.sensor.initialize(tc.minDimension / 2, 3, 20, 3);
@@ -466,7 +466,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.domain = tc.domain.initialize([zeros(1, 3);tc.minDimension* ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
% make basic sensing objective
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([3, 7]), tc.domain, tc.discretizationStep, tc.protectedRange, 1e-6, [3, 7]);
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([3, 7]), tc.domain, tc.discretizationStep, tc.protectedRange);
% Initialize agent collision geometry
tc.agents = {agent; agent};
@@ -504,7 +504,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.domain = tc.domain.initialize([zeros(1, 3);tc.minDimension* ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
% make basic sensing objective
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([8, 5.2195]), tc.domain, tc.discretizationStep, tc.protectedRange, 1e-6, [8, 5.2195]);
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([8, 5.2195]), tc.domain, tc.discretizationStep, tc.protectedRange);
% Initialize agent collision geometry
tc.agents = {agent; agent;};
@@ -588,7 +588,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.domain = tc.domain.initialize([zeros(1, 3); tc.minDimension* ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
% make basic sensing objective
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([8, 5]), tc.domain, tc.discretizationStep, tc.protectedRange, 1e-6, [8, 5]);
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([8, 5]), tc.domain, tc.discretizationStep, tc.protectedRange);
% Initialize agent collision geometry
tc.agents = {agent; agent;};
@@ -633,7 +633,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.domain = tc.domain.initialize([zeros(1, 3);tc.minDimension* ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
% make basic sensing objective
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([8, 5]), tc.domain, tc.discretizationStep, tc.protectedRange, 1e-6, [8, 5]);
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([8, 5]), tc.domain, tc.discretizationStep, tc.protectedRange);
% Initialize agent collision geometry
tc.agents = {agent; agent; agent; agent; agent;};
@@ -683,7 +683,7 @@ classdef test_miSim < matlab.unittest.TestCase
tc.domain = tc.domain.initialize([zeros(1, 3); tc.minDimension* ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
% make basic sensing objective
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([8, 5]), tc.domain, tc.discretizationStep, tc.protectedRange, 1e-6, [8, 5]);
tc.domain.objective = tc.domain.objective.initialize(objectiveFunctionWrapper([8, 5]), tc.domain, tc.discretizationStep, tc.protectedRange);
% Initialize agent collision geometry
tc.agents = {agent; agent; agent; agent; agent; agent; agent;};
@@ -728,128 +728,6 @@ classdef test_miSim < matlab.unittest.TestCase
0, 0, 0, 1, 1, 1, 1;
0, 0, 0, 0, 0, 1, 1; ]));
end
function miSim_initializeFromInits(tc)
% Build a minimal valid simulation, write it to a matfile, reload
% via initializeFromInits, assert round-trip consistency, then
% delete the file. No plotting or video at any stage.
% Obstacles
nGeom = tc.minNumObstacles + randi(tc.maxNumObstacles - tc.minNumObstacles);
tc.obstacles = cell(nGeom, 1);
for ii = 1:nGeom
badCandidate = true;
while badCandidate
tc.obstacles{ii} = rectangularPrism;
tc.obstacles{ii} = tc.obstacles{ii}.initializeRandom(REGION_TYPE.OBSTACLE, ...
sprintf("Obstacle %d", ii), tc.minObstacleSize, tc.maxObstacleSize, ...
tc.domain, tc.minAlt);
if ~tc.obstacleCollisionCheck(tc.obstacles(1:(ii - 1)), tc.obstacles{ii})
badCandidate = false;
end
end
end
% Agents
nAgents = tc.minAgents;
tc.agents = cell(nAgents, 1);
tc.collisionRanges = tc.minCollisionRange + rand(nAgents, 1) * (tc.maxCollisionRange - tc.minCollisionRange);
tc.commsRanges = tc.minCommsRange + rand(nAgents, 1) * (tc.maxCommsRange - tc.minCommsRange);
for ii = 1:nAgents
initInvalid = true;
while initInvalid
if ii == 1
candidatePos = tc.domain.random();
candidatePos(3) = tc.minAlt + rand * 3;
while agentsCrowdObjective(tc.domain.objective, candidatePos, mean(tc.domain.dimensions) / 2)
candidatePos = tc.domain.random();
candidatePos(3) = tc.minAlt + rand * 3;
end
else
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;
end
if ~tc.domain.contains(candidatePos), continue; end
if agentsCrowdObjective(tc.domain.objective, candidatePos, mean(tc.domain.dimensions) / 2), continue; end
% Connectivity check
connections = false(1, ii - 1);
for jj = 1:(ii - 1)
if norm(tc.agents{jj}.pos - candidatePos) <= min(tc.commsRanges([ii, jj]))
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
if ii ~= 1 && ~any(connections), continue; end
geom = spherical;
geom = geom.initialize(candidatePos, tc.collisionRanges(ii), REGION_TYPE.COLLISION);
tc.sensor = sigmoidSensor;
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));
newAgent = agent;
newAgent = newAgent.initialize(candidatePos, geom, tc.sensor, tc.commsRanges(ii), tc.maxIter, tc.initialStepSize);
% Domain / obstacle / agent collision checks
violation = false;
for jj = 1:size(newAgent.collisionGeometry.vertices, 1)
if ~tc.domain.contains(newAgent.collisionGeometry.vertices(jj, 1:3))
violation = true; break;
end
end
if violation, continue; end
for kk = 1:size(tc.obstacles, 1)
if geometryIntersects(tc.obstacles{kk}, newAgent.collisionGeometry)
violation = true; break;
end
end
if violation, continue; end
for kk = 1:(ii - 1)
if geometryIntersects(tc.agents{kk}.collisionGeometry, newAgent.collisionGeometry)
violation = true; break;
end
end
if newAgent.pos(3) - newAgent.collisionGeometry.radius <= tc.minAlt
violation = true;
end
if violation, continue; end
initInvalid = false;
tc.agents{ii} = newAgent;
end
end
% Initialize first sim (no plots / video)
sim1 = miSim;
sim1 = sim1.initialize(tc.domain, tc.agents, tc.barrierGain, tc.barrierExponent, ...
tc.minAlt, tc.timestep, tc.maxIter, tc.obstacles, false, false, ...
tc.useDoubleIntegrator, tc.dampingCoeff, tc.useFixedTopology);
% Write inits and build file path
sim1.writeInits();
initsFile = fullfile(matlab.project.rootProject().RootFolder, "sandbox", ...
strcat(sim1.artifactName, "_miSimInits.mat"));
% Load via initializeFromInits
sim2 = miSim;
sim2 = sim2.initializeFromInits(initsFile);
% Assertions
tc.assertEqual(size(sim2.agents, 1), size(sim1.agents, 1));
tc.assertEqual(sim2.maxIter, sim1.maxIter);
tc.assertEqual(sim2.barrierGain, sim1.barrierGain);
% Cleanup
delete(initsFile);
end
end
methods
util/objectiveFunctionWrapper.m
+1 -1
View File
@@ -9,7 +9,7 @@ function f = objectiveFunctionWrapper(center, sigma)
arguments (Output)
f (1, 1) {mustBeA(f, "function_handle")};
end
assert(size(center, 1) == size(sigma, 1));
f = @(x,y) sum(cell2mat(arrayfun(@(i) mvnpdf([x(:), y(:)], center(i,:), squeeze(sigma(i, :, :))), 1:size(center,1), "UniformOutput", false)), 2);
end