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included features from SPAWC 2026 branch

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This commit is contained in:
Kevin Dee
2026-03-31 21:18:02 -07:00
parent ca891a809f
commit c3fa1de914
16 changed files with 65 additions and 36 deletions
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1
.gitignore vendored
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@@ -48,6 +48,7 @@ sandbox/*
# Figures # Figures
*.fig *.fig
*.png
# Python Virtual Environment # Python Virtual Environment
aerpaw/venv/ aerpaw/venv/

3
@agent/initialize.m
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@@ -15,6 +15,7 @@ function obj = initialize(obj, pos, collisionGeometry, sensorModel, comRange, ma
end end
obj.pos = pos; obj.pos = pos;
obj.lastPos = pos;
obj.vel = zeros(1, 3); obj.vel = zeros(1, 3);
obj.lastVel = zeros(1, 3); obj.lastVel = zeros(1, 3);
obj.collisionGeometry = collisionGeometry; obj.collisionGeometry = collisionGeometry;
@@ -35,4 +36,4 @@ function obj = initialize(obj, pos, collisionGeometry, sensorModel, comRange, ma
% Initialize FOV cone % Initialize FOV cone
obj.fovGeometry = cone; obj.fovGeometry = cone;
obj.fovGeometry = obj.fovGeometry.initialize([obj.pos(1:3)], tand(obj.sensorModel.alphaTilt) * obj.pos(3), obj.pos(3), REGION_TYPE.FOV, sprintf("%s FOV", obj.label)); obj.fovGeometry = obj.fovGeometry.initialize([obj.pos(1:3)], tand(obj.sensorModel.alphaTilt) * obj.pos(3), obj.pos(3), REGION_TYPE.FOV, sprintf("%s FOV", obj.label));
end end

9
@agent/run.m
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@@ -14,6 +14,13 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents, useD
obj (1, 1) {mustBeA(obj, "agent")}; obj (1, 1) {mustBeA(obj, "agent")};
end end
% Always update lastPos/lastVel so constrainMotion evaluates barriers at
% the correct (most recent) position, even when this agent has no partition.
obj.lastPos = obj.pos;
if useDoubleIntegrator
obj.lastVel = obj.vel;
end
% Collect objective function values across partition % Collect objective function values across partition
partitionMask = partitioning == index; partitionMask = partitioning == index;
if ~any(partitionMask(:)) if ~any(partitionMask(:))
@@ -79,10 +86,8 @@ function obj = run(obj, domain, partitioning, timestepIndex, index, agents, useD
gradNorm = norm(gradC); gradNorm = norm(gradC);
% Compute unconstrained next state % Compute unconstrained next state
obj.lastPos = obj.pos;
if useDoubleIntegrator if useDoubleIntegrator
% Double-integrator: gradient produces desired acceleration with damping % Double-integrator: gradient produces desired acceleration with damping
obj.lastVel = obj.vel;
if gradNorm < 1e-100 if gradNorm < 1e-100
a_gradient = zeros(1, 3); a_gradient = zeros(1, 3);
else else

26
@miSim/constrainMotion.m
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@@ -39,10 +39,10 @@ function [obj] = constrainMotion(obj)
h(logical(eye(nAgents))) = 0; % self value is 0 h(logical(eye(nAgents))) = 0; % self value is 0
for ii = 1:(nAgents - 1) for ii = 1:(nAgents - 1)
for jj = (ii + 1):nAgents for jj = (ii + 1):nAgents
h(ii, jj) = norm(obj.agents{ii}.pos - obj.agents{jj}.pos)^2 - (obj.agents{ii}.collisionGeometry.radius + obj.agents{jj}.collisionGeometry.radius)^2; h(ii, jj) = norm(obj.agents{ii}.lastPos - obj.agents{jj}.lastPos)^2 - (obj.agents{ii}.collisionGeometry.radius + obj.agents{jj}.collisionGeometry.radius)^2;
h(jj, ii) = h(ii, jj); h(jj, ii) = h(ii, jj);
A(kk, (3 * ii - 2):(3 * ii)) = -2 * (obj.agents{ii}.pos - obj.agents{jj}.pos); A(kk, (3 * ii - 2):(3 * ii)) = -2 * (obj.agents{ii}.lastPos - obj.agents{jj}.lastPos);
A(kk, (3 * jj - 2):(3 * jj)) = -A(kk, (3 * ii - 2):(3 * ii)); A(kk, (3 * jj - 2):(3 * jj)) = -A(kk, (3 * ii - 2):(3 * ii));
% Slack derived from existing params: recovery velocity = max gradient approach velocity. % Slack derived from existing params: recovery velocity = max gradient approach velocity.
% Correction splits between 2 agents, so |A| = 2*r_sum % Correction splits between 2 agents, so |A| = 2*r_sum
@@ -69,11 +69,11 @@ function [obj] = constrainMotion(obj)
for ii = 1:nAgents for ii = 1:nAgents
for jj = 1:size(obj.obstacles, 1) for jj = 1:size(obj.obstacles, 1)
% find closest position to agent on/in obstacle % find closest position to agent on/in obstacle
cPos = obj.obstacles{jj}.closestToPoint(obj.agents{ii}.pos); cPos = obj.obstacles{jj}.closestToPoint(obj.agents{ii}.lastPos);
hObs(ii, jj) = dot(obj.agents{ii}.pos - cPos, obj.agents{ii}.pos - cPos) - obj.agents{ii}.collisionGeometry.radius^2; hObs(ii, jj) = dot(obj.agents{ii}.lastPos - cPos, obj.agents{ii}.lastPos - cPos) - obj.agents{ii}.collisionGeometry.radius^2;
A(kk, (3 * ii - 2):(3 * ii)) = -2 * (obj.agents{ii}.pos - cPos); A(kk, (3 * ii - 2):(3 * ii)) = -2 * (obj.agents{ii}.lastPos - cPos);
% Floor for single-agent constraint: full correction on one agent, |A| = 2*r_i % Floor for single-agent constraint: full correction on one agent, |A| = 2*r_i
r_i = obj.agents{ii}.collisionGeometry.radius; r_i = obj.agents{ii}.collisionGeometry.radius;
v_max_i = obj.agents{ii}.initialStepSize / obj.timestep; v_max_i = obj.agents{ii}.initialStepSize / obj.timestep;
@@ -93,37 +93,37 @@ function [obj] = constrainMotion(obj)
h_xMin = 0.0; h_xMax = 0.0; h_yMin = 0.0; h_yMax = 0.0; h_zMin = 0.0; h_zMax = 0.0; h_xMin = 0.0; h_xMax = 0.0; h_yMin = 0.0; h_yMax = 0.0; h_zMin = 0.0; h_zMax = 0.0;
for ii = 1:nAgents for ii = 1:nAgents
% X minimum % X minimum
h_xMin = (obj.agents{ii}.pos(1) - obj.domain.minCorner(1)) - obj.agents{ii}.collisionGeometry.radius; h_xMin = (obj.agents{ii}.lastPos(1) - obj.domain.minCorner(1)) - obj.agents{ii}.collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [-1, 0, 0]; A(kk, (3 * ii - 2):(3 * ii)) = [-1, 0, 0];
b(kk) = obj.barrierGain * max(0, h_xMin)^obj.barrierExponent; b(kk) = obj.barrierGain * max(0, h_xMin)^obj.barrierExponent;
kk = kk + 1; kk = kk + 1;
% X maximum % X maximum
h_xMax = (obj.domain.maxCorner(1) - obj.agents{ii}.pos(1)) - obj.agents{ii}.collisionGeometry.radius; h_xMax = (obj.domain.maxCorner(1) - obj.agents{ii}.lastPos(1)) - obj.agents{ii}.collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [1, 0, 0]; A(kk, (3 * ii - 2):(3 * ii)) = [1, 0, 0];
b(kk) = obj.barrierGain * max(0, h_xMax)^obj.barrierExponent; b(kk) = obj.barrierGain * max(0, h_xMax)^obj.barrierExponent;
kk = kk + 1; kk = kk + 1;
% Y minimum % Y minimum
h_yMin = (obj.agents{ii}.pos(2) - obj.domain.minCorner(2)) - obj.agents{ii}.collisionGeometry.radius; h_yMin = (obj.agents{ii}.lastPos(2) - obj.domain.minCorner(2)) - obj.agents{ii}.collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [0, -1, 0]; A(kk, (3 * ii - 2):(3 * ii)) = [0, -1, 0];
b(kk) = obj.barrierGain * max(0, h_yMin)^obj.barrierExponent; b(kk) = obj.barrierGain * max(0, h_yMin)^obj.barrierExponent;
kk = kk + 1; kk = kk + 1;
% Y maximum % Y maximum
h_yMax = (obj.domain.maxCorner(2) - obj.agents{ii}.pos(2)) - obj.agents{ii}.collisionGeometry.radius; h_yMax = (obj.domain.maxCorner(2) - obj.agents{ii}.lastPos(2)) - obj.agents{ii}.collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [0, 1, 0]; A(kk, (3 * ii - 2):(3 * ii)) = [0, 1, 0];
b(kk) = obj.barrierGain * max(0, h_yMax)^obj.barrierExponent; b(kk) = obj.barrierGain * max(0, h_yMax)^obj.barrierExponent;
kk = kk + 1; kk = kk + 1;
% Z minimum — enforce z >= minAlt + radius (not just z >= domain floor + radius) % Z minimum — enforce z >= minAlt + radius (not just z >= domain floor + radius)
h_zMin = (obj.agents{ii}.pos(3) - obj.minAlt) - obj.agents{ii}.collisionGeometry.radius; h_zMin = (obj.agents{ii}.lastPos(3) - obj.minAlt) - obj.agents{ii}.collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [0, 0, -1]; A(kk, (3 * ii - 2):(3 * ii)) = [0, 0, -1];
b(kk) = obj.barrierGain * max(0, h_zMin)^obj.barrierExponent; b(kk) = obj.barrierGain * max(0, h_zMin)^obj.barrierExponent;
kk = kk + 1; kk = kk + 1;
% Z maximum % Z maximum
h_zMax = (obj.domain.maxCorner(3) - obj.agents{ii}.pos(3)) - obj.agents{ii}.collisionGeometry.radius; h_zMax = (obj.domain.maxCorner(3) - obj.agents{ii}.lastPos(3)) - obj.agents{ii}.collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [0, 0, 1]; A(kk, (3 * ii - 2):(3 * ii)) = [0, 0, 1];
b(kk) = obj.barrierGain * max(0, h_zMax)^obj.barrierExponent; b(kk) = obj.barrierGain * max(0, h_zMax)^obj.barrierExponent;
kk = kk + 1; kk = kk + 1;
@@ -145,9 +145,9 @@ function [obj] = constrainMotion(obj)
if obj.constraintAdjacencyMatrix(ii, jj) if obj.constraintAdjacencyMatrix(ii, jj)
paddingFactor = 0.9; % Barrier at 90% of actual range; real comms still work beyond this paddingFactor = 0.9; % Barrier at 90% of actual range; real comms still work beyond this
r_comms = paddingFactor * min([obj.agents{ii}.commsGeometry.radius, obj.agents{jj}.commsGeometry.radius]); r_comms = paddingFactor * min([obj.agents{ii}.commsGeometry.radius, obj.agents{jj}.commsGeometry.radius]);
hComms(ii, jj) = r_comms^2 - norm(obj.agents{ii}.pos - obj.agents{jj}.pos)^2; hComms(ii, jj) = r_comms^2 - norm(obj.agents{ii}.lastPos - obj.agents{jj}.lastPos)^2;
A(kk, (3 * ii - 2):(3 * ii)) = 2 * (obj.agents{ii}.pos - obj.agents{jj}.pos); A(kk, (3 * ii - 2):(3 * ii)) = 2 * (obj.agents{ii}.lastPos - obj.agents{jj}.lastPos);
A(kk, (3 * jj - 2):(3 * jj)) = -A(kk, (3 * ii - 2):(3 * ii)); A(kk, (3 * jj - 2):(3 * jj)) = -A(kk, (3 * ii - 2):(3 * ii));
% One-step forward invariance: b = h/dt ensures h cannot % One-step forward invariance: b = h/dt ensures h cannot

6
@miSim/initialize.m
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@@ -138,6 +138,12 @@ function [obj] = initialize(obj, domain, agents, barrierGain, barrierExponent, m
% Initialize variable that will store barrier function values per timestep for analysis purposes % Initialize variable that will store barrier function values per timestep for analysis purposes
obj.barriers = NaN(obj.numBarriers, size(obj.times, 1)); obj.barriers = NaN(obj.numBarriers, size(obj.times, 1));
% Initialize constraint adjacency history (nAgents x nAgents x nTimesteps)
nAgents = size(obj.agents, 1);
obj.constraintAdjacencyHist = false(nAgents, nAgents, size(obj.times, 1));
obj.constraintAdjacencyHist(:, :, 1) = obj.constraintAdjacencyMatrix;
% Set up plots showing initialized state % Set up plots showing initialized state
obj = obj.plot(); obj = obj.plot();

3
@miSim/miSim.m
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@@ -7,7 +7,6 @@ classdef miSim
timestepIndex = NaN; % index of the current timestep (useful for time-indexed arrays) timestepIndex = NaN; % index of the current timestep (useful for time-indexed arrays)
maxIter = NaN; % maximum number of simulation iterations maxIter = NaN; % maximum number of simulation iterations
domain; domain;
objective;
obstacles; % geometries that define obstacles within the domain obstacles; % geometries that define obstacles within the domain
agents; % agents that move within the domain agents; % agents that move within the domain
adjacency = false(0, 0); % Adjacency matrix representing communications network graph adjacency = false(0, 0); % Adjacency matrix representing communications network graph
@@ -28,6 +27,7 @@ classdef miSim
spatialPlotIndices = [6, 4, 3, 2]; spatialPlotIndices = [6, 4, 3, 2];
numBarriers = 0; % Number of barrier functions needed numBarriers = 0; % Number of barrier functions needed
barriers = []; % log barrier function values at each timestep for analysis barriers = []; % log barrier function values at each timestep for analysis
constraintAdjacencyHist = []; % log constraint adjacency matrix at each timestep
end end
properties (Access = private) properties (Access = private)
@@ -67,7 +67,6 @@ classdef miSim
obj (1, 1) miSim obj (1, 1) miSim
end end
obj.domain = rectangularPrism; obj.domain = rectangularPrism;
obj.objective = sensingObjective;
obj.obstacles = {rectangularPrism}; obj.obstacles = {rectangularPrism};
obj.agents = {agent}; obj.agents = {agent};
end end

5
@miSim/run.m
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@@ -34,6 +34,11 @@ function [obj] = run(obj)
obj = obj.lesserNeighbor(); obj = obj.lesserNeighbor();
end end
% Log constraint adjacency for this timestep
if coder.target('MATLAB')
obj.constraintAdjacencyHist(:, :, ii) = obj.constraintAdjacencyMatrix;
end
% Moving % Moving
% Iterate over agents to simulate their unconstrained motion % Iterate over agents to simulate their unconstrained motion
for jj = 1:size(obj.agents, 1) for jj = 1:size(obj.agents, 1)

3
@miSim/teardown.m
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@@ -20,6 +20,7 @@ function obj = teardown(obj)
out.dampingCoeff = obj.dampingCoeff; out.dampingCoeff = obj.dampingCoeff;
out.useDoubleIntegrator = obj.useDoubleIntegrator; out.useDoubleIntegrator = obj.useDoubleIntegrator;
out.useFixedTopology = obj.useFixedTopology; out.useFixedTopology = obj.useFixedTopology;
out.constraintAdjacency = obj.constraintAdjacencyHist(:, :, 1:(end - 1));
for ii = 1:size(obj.agents, 1) for ii = 1:size(obj.agents, 1)
out.agent(ii).pos = squeeze(obj.posHist(ii, 1:(end - 1), 1:3)); out.agent(ii).pos = squeeze(obj.posHist(ii, 1:(end - 1), 1:3));
out.agent(ii).vel = squeeze(obj.velHist(ii, 1:(end - 1), 1:3)); out.agent(ii).vel = squeeze(obj.velHist(ii, 1:(end - 1), 1:3));
@@ -39,11 +40,11 @@ function obj = teardown(obj)
obj.timestepIndex = NaN; obj.timestepIndex = NaN;
obj.maxIter = NaN; obj.maxIter = NaN;
obj.domain = rectangularPrism; obj.domain = rectangularPrism;
obj.objective = sensingObjective;
obj.obstacles = cell(0, 1); obj.obstacles = cell(0, 1);
obj.agents = cell(0, 1); obj.agents = cell(0, 1);
obj.adjacency = NaN; obj.adjacency = NaN;
obj.constraintAdjacencyMatrix = NaN; obj.constraintAdjacencyMatrix = NaN;
obj.constraintAdjacencyHist = [];
obj.partitioning = NaN; obj.partitioning = NaN;
obj.performance = 0; obj.performance = 0;
obj.barrierGain = NaN; obj.barrierGain = NaN;

7
@miSim/validate.m
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@@ -7,11 +7,11 @@ function validate(obj)
%% Communications Network Validators %% Communications Network Validators
if max(conncomp(graph(obj.adjacency))) ~= 1 if max(conncomp(graph(obj.adjacency))) ~= 1
warning("Network is not connected"); error("Network is not connected");
end end
if any(obj.adjacency - obj.constraintAdjacencyMatrix < 0, "all") if any(obj.adjacency - obj.constraintAdjacencyMatrix < 0, "all")
warning("Eliminated network connections that were necessary"); error("Eliminated network connections that were necessary");
end end
%% Obstacle Validators %% Obstacle Validators
@@ -21,9 +21,8 @@ function validate(obj)
P = min(max(obj.agents{kk}.pos, obj.obstacles{jj}.minCorner), obj.obstacles{jj}.maxCorner); P = min(max(obj.agents{kk}.pos, obj.obstacles{jj}.minCorner), obj.obstacles{jj}.maxCorner);
d = obj.agents{kk}.pos - P; d = obj.agents{kk}.pos - P;
if dot(d, d) < obj.agents{kk}.collisionGeometry.radius^2 if dot(d, d) < obj.agents{kk}.collisionGeometry.radius^2
warning("%s colliding with %s by %d", obj.agents{kk}.label, obj.obstacles{jj}.label, dot(d, d) - obj.agents{kk}.collisionGeometry.radius^2); % this will cause quadprog to fail error("%s colliding with %s by %d", obj.agents{kk}.label, obj.obstacles{jj}.label, dot(d, d) - obj.agents{kk}.collisionGeometry.radius^2); % this will cause quadprog to fail
end end
end end
end end
end end

6
@miSim/writeInits.m
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@@ -14,6 +14,8 @@ function writeInits(obj)
comRanges = cellfun(@(x) x.commsGeometry.radius, obj.agents); comRanges = cellfun(@(x) x.commsGeometry.radius, obj.agents);
initialStepSize = cellfun(@(x) x.initialStepSize, obj.agents); initialStepSize = cellfun(@(x) x.initialStepSize, obj.agents);
pos = cell2mat(cellfun(@(x) x.pos, obj.agents, 'UniformOutput', false)); pos = cell2mat(cellfun(@(x) x.pos, obj.agents, '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 % Combine with simulation parameters
inits = struct("timestep", obj.timestep, "maxIter", obj.maxIter, "minAlt", obj.obstacles{end}.maxCorner(3), ... inits = struct("timestep", obj.timestep, "maxIter", obj.maxIter, "minAlt", obj.obstacles{end}.maxCorner(3), ...
@@ -24,7 +26,9 @@ function writeInits(obj)
"useDoubleIntegrator", obj.useDoubleIntegrator, "dampingCoeff", obj.dampingCoeff, ... "useDoubleIntegrator", obj.useDoubleIntegrator, "dampingCoeff", obj.dampingCoeff, ...
"alphaDist", alphaDist, "betaDist", betaDist, "alphaTilt", alphaTilt, "betaTilt", betaTilt, ... "alphaDist", alphaDist, "betaDist", betaDist, "alphaTilt", alphaTilt, "betaTilt", betaTilt, ...
... % ^^^ PARAMETERS ^^^ | vvv STATES vvv ... % ^^^ PARAMETERS ^^^ | vvv STATES vvv
"pos", pos); % still needs obstacle states and objective state "pos", pos, "objectivePos", obj.domain.objective.groundPos, "objectiveSigma", obj.domain.objective.objectiveSigma, ...
"obsMinCorners", obsMinCorners, "obsMaxCorners", obsMaxCorners, ...
"objectiveIntegral", sum(obj.domain.objective.values(:)));
% Save all parameters to output file % Save all parameters to output file
initsFile = strcat(obj.artifactName, "_miSimInits"); initsFile = strcat(obj.artifactName, "_miSimInits");

17
@sensingObjective/initialize.m
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@@ -1,4 +1,4 @@
function obj = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange, sensorPerformanceMinimum) function obj = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange, sensorPerformanceMinimum, objectiveMu, objectiveSigma)
arguments (Input) arguments (Input)
obj (1,1) {mustBeA(obj, "sensingObjective")}; obj (1,1) {mustBeA(obj, "sensingObjective")};
objectiveFunction (1, 1) {mustBeA(objectiveFunction, "function_handle")}; objectiveFunction (1, 1) {mustBeA(objectiveFunction, "function_handle")};
@@ -6,6 +6,8 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
discretizationStep (1, 1) double = 1; discretizationStep (1, 1) double = 1;
protectedRange (1, 1) double = 1; protectedRange (1, 1) double = 1;
sensorPerformanceMinimum (1, 1) double = 1e-6; sensorPerformanceMinimum (1, 1) double = 1e-6;
objectiveMu (:, 2) double = NaN(1, 2);
objectiveSigma (:, 2, 2) double = NaN(1, 2, 2);
end end
arguments (Output) arguments (Output)
obj (1,1) {mustBeA(obj, "sensingObjective")}; obj (1,1) {mustBeA(obj, "sensingObjective")};
@@ -36,9 +38,14 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
obj.values = obj.values ./ max(obj.values, [], "all"); obj.values = obj.values ./ max(obj.values, [], "all");
% store ground position % store ground position
idx = obj.values == 1; idx = obj.values == 1;
obj.groundPos = [obj.X(idx), obj.Y(idx)]; if any(isnan(objectiveMu))
obj.groundPos = obj.groundPos(1, 1:2); % for safety, in case 2 points are maximal (somehow) obj.groundPos = [obj.X(idx), obj.Y(idx)];
obj.groundPos = obj.groundPos(1, 1:2); % for safety, in case 2 points are maximal (somehow)
else
obj.groundPos = objectiveMu;
end
obj.objectiveSigma = objectiveSigma;
assert(domain.distance([obj.groundPos, 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 end

2
@sensingObjective/initializeRandomMvnpdf.m
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@@ -11,7 +11,7 @@ function obj = initializeRandomMvnpdf(obj, domain, discretizationStep, protected
% Set random objective position % Set random objective position
mu = domain.minCorner; mu = domain.minCorner;
while domain.distance(mu) < protectedRange while domain.distance(mu) < protectedRange * 1.01
mu = domain.random(); mu = domain.random();
end end

3
@sensingObjective/sensingObjective.m
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@@ -2,7 +2,8 @@ classdef sensingObjective
% Sensing objective definition parent class % Sensing objective definition parent class
properties (SetAccess = private, GetAccess = public) properties (SetAccess = private, GetAccess = public)
label = ""; label = "";
groundPos = [NaN, NaN]; groundPos = NaN(1, 2);
objectiveSigma = NaN(1, 2, 2);
discretizationStep = NaN; discretizationStep = NaN;
X = []; X = [];
Y = []; Y = [];

2
aerpaw/config/scenario.csv
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@@ -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 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
5, 100, 30.0, 0.1, 2.0, 2.0, 100, 3, "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, 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
2 5 1 100 150 30.0 0.1 2.0 2.0 1 100 1 3 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

2
test/parametricTestSuite.m
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@@ -150,7 +150,7 @@ classdef parametricTestSuite < matlab.unittest.TestCase
end end
% randomly shuffle agents to make the network more interesting (probably) % randomly shuffle agents to make the network more interesting (probably)
agents = agents(randperm(numel(agents))); agents = agents(randperm(numel(agents)));
% Set up obstacles % Set up obstacles
obstacles = cell(params.numObstacles(ii), 1); obstacles = cell(params.numObstacles(ii), 1);

6
util/objectiveFunctionWrapper.m
View File

@@ -4,12 +4,12 @@ function f = objectiveFunctionWrapper(center, sigma)
% composite objectives in particular % composite objectives in particular
arguments (Input) arguments (Input)
center (:, 2) double; center (:, 2) double;
sigma (2, 2) double = eye(2); sigma (:, 2, 2) double = eye(2);
end end
arguments (Output) arguments (Output)
f (1, 1) {mustBeA(f, "function_handle")}; f (1, 1) {mustBeA(f, "function_handle")};
end end
f = @(x,y) sum(cell2mat(arrayfun(@(i) mvnpdf([x(:), y(:)], center(i,:), sigma), 1:size(center,1), "UniformOutput", false)), 2); 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 end