64 Commits

Author SHA1 Message Date
2604711c78 fixed unit tests 2026-01-13 23:16:41 -08:00
bcb3bc3da3 lots of cleanup and simplification in test case construction 2026-01-13 21:17:35 -08:00
08e396c155 demonstrated test iteration over parameters, scales really poorly 2026-01-13 16:37:39 -08:00
df31c2f03c beginning to write parametric tests 2026-01-13 15:15:42 -08:00
ff02e8a1c6 now doing partitioning on every timestep, looks super smooth 2026-01-11 19:09:32 -08:00
2a48b1d469 parameterized variable gradient ascent step size 2026-01-11 18:50:14 -08:00
7ba21fbaa7 fixed cone plotting all the way to ground 2026-01-11 18:43:44 -08:00
40df9059e7 gradient ascent fix 2026-01-11 17:52:21 -08:00
103e8b391b improved gradient ascent test case 2026-01-11 14:41:42 -08:00
796e2f322a fixed performance plotting 2026-01-11 14:41:27 -08:00
ec202d7790 reimplemented gradient ascent as central finite differences method 2026-01-11 12:42:48 -08:00
c47b7229ba a 2026-01-08 19:35:10 -08:00
02189baaab unit test fixes 2026-01-07 12:41:22 -08:00
af6a0447a8 added z component to GA with constant partition 2026-01-07 11:43:14 -08:00
62e015da42 removed extra file 2026-01-07 09:42:09 -08:00
ddecf63d68 added h plots 2026-01-06 21:57:30 -08:00
591430ad8a unit test updates 2026-01-06 20:22:28 -08:00
1e7540226e fixed and verified communications constraint 2026-01-06 12:24:42 -08:00
4fe897455d fixed lesser neighbor algorithm 2026-01-06 10:57:56 -08:00
7d1154d028 cleaned up todo notes 2026-01-02 13:30:40 -08:00
16673a437e project housekeeping 2026-01-02 13:26:35 -08:00
6403e7cbcc removed options for guidance models other than GA 2026-01-01 17:26:15 -08:00
066acd0949 removed collision geometry label input 2026-01-01 17:22:17 -08:00
8dfa0c337a removed agent label input at initialization 2026-01-01 17:16:11 -08:00
492c5c2140 nixed agent index property 2026-01-01 17:02:36 -08:00
06f6af1511 added silent LNA test case 2026-01-01 16:27:28 -08:00
c59b96f547 added agent position trail to plot 2026-01-01 16:06:19 -08:00
4735c2b77b debugging comms constraints 2025-12-31 20:54:01 -08:00
d6a9c4ac06 Added lesser neighbor algorithm and constraints 2025-12-31 19:19:36 -08:00
fa8da50db1 test updates 2025-12-29 17:35:38 -08:00
61cdb96102 added communications geometry 2025-12-27 16:14:44 -08:00
1d11ac4e90 made no plotting flag for better performance and unit testing 2025-12-24 16:20:57 -08:00
843e5ba574 cleanup 2025-12-24 16:01:31 -08:00
50eaad9504 fixed comms LOS obstruction by obstacles 2025-12-24 16:00:42 -08:00
14e372ae55 added domain constraints 2025-12-23 17:22:34 -08:00
8315b6c511 obstacle avoidance 2025-12-23 14:57:13 -08:00
4fa942564a added basic obstacle avoidance test case 2025-12-23 12:37:53 -08:00
6632c9885d fixed minimum agent altitude initial condition 2025-12-23 12:13:15 -08:00
1fa76c7023 added minimum altitude constraint as obstacle 2025-12-23 12:02:40 -08:00
33036c95fd made video writing optional for performance benefits 2025-12-23 11:50:26 -08:00
557d8fe63c t 2025-12-13 12:18:48 -08:00
2cd1bb8659 CA verifying test 2025-12-05 17:52:53 -08:00
06882d2f30 fixed abuse of memory 2025-12-05 17:28:34 -08:00
95ea19e546 fixed guidance only pulling things towards the middle and added CA QP CBF code 2025-12-05 16:04:02 -08:00
96c91c3988 added collision barrier function and gradient 2025-12-04 18:24:49 -08:00
d70781fadc Merge branch 'main' into gradient-ascent 2025-12-04 16:10:13 -08:00
a688e9c285 cleanup 2025-12-04 15:24:11 -08:00
d30fd9ccaa fixed performance plot after 50th timestep 2025-12-01 22:59:35 -08:00
bdd018e566 refactored performance plot data storage 2025-12-01 22:59:35 -08:00
28a6bfe3de gradient ascent works now? 2025-12-01 22:59:35 -08:00
c92ef143d1 added debug visualization for agent GA 2025-12-01 22:59:35 -08:00
6d16dfe974 flawed GA implementation 2025-12-01 22:59:35 -08:00
1e0db2a46c removed early exit from main loop 2025-12-01 22:59:35 -08:00
f9aa2eb9d4 fixed performance plot after 50th timestep 2025-12-01 22:58:38 -08:00
f296fd2803 refactored performance plot data storage 2025-11-30 22:32:17 -08:00
7c87458b66 gradient ascent works now? 2025-11-30 19:08:15 -08:00
4e0f213d0c added debug visualization for agent GA 2025-11-30 11:00:39 -08:00
f9f070e2d0 flawed GA implementation 2025-11-30 09:52:17 -08:00
352d2ed1de removed early exit from main loop 2025-11-25 13:09:33 -08:00
59805dff72 added early exit from main loop for semistable final states 2025-11-25 09:07:02 -08:00
a8380985e1 better sigmoid sensor unit testing 2025-11-25 09:07:02 -08:00
55b69d4e33 added performance plot legend, rolling normalization 2025-11-25 09:07:02 -08:00
779d7d2cc6 fixed issues in sigmoid sensor model causing inverted response (annular partitions) 2025-11-25 09:07:02 -08:00
58d009c8fc fixed initial altitude range 2025-11-25 09:07:02 -08:00
133 changed files with 1668 additions and 441 deletions

3
.gitignore vendored
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@@ -45,3 +45,6 @@ sandbox/*
# Videos
*.mp4
*.avi
# Figures
*.fig

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@@ -1,22 +1,14 @@
classdef agent
properties (SetAccess = private, GetAccess = public)
properties (SetAccess = public, GetAccess = public)
% Identifiers
index = NaN;
label = "";
% Sensor
sensorModel;
sensingLength = 0.05; % length parameter used by sensing function
% Guidance
guidanceModel;
% State
lastPos = NaN(1, 3); % position from previous timestep
pos = NaN(1, 3); % current position
vel = NaN(1, 3); % current velocity
pan = NaN; % pan angle
tilt = NaN; % tilt angle
% Sensor
sensorModel;
% Collision
collisionGeometry;
@@ -25,15 +17,26 @@ classdef agent
fovGeometry;
% Communication
comRange = NaN;
commsGeometry = spherical;
lesserNeighbors = [];
% Performance
performance = 0;
% Plotting
scatterPoints;
plotCommsGeometry = true;
end
properties (SetAccess = private, GetAccess = public)
initialStepSize = NaN;
stepDecayRate = NaN;
end
methods (Access = public)
[obj] = initialize(obj, pos, vel, pan, tilt, collisionGeometry, sensorModel, guidanceModel, comRange, index, label);
[obj] = run(obj, sensingObjective, domain, partitioning);
[obj] = initialize(obj, pos, pan, tilt, collisionGeometry, sensorModel, guidanceModel, comRange, index, label);
[obj] = run(obj, domain, partitioning, t, index, agents);
[partitioning] = partition(obj, agents, objective)
[obj, f] = plot(obj, ind, f);
updatePlots(obj);
end

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@@ -1,33 +1,34 @@
function obj = initialize(obj, pos, vel, pan, tilt, collisionGeometry, sensorModel, guidanceModel, comRange, index, label)
function obj = initialize(obj, pos, collisionGeometry, sensorModel, comRange, maxIter, initialStepSize, label, plotCommsGeometry)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'agent')};
pos (1, 3) double;
vel (1, 3) double;
pan (1, 1) double;
tilt (1, 1) double;
collisionGeometry (1, 1) {mustBeGeometry};
sensorModel (1, 1) {mustBeSensor}
guidanceModel (1, 1) {mustBeA(guidanceModel, 'function_handle')};
comRange (1, 1) double = NaN;
index (1, 1) double = NaN;
sensorModel (1, 1) {mustBeSensor};
comRange (1, 1) double;
maxIter (1, 1) double;
initialStepSize (1, 1) double = 0.2;
label (1, 1) string = "";
plotCommsGeometry (1, 1) logical = false;
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'agent')};
end
obj.pos = pos;
obj.vel = vel;
obj.pan = pan;
obj.tilt = tilt;
obj.collisionGeometry = collisionGeometry;
obj.sensorModel = sensorModel;
obj.guidanceModel = guidanceModel;
obj.comRange = comRange;
obj.index = index;
obj.label = label;
obj.plotCommsGeometry = plotCommsGeometry;
obj.initialStepSize = initialStepSize;
obj.stepDecayRate = obj.initialStepSize / maxIter;
% Initialize performance vector
obj.performance = [0, NaN(1, maxIter), 0];
% Add spherical geometry based on com range
obj.commsGeometry = obj.commsGeometry.initialize(obj.pos, comRange, REGION_TYPE.COMMS, sprintf("%s Comms Geometry", obj.label));
% Initialize FOV cone
obj.fovGeometry = cone;
obj.fovGeometry = obj.fovGeometry.initialize([obj.pos(1:2), 0], tan(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

35
@agent/partition.m Normal file
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@@ -0,0 +1,35 @@
function [partitioning] = partition(obj, agents, objective)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'agent')};
agents (:, 1) {mustBeA(agents, 'cell')};
objective (1, 1) {mustBeA(objective, 'sensingObjective')};
end
arguments (Output)
partitioning (:, :) double;
end
% Assess sensing performance of each agent at each sample point
% in the domain
agentPerformances = cellfun(@(x) reshape(x.sensorModel.sensorPerformance(x.pos, [objective.X(:), objective.Y(:), zeros(size(objective.X(:)))]), size(objective.X)), agents, 'UniformOutput', false);
agentPerformances{end + 1} = objective.sensorPerformanceMinimum * ones(size(agentPerformances{end})); % add additional layer to represent the threshold that has to be cleared for assignment to any partiton
agentPerformances = cat(3, agentPerformances{:});
% Get highest performance value at each point
[~, idx] = max(agentPerformances, [], 3);
% Collect agent indices in the same way as performance
indices = 1:size(agents, 1);
agentInds = squeeze(tensorprod(indices, ones(size(objective.X))));
if size(agentInds, 1) ~= size(agents, 1)
agentInds = reshape(agentInds, [size(agents, 1), size(agentInds)]); % needed for cases with 1 agent where prior squeeze is too agressive
end
agentInds = num2cell(agentInds, 2:3);
agentInds = cellfun(@(x) squeeze(x), agentInds, 'UniformOutput', false);
agentInds{end + 1} = zeros(size(agentInds{end})); % index for no assignment
agentInds = cat(3, agentInds{:});
% Use highest performing agent's index to form partitions
[m, n, ~] = size(agentInds);
[jj, kk] = ndgrid(1:m, 1:n);
partitioning = agentInds(sub2ind(size(agentInds), jj, kk, idx));
end

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@@ -30,6 +30,12 @@ function [obj, f] = plot(obj, ind, f)
% Plot collision geometry
[obj.collisionGeometry, f] = obj.collisionGeometry.plotWireframe(ind, f);
% Plot communications geometry
if obj.plotCommsGeometry
[obj.commsGeometry, f] = obj.commsGeometry.plotWireframe(ind, f);
end
% Plot FOV geometry
[obj.fovGeometry, f] = obj.fovGeometry.plot(ind, f);
maxAlt = f.Children(1).Children(end).ZLim(2); % to avoid scaling the FOV geometry as the sim runs, let's just make it really big and hide the excess under the floor of the domain. Check the domain altitude to figure out how big it needs to be to achieve this deception.
[obj.fovGeometry, f] = obj.fovGeometry.plot(ind, f, maxAlt);
end

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@@ -1,28 +1,92 @@
function obj = run(obj, sensingObjective, domain, partitioning)
function obj = run(obj, domain, partitioning, timestepIndex, index, agents)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'agent')};
sensingObjective (1, 1) {mustBeA(sensingObjective, 'sensingObjective')};
domain (1, 1) {mustBeGeometry};
partitioning (:, :) double;
timestepIndex (1, 1) double;
index (1, 1) double;
agents (:, 1) {mustBeA(agents, 'cell')};
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'agent')};
end
% Do sensing
[sensedValues, sensedPositions] = obj.sensorModel.sense(obj, sensingObjective, domain, partitioning);
% Collect objective function values across partition
partitionMask = partitioning == index;
if ~unique(partitionMask)
% This agent has no partition, maintain current state
return;
end
objectiveValues = domain.objective.values(partitionMask); % f(omega) on W_n
% Determine next planned position
nextPos = obj.guidanceModel(sensedValues, sensedPositions, obj.pos);
% Compute sensor performance on partition
maskedX = domain.objective.X(partitionMask);
maskedY = domain.objective.Y(partitionMask);
% Compute agent performance at the current position and each delta position +/- X, Y, Z
delta = domain.objective.discretizationStep; % smallest possible step size that gets different results
deltaApplicator = [0, 0, 0; 1, 0, 0; -1, 0, 0; 0, 1, 0; 0, -1, 0; 0, 0, 1; 0, 0, -1]; % none, +X, -X, +Y, -Y, +Z, -Z
C_delta = NaN(7, 1); % agent performance at delta steps in each direction
for ii = 1:7
% Apply delta to position
pos = obj.pos + delta * deltaApplicator(ii, 1:3);
% Compute performance values on partition
if ii < 5
% Compute sensing performance
sensorValues = obj.sensorModel.sensorPerformance(pos, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
% Objective performance does not change for 0, +/- X, Y steps.
% Those values are computed once before the loop and are only
% recomputed when +/- Z steps are applied
else
% Redo partitioning for Z stepping only
partitioning = obj.partition(agents, domain.objective);
% Recompute partiton-derived performance values for objective
partitionMask = partitioning == index;
objectiveValues = domain.objective.values(partitionMask); % f(omega) on W_n
% Recompute partiton-derived performance values for sensing
maskedX = domain.objective.X(partitionMask);
maskedY = domain.objective.Y(partitionMask);
sensorValues = obj.sensorModel.sensorPerformance(pos, [maskedX, maskedY, zeros(size(maskedX))]); % S_n(omega, P_n) on W_n
end
% Rearrange data into image arrays
F = NaN(size(partitionMask));
F(partitionMask) = objectiveValues;
S = NaN(size(partitionMask));
S(partitionMask) = sensorValues;
% Compute agent performance
C = S .* F;
C_delta(ii) = sum(C(~isnan(C)));
end
% Store agent performance at current time and place
obj.performance(timestepIndex + 1) = C_delta(1);
% Compute gradient by finite central differences
gradC = [(C_delta(2)-C_delta(3))/(2*delta), (C_delta(4)-C_delta(5))/(2*delta), (C_delta(6)-C_delta(7))/(2*delta)];
% Compute scaling factor
targetRate = obj.initialStepSize - obj.stepDecayRate * timestepIndex; % slow down as you get closer
rateFactor = targetRate / norm(gradC);
% Compute unconstrained next position
pNext = obj.pos + rateFactor * gradC;
% Move to next position
% (dynamics not modeled at this time)
obj.lastPos = obj.pos;
obj.pos = nextPos;
% Calculate movement
d = obj.pos - obj.collisionGeometry.center;
obj.pos = pNext;
% Reinitialize collision geometry in the new position
d = obj.pos - obj.collisionGeometry.center;
if isa(obj.collisionGeometry, 'rectangularPrism')
obj.collisionGeometry = obj.collisionGeometry.initialize([obj.collisionGeometry.minCorner; obj.collisionGeometry.maxCorner] + d, obj.collisionGeometry.tag, obj.collisionGeometry.label);
elseif isa(obj.collisionGeometry, 'spherical')
obj.collisionGeometry = obj.collisionGeometry.initialize(obj.collisionGeometry.center + d, obj.collisionGeometry.radius, obj.collisionGeometry.tag, obj.collisionGeometry.label);
else
error("?");
end
end

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@@ -5,6 +5,13 @@ function updatePlots(obj)
arguments (Output)
end
% Find change in agent position since last timestep
deltaPos = obj.pos - obj.lastPos;
if all(isnan(deltaPos)) || all(deltaPos == zeros(1, 3))
% Agent did not move, so nothing has to move on the plots
return;
end
% Scatterplot point positions
for ii = 1:size(obj.scatterPoints, 1)
obj.scatterPoints(ii).XData = obj.pos(1);
@@ -12,9 +19,6 @@ function updatePlots(obj)
obj.scatterPoints(ii).ZData = obj.pos(3);
end
% Find change in agent position since last timestep
deltaPos = obj.pos - obj.lastPos;
% Collision geometry edges
for jj = 1:size(obj.collisionGeometry.lines, 2)
% Update plotting
@@ -25,9 +29,21 @@ function updatePlots(obj)
end
end
% Communications geometry edges
if obj.plotCommsGeometry
for jj = 1:size(obj.commsGeometry.lines, 2)
for ii = 1:size(obj.collisionGeometry.lines(:, jj), 1)
obj.collisionGeometry.lines(ii, jj).XData = obj.collisionGeometry.lines(ii, jj).XData + deltaPos(1);
obj.collisionGeometry.lines(ii, jj).YData = obj.collisionGeometry.lines(ii, jj).YData + deltaPos(2);
obj.collisionGeometry.lines(ii, jj).ZData = obj.collisionGeometry.lines(ii, jj).ZData + deltaPos(3);
end
end
end
% Update FOV geometry surfaces
for jj = 1:size(obj.fovGeometry.surface, 2)
% Update each plot
% obj.fovGeometry = obj.fovGeometry.plot(obj.spatialPlotIndices)
obj.fovGeometry.surface(jj).XData = obj.fovGeometry.surface(jj).XData + deltaPos(1);
obj.fovGeometry.surface(jj).YData = obj.fovGeometry.surface(jj).YData + deltaPos(2);
obj.fovGeometry.surface(jj).ZData = obj.fovGeometry.surface(jj).ZData + deltaPos(3);

154
@miSim/constrainMotion.m Normal file
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@@ -0,0 +1,154 @@
function [obj] = constrainMotion(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
if size(obj.agents, 1) < 2
nAAPairs = 0;
else
nAAPairs = nchoosek(size(obj.agents, 1), 2); % unique agent/agent pairs
end
agents = [obj.agents{:}];
v = reshape(([agents.pos] - [agents.lastPos])./obj.timestep, 3, size(obj.agents, 1))';
if all(isnan(v), 'all') || all(v == zeros(size(obj.agents, 1), 3), 'all')
% Agents are not attempting to move, so there is no motion to be
% constrained
return;
end
% Initialize QP based on number of agents and obstacles
nAOPairs = size(obj.agents, 1) * size(obj.obstacles, 1); % unique agent/obstacle pairs
nADPairs = size(obj.agents, 1) * 5; % agents x (4 walls + 1 ceiling)
nLNAPairs = sum(obj.constraintAdjacencyMatrix, 'all') - size(obj.agents, 1);
total = nAAPairs + nAOPairs + nADPairs + nLNAPairs;
kk = 1;
A = zeros(total, 3 * size(obj.agents, 1));
b = zeros(total, 1);
% Set up collision avoidance constraints
h = NaN(size(obj.agents, 1));
h(logical(eye(size(obj.agents, 1)))) = 0; % self value is 0
for ii = 1:(size(obj.agents, 1) - 1)
for jj = (ii + 1):size(obj.agents, 1)
h(ii, jj) = norm(agents(ii).pos - agents(jj).pos)^2 - (agents(ii).collisionGeometry.radius + agents(jj).collisionGeometry.radius)^2;
h(jj, ii) = h(ii, jj);
A(kk, (3 * ii - 2):(3 * ii)) = -2 * (agents(ii).pos - agents(jj).pos);
A(kk, (3 * jj - 2):(3 * jj)) = -A(kk, (3 * ii - 2):(3 * ii));
b(kk) = obj.barrierGain * h(ii, jj)^obj.barrierExponent;
kk = kk + 1;
end
end
hObs = NaN(size(obj.agents, 1), size(obj.obstacles, 1));
% Set up obstacle avoidance constraints
for ii = 1:size(obj.agents, 1)
for jj = 1:size(obj.obstacles, 1)
% find closest position to agent on/in obstacle
cPos = obj.obstacles{jj}.closestToPoint(agents(ii).pos);
hObs(ii, jj) = dot(agents(ii).pos - cPos, agents(ii).pos - cPos) - agents(ii).collisionGeometry.radius^2;
A(kk, (3 * ii - 2):(3 * ii)) = -2 * (agents(ii).pos - cPos);
b(kk) = obj.barrierGain * hObs(ii, jj)^obj.barrierExponent;
kk = kk + 1;
end
end
% Set up domain constraints (walls and ceiling only)
% Floor constraint is implicit with an obstacle corresponding to the
% minimum allowed altitude, but I included it anyways
for ii = 1:size(obj.agents, 1)
% X minimum
h_xMin = (agents(ii).pos(1) - obj.domain.minCorner(1)) - agents(ii).collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [-1, 0, 0];
b(kk) = obj.barrierGain * h_xMin^obj.barrierExponent;
kk = kk + 1;
% X maximum
h_xMax = (obj.domain.maxCorner(1) - agents(ii).pos(1)) - agents(ii).collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [1, 0, 0];
b(kk) = obj.barrierGain * h_xMax^obj.barrierExponent;
kk = kk + 1;
% Y minimum
h_yMin = (agents(ii).pos(2) - obj.domain.minCorner(2)) - agents(ii).collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [0, -1, 0];
b(kk) = obj.barrierGain * h_yMin^obj.barrierExponent;
kk = kk + 1;
% Y maximum
h_yMax = (obj.domain.maxCorner(2) - agents(ii).pos(2)) - agents(ii).collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [0, 1, 0];
b(kk) = obj.barrierGain * h_yMax^obj.barrierExponent;
kk = kk + 1;
% Z minimum
h_zMin = (agents(ii).pos(3) - obj.domain.minCorner(3)) - agents(ii).collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [0, 0, -1];
b(kk) = obj.barrierGain * h_zMin^obj.barrierExponent;
kk = kk + 1;
% Z maximum
h_zMax = (obj.domain.maxCorner(2) - agents(ii).pos(2)) - agents(ii).collisionGeometry.radius;
A(kk, (3 * ii - 2):(3 * ii)) = [0, 0, 1];
b(kk) = obj.barrierGain * h_zMax^obj.barrierExponent;
kk = kk + 1;
end
% Save off h function values (ignoring network constraints which may evolve in time)
obj.h(:, obj.timestepIndex) = [h(triu(true(size(obj.agents, 1)), 1)); reshape(hObs, [], 1); h_xMin; h_xMax; h_yMin; h_yMax; h_zMin; h_zMax;];
% Add communication network constraints
hComms = NaN(size(obj.agents, 1));
hComms(logical(eye(size(obj.agents, 1)))) = 0;
for ii = 1:(size(obj.agents, 1) - 1)
for jj = (ii + 1):size(obj.agents, 1)
if obj.constraintAdjacencyMatrix(ii, jj)
hComms(ii, jj) = min([obj.agents{ii}.commsGeometry.radius, obj.agents{jj}.commsGeometry.radius])^2 - norm(agents(ii).pos - agents(jj).pos)^2;
A(kk, (3 * ii - 2):(3 * ii)) = 2 * (agents(ii).pos - agents(jj).pos);
A(kk, (3 * jj - 2):(3 * jj)) = -A(kk, (3 * ii - 2):(3 * ii));
b(kk) = obj.barrierGain * hComms(ii, jj)^obj.barrierExponent;
kk = kk + 1;
end
end
end
% Solve QP program generated earlier
vhat = reshape(v', 3 * size(obj.agents, 1), 1);
H = 2 * eye(3 * size(obj.agents, 1));
f = -2 * vhat;
% Update solution based on constraints
assert(size(A,2) == size(H,1))
assert(size(A,1) == size(b,1))
assert(size(H,1) == length(f))
opt = optimoptions('quadprog', 'Display', 'off');
[vNew, ~, exitflag, m] = quadprog(sparse(H), double(f), A, b, [],[], [], [], [], opt);
assert(exitflag == 1, sprintf('quadprog failure... %s%s', newline, m.message));
vNew = reshape(vNew, 3, size(obj.agents, 1))';
if exitflag <= 0
warning("QP failed, continuing with unconstrained solution...")
vNew = v;
end
% Update the "next position" that was previously set by unconstrained
% GA using the constrained solution produced here
for ii = 1:size(vNew, 1)
obj.agents{ii}.pos = obj.agents{ii}.lastPos + vNew(ii, :) * obj.timestep;
end
% Here we run this at the simulation level, but in reality there is no
% parent level, so this would be run independently on each agent.
% Running at the simulation level is just meant to simplify the
% simulation
end

View File

@@ -1,49 +1,97 @@
function obj = initialize(obj, domain, objective, agents, timestep, partitoningFreq, maxIter, obstacles)
function [obj] = initialize(obj, domain, agents, barrierGain, barrierExponent, minAlt, timestep, maxIter, obstacles, makePlots, makeVideo)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
domain (1, 1) {mustBeGeometry};
objective (1, 1) {mustBeA(objective, 'sensingObjective')};
agents (:, 1) cell;
barrierGain (1, 1) double = 100;
barrierExponent (1, 1) double = 3;
minAlt (1, 1) double = 1;
timestep (:, 1) double = 0.05;
partitoningFreq (:, 1) double = 0.25
maxIter (:, 1) double = 1000;
obstacles (:, 1) cell {mustBeGeometry} = cell(0, 1);
makePlots(1, 1) logical = true;
makeVideo (1, 1) logical = true;
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
% enable/disable plotting and video writer
obj.makePlots = makePlots;
if ~obj.makePlots
if makeVideo
warning("makeVideo set to true, but makePlots set to false. Setting makeVideo to false.");
makeVideo = false;
end
end
obj.makeVideo = makeVideo;
% Generate artifact(s) name
obj.artifactName = strcat(string(datetime('now'), 'yyyy_MM_dd_HH_mm_ss'));
% Define simulation time parameters
obj.timestep = timestep;
obj.timestepIndex = 0;
obj.maxIter = maxIter - 1;
% Define domain
obj.domain = domain;
obj.partitioningFreq = partitoningFreq;
% Add geometries representing obstacles within the domain
obj.obstacles = obstacles;
% Define objective
obj.objective = objective;
% Add an additional obstacle spanning the domain's footprint to
% represent the minimum allowable altitude
if minAlt > 0
obj.obstacles{end + 1, 1} = rectangularPrism;
obj.obstacles{end, 1} = obj.obstacles{end, 1}.initialize([obj.domain.minCorner; obj.domain.maxCorner(1:2), minAlt], "OBSTACLE", "Minimum Altitude Domain Constraint");
end
% Define agents
obj.agents = agents;
obj.constraintAdjacencyMatrix = logical(eye(size(agents, 1)));
% Compute adjacency matrix
% Set labels for agents and collision geometries in cases where they
% were not provieded at the time of their initialization
for ii = 1:size(obj.agents, 1)
% Agent
if isempty(char(obj.agents{ii}.label))
obj.agents{ii}.label = sprintf("Agent %d", ii);
end
% Collision geometry
if isempty(char(obj.agents{ii}.collisionGeometry.label))
obj.agents{ii}.collisionGeometry.label = sprintf("Agent %d Collision Geometry", ii);
end
end
% Set CBF parameters
obj.barrierGain = barrierGain;
obj.barrierExponent = barrierExponent;
% Compute adjacency matrix and lesser neighbors
obj = obj.updateAdjacency();
obj = obj.lesserNeighbor();
% Set up times to iterate over
obj.times = linspace(0, obj.timestep * obj.maxIter, obj.maxIter+1)';
obj.partitioningTimes = obj.times(obj.partitioningFreq:obj.partitioningFreq:size(obj.times, 1));
% Prepare performance data store (at t = 0, all have 0 performance)
obj.fPerf = figure;
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));
% Create initial partitioning
obj = obj.partition();
obj.partitioning = obj.agents{1}.partition(obj.agents, obj.domain.objective);
% Initialize variable that will store agent positions for trail plots
obj.posHist = NaN(size(obj.agents, 1), obj.maxIter + 1, 3);
obj.posHist(1:size(obj.agents, 1), 1, 1:3) = reshape(cell2mat(cellfun(@(x) x.pos, obj.agents, 'UniformOutput', false)), size(obj.agents, 1), 1, 3);
% Set up plots showing initialized state
obj = obj.plot();
% Run validations
obj.validate();
end

76
@miSim/lesserNeighbor.m Normal file
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@@ -0,0 +1,76 @@
function obj = lesserNeighbor(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
% initialize solution with self-connections only
constraintAdjacencyMatrix = logical(eye(size(obj.agents, 1)));
for ii = 1:size(obj.agents, 1)
% Find lesser neighbors of each agent
% Lesser neighbors of ii are jj < ii in range of ii
lesserNeighbors = [];
for jj = 1:(ii - 1)
if obj.adjacency(ii, jj)
lesserNeighbors = [lesserNeighbors, jj];
end
end
obj.agents{ii}.lesserNeighbors = lesserNeighbors;
% Early exit for isolated agents
if isempty(obj.agents{ii}.lesserNeighbors)
continue
end
% Focus on subgraph defined by lesser neighbors
subgraphAdjacency = obj.adjacency(obj.agents{ii}.lesserNeighbors, obj.agents{ii}.lesserNeighbors);
% Find connected components in each agent's subgraph
% TODO: rewrite this using matlab "conncomp" function?
visited = false(size(subgraphAdjacency, 1), 1);
components = {};
for jj = 1:size(subgraphAdjacency, 1)
if ~visited(jj)
reachable = bfs(subgraphAdjacency, jj);
visited(reachable) = true;
components{end+1} = obj.agents{ii}.lesserNeighbors(reachable);
end
end
% Connect to the greatest index in each connected component in the
% lesser neighborhood of this agent
for jj = 1:size(components, 2)
constraintAdjacencyMatrix(ii, max(components{jj})) = true;
constraintAdjacencyMatrix(max(components{jj}), ii) = true;
end
end
obj.constraintAdjacencyMatrix = constraintAdjacencyMatrix | constraintAdjacencyMatrix';
end
function cComp = bfs(subgraphAdjacency, startIdx)
n = size(subgraphAdjacency, 1);
visited = false(1, n);
queue = startIdx;
cComp = startIdx;
visited(startIdx) = true;
while ~isempty(queue)
current = queue(1);
queue(1) = [];
% Find all neighbors of current node in the subgraph
neighbors = find(subgraphAdjacency(current, :));
for neighbor = neighbors
if ~visited(neighbor)
visited(neighbor) = true;
cComp = [cComp, neighbor];
queue = [queue, neighbor];
end
end
end
cComp = sort(cComp);
end

View File

@@ -4,51 +4,71 @@ classdef miSim
% Simulation parameters
properties (SetAccess = private, GetAccess = public)
timestep = NaN; % delta time interval for simulation iterations
partitioningFreq = NaN; % number of simulation timesteps at which the partitioning routine is re-run
timestepIndex = NaN; % index of the current timestep (useful for time-indexed arrays)
maxIter = NaN; % maximum number of simulation iterations
domain = rectangularPrism;
objective = sensingObjective;
obstacles = cell(0, 1); % geometries that define obstacles within the domain
agents = cell(0, 1); % agents that move within the domain
adjacency = NaN; % Adjacency matrix representing communications network graph
sensorPerformanceMinimum = 1e-6; % minimum sensor performance to allow assignment of a point in the domain to a partition
constraintAdjacencyMatrix = NaN; % Adjacency matrix representing desired lesser neighbor connections
partitioning = NaN;
performance = NaN; % current cumulative sensor performance
perf; % sensor performance timeseries array
performance = 0; % simulation performance timeseries vector
barrierGain = 100; % CBF gain parameter
barrierExponent = 3; % CBF exponent parameter
artifactName = "";
fPerf; % performance plot figure
end
properties (Access = private)
% Sim
t = NaN; % current sim time
perf; % sensor performance timeseries array
times;
partitioningTimes;
% Plot objects
f = firstPlotSetup(); % main plotting tiled layout figure
makePlots = true; % enable/disable simulation plotting (performance implications)
makeVideo = true; % enable/disable VideoWriter (performance implications)
f; % main plotting tiled layout figure
connectionsPlot; % objects for lines connecting agents in spatial plots
graphPlot; % objects for abstract network graph plot
partitionPlot; % objects for partition plot
fPerf; % performance plot figure
performancePlot; % objects for sensor performance plot
posHist; % data for trail plot
trailPlot; % objects for agent trail plot
% Indicies for various plot types in the main tiled layout figure
spatialPlotIndices = [6, 4, 3, 2];
objectivePlotIndices = [6, 4];
networkGraphIndex = 5;
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
domPlot; % objects for domain h function plot
end
methods (Access = public)
[obj] = initialize(obj, domain, objective, agents, timestep, partitoningFreq, maxIter, obstacles);
[obj] = initialize(obj, domain, agents, barrierGain, barrierExponent, minAlt, timestep, maxIter, obstacles, makePlots, makeVideo);
[obj] = run(obj);
[obj] = lesserNeighbor(obj);
[obj] = constrainMotion(obj);
[obj] = partition(obj);
[obj] = updateAdjacency(obj);
[obj] = plot(obj);
[obj] = plotConnections(obj);
[obj] = plotPartitions(obj);
[obj] = plotGraph(obj);
[obj] = updatePlots(obj, updatePartitions);
[obj] = plotTrails(obj);
[obj] = plotH(obj);
[obj] = updatePlots(obj);
validate(obj);
teardown(obj);
end
methods (Access = private)
[v] = setupVideoWriter(obj);

View File

@@ -1,36 +0,0 @@
function obj = partition(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
% Assess sensing performance of each agent at each sample point
% in the domain
agentPerformances = cellfun(@(x) reshape(x.sensorModel.sensorPerformance(x.pos, x.pan, x.tilt, [obj.objective.X(:), obj.objective.Y(:), zeros(size(obj.objective.X(:)))]), size(obj.objective.X)), obj.agents, 'UniformOutput', false);
agentPerformances{end + 1} = obj.sensorPerformanceMinimum * ones(size(agentPerformances{end})); % add additional layer to represent the threshold that has to be cleared for assignment to any partiton
agentPerformances = cat(3, agentPerformances{:});
% Get highest performance value at each point
[~, idx] = max(agentPerformances, [], 3);
% Collect agent indices in the same way as performance
agentInds = cellfun(@(x) x.index * ones(size(obj.objective.X)), obj.agents, 'UniformOutput', false);
agentInds{end + 1} = zeros(size(agentInds{end})); % index for no assignment
agentInds = cat(3, agentInds{:});
% Get highest performing agent's index
[m,n,~] = size(agentInds);
[jj,kk] = ndgrid(1:m, 1:n);
obj.partitioning = agentInds(sub2ind(size(agentInds), jj, kk, idx));
% Get individual agent sensor performance
nowIdx = [0; obj.partitioningTimes] == obj.t;
for ii = 1:size(obj.agents, 1)
obj.perf(ii, nowIdx) = sum(agentPerformances(sub2ind(size(agentInds), jj, kk, ii)), 'all');
end
% Current total performance
obj.perf(end, nowIdx) = sum(obj.perf(1:(end - 1), nowIdx));
end

View File

@@ -6,6 +6,11 @@ function obj = plot(obj)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
% fast exit when plotting is disabled
if ~obj.makePlots
return;
end
% Plot domain
[obj.domain, obj.f] = obj.domain.plotWireframe(obj.spatialPlotIndices);
@@ -17,7 +22,7 @@ function obj = plot(obj)
% Plot objective gradient
obj.f = obj.domain.objective.plot(obj.objectivePlotIndices, obj.f);
% Plot agents and their collision geometries
% Plot agents and their collision/communications geometries
for ii = 1:size(obj.agents, 1)
[obj.agents{ii}, obj.f] = obj.agents{ii}.plot(obj.spatialPlotIndices, obj.f);
end
@@ -31,6 +36,9 @@ function obj = plot(obj)
% Plot domain partitioning
obj = obj.plotPartitions();
% Plot agent trails
obj = obj.plotTrails();
% Enforce plot limits
for ii = 1:size(obj.spatialPlotIndices, 2)
xlim(obj.f.Children(1).Children(obj.spatialPlotIndices(ii)), [obj.domain.minCorner(1), obj.domain.maxCorner(1)]);
@@ -40,4 +48,10 @@ function obj = plot(obj)
% Plot performance
obj = obj.plotPerformance();
% Plot h functions
obj = obj.plotH();
% Switch back to primary figure
figure(obj.f);
end

View File

@@ -9,9 +9,9 @@ function obj = plotConnections(obj)
% Iterate over lower triangle off-diagonal region of the
% adjacency matrix to plot communications links between agents
X = []; Y = []; Z = [];
for ii = 2:size(obj.adjacency, 1)
for ii = 2:size(obj.constraintAdjacencyMatrix, 1)
for jj = 1:(ii - 1)
if obj.adjacency(ii, jj)
if obj.constraintAdjacencyMatrix(ii, jj)
X = [X; obj.agents{ii}.pos(1), obj.agents{jj}.pos(1)];
Y = [Y; obj.agents{ii}.pos(2), obj.agents{jj}.pos(2)];
Z = [Z; obj.agents{ii}.pos(3), obj.agents{jj}.pos(3)];

View File

@@ -7,7 +7,7 @@ function obj = plotGraph(obj)
end
% Form graph from adjacency matrix
G = graph(obj.adjacency, 'omitselfloops');
G = graph(obj.constraintAdjacencyMatrix, 'omitselfloops');
% Plot graph object
if isnan(obj.networkGraphIndex)

61
@miSim/plotH.m Normal file
View File

@@ -0,0 +1,61 @@
function obj = plotH(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
obj.hf = figure;
tiledlayout(obj.hf, 4, 1, "TileSpacing", "tight", "Padding", "compact");
nexttile(obj.hf.Children(1));
axes(obj.hf.Children(1).Children(1));
grid(obj.hf.Children(1).Children(1), "on");
xlabel(obj.hf.Children(1).Children(1), "Time (s)"); % ylabel(obj.hf.Children(1).Children(1), "");
title(obj.hf.Children(1).Children(1), "Collision Avoidance");
hold(obj.hf.Children(1).Children(1), "on");
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)
legendStrings = [legendStrings; sprintf("A%d A%d", jj, ii)];
end
end
legend(obj.hf.Children(1).Children(1), legendStrings, 'Location', 'bestoutside');
hold(obj.hf.Children(1).Children(2), "off");
nexttile(obj.hf.Children(1));
axes(obj.hf.Children(1).Children(1));
grid(obj.hf.Children(1).Children(1), "on");
xlabel(obj.hf.Children(1).Children(1), "Time (s)"); % ylabel(obj.hf.Children(1).Children(2), "");
title(obj.hf.Children(1).Children(1), "Obstacles");
hold(obj.hf.Children(1).Children(1), "on");
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)
legendStrings = [legendStrings; sprintf("A%d O%d", jj, ii)];
end
end
legend(obj.hf.Children(1).Children(1), legendStrings, 'Location', 'bestoutside');
hold(obj.hf.Children(1).Children(2), "off");
nexttile(obj.hf.Children(1));
axes(obj.hf.Children(1).Children(1));
grid(obj.hf.Children(1).Children(1), "on");
xlabel(obj.hf.Children(1).Children(1), "Time (s)"); % ylabel(obj.hf.Children(1).Children(1), "");
title(obj.hf.Children(1).Children(1), "Domain");
hold(obj.hf.Children(1).Children(1), "on");
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));
axes(obj.hf.Children(1).Children(1));
grid(obj.hf.Children(1).Children(1), "on");
xlabel(obj.hf.Children(1).Children(1), "Time (s)"); % ylabel(obj.hf.Children(1).Children(1), "");
title(obj.hf.Children(1).Children(1), "Communications");
% skipped this for now because it is very complicated
end

View File

@@ -6,6 +6,13 @@ function obj = plotPerformance(obj)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
% fast exit when plotting is disabled
if ~obj.makePlots
return;
end
obj.fPerf = figure;
axes(obj.fPerf);
title(obj.fPerf.Children(1), "Sensor Performance");
xlabel(obj.fPerf.Children(1), 'Time (s)');
@@ -15,14 +22,24 @@ function obj = plotPerformance(obj)
% Plot current cumulative performance
hold(obj.fPerf.Children(1), 'on');
o = plot(obj.fPerf.Children(1), obj.perf(end, :));
warning('off', 'MATLAB:gui:array:InvalidArrayShape'); % suppress this warning to avoid polluting output
o.XData = NaN(1, obj.maxIter); % correct time will be set at runtime
o.YData = [0, NaN(1, obj.maxIter - 1)];
hold(obj.fPerf.Children(1), 'off');
% Plot current agent performance
for ii = 1:(size(obj.perf, 1) - 1)
hold(obj.fPerf.Children(1), 'on');
o = [o; plot(obj.fPerf.Children(1), obj.perf(ii, :))];
o(end).XData = NaN(1, obj.maxIter); % correct time will be set at runtime
o(end).YData = [0, NaN(1, obj.maxIter - 1)];
hold(obj.fPerf.Children(1), 'off');
end
% Add legend
agentStrings = string(cellfun(@(x) x.label, obj.agents, 'UniformOutput', false));
agentStrings = ["Total"; agentStrings];
legend(obj.fPerf.Children(1), agentStrings, 'Location', 'northwest');
obj.performancePlot = o;
end

26
@miSim/plotTrails.m Normal file
View File

@@ -0,0 +1,26 @@
function obj = plotTrails(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')}
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'miSim')}
end
% fast exit when plotting is disabled
if ~obj.makePlots
return;
end
% Plot full range of position history on each spatial plot axes
o = [];
for ii = 1:(size(obj.posHist, 1))
hold(obj.f.Children(1).Children(obj.spatialPlotIndices(1)), 'on');
o = [o; plot3(obj.f.Children(1).Children(obj.spatialPlotIndices(1)), obj.posHist(ii, 1:obj.maxIter, 1), obj.posHist(ii, 1:obj.maxIter, 2), obj.posHist(ii, 1:obj.maxIter, 3), 'Color', 'k', 'LineWidth', 1)];
hold(obj.f.Children(1).Children(obj.spatialPlotIndices(1)), 'off');
end
% Copy trails to other figures?
obj.trailPlot = o;
% Add legend?
end

View File

@@ -7,37 +7,60 @@ function [obj] = run(obj)
end
% Start video writer
if obj.makeVideo
v = obj.setupVideoWriter();
v.open();
end
for ii = 1:size(obj.times, 1)
% Display current sim time
obj.t = obj.times(ii);
obj.timestepIndex = ii;
fprintf("Sim Time: %4.2f (%d/%d)\n", obj.t, ii, obj.maxIter + 1);
% Check if it's time for new partitions
updatePartitions = false;
if ismember(obj.t, obj.partitioningTimes)
updatePartitions = true;
obj = obj.partition();
% Before moving
% Validate current simulation configuration
obj.validate();
% Update partitioning before moving (this one is strictly for
% plotting purposes, the real partitioning is done by the agents)
obj.partitioning = obj.agents{1}.partition(obj.agents, obj.domain.objective);
% Determine desired communications links
obj = obj.lesserNeighbor();
% Moving
% Iterate over agents to simulate their unconstrained motion
for jj = 1:size(obj.agents, 1)
obj.agents{jj} = obj.agents{jj}.run(obj.domain, obj.partitioning, obj.timestepIndex, jj, obj.agents);
end
% Iterate over agents to simulate their motion
for jj = 1:size(obj.agents, 1)
obj.agents{jj} = obj.agents{jj}.run(obj.objective, obj.domain, obj.partitioning);
end
% Adjust motion determined by unconstrained gradient ascent using
% CBF constraints solved by QP
obj = constrainMotion(obj);
% After moving
% Update agent position history array
obj.posHist(1:size(obj.agents, 1), obj.timestepIndex + 1, 1:3) = reshape(cell2mat(cellfun(@(x) x.pos, obj.agents, 'UniformOutput', false)), size(obj.agents, 1), 1, 3);
% Update total performance
obj.performance = [obj.performance, sum(cellfun(@(x) x.performance(obj.timestepIndex+1), obj.agents))];
% Update adjacency matrix
obj = obj.updateAdjacency();
% Update plots
obj = obj.updatePlots(updatePartitions);
obj = obj.updatePlots();
% Write frame in to video
if obj.makeVideo
I = getframe(obj.f);
v.writeVideo(I);
end
end
if obj.makeVideo
% Close video file
v.close();
end
end

View File

@@ -7,9 +7,9 @@ function v = setupVideoWriter(obj)
end
if ispc || ismac
v = VideoWriter(fullfile('sandbox', strcat(string(datetime('now'), 'yyyy_MM_dd_HH_mm_ss'), '_miSimHist')), 'MPEG-4');
v = VideoWriter(fullfile(matlab.project.rootProject().RootFolder, 'sandbox', strcat(obj.artifactName, "_miSimHist")), 'MPEG-4');
elseif isunix
v = VideoWriter(fullfile('.', strcat(string(datetime('now'), 'yyyy_MM_dd_HH_mm_ss'), '_miSimHist')), 'Motion JPEG AVI');
v = VideoWriter(fullfile(matlab.project.rootProject().RootFolder, 'sandbox', strcat(obj.artifactName, "_miSimHist")), 'Motion JPEG AVI');
end
v.FrameRate = 1 / obj.timestep;

13
@miSim/teardown.m Normal file
View File

@@ -0,0 +1,13 @@
function teardown(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
arguments (Output)
end
% Close plots
close(obj.hf);
close(obj.fPerf);
close(obj.f);
end

View File

@@ -7,26 +7,18 @@ function obj = updateAdjacency(obj)
end
% Initialize assuming only self-connections
A = logical(eye(size(obj.agents, 1)));
A = true(size(obj.agents, 1));
% Check lower triangle off-diagonal connections
for ii = 2:size(A, 1)
for jj = 1:(ii - 1)
if norm(obj.agents{ii}.pos - obj.agents{jj}.pos) <= min([obj.agents{ii}.comRange, obj.agents{jj}.comRange])
% Make sure that obstacles don't obstruct the line
% of sight, breaking the connection
for kk = 1:size(obj.obstacles, 1)
if ~obj.obstacles{kk}.containsLine(obj.agents{ii}.pos, obj.agents{jj}.pos)
A(ii, jj) = true;
end
end
% need extra handling for cases with no obstacles
if isempty(obj.obstacles)
A(ii, jj) = true;
end
% Check that agents are not out of range
if norm(obj.agents{ii}.pos - obj.agents{jj}.pos) > min([obj.agents{ii}.commsGeometry.radius, obj.agents{jj}.commsGeometry.radius])
A(ii, jj) = false; % comm range violation
continue;
end
end
end
obj.adjacency = A | A';
obj.adjacency = A & A';
end

View File

@@ -1,19 +1,23 @@
function [obj] = updatePlots(obj, updatePartitions)
function [obj] = updatePlots(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
updatePartitions (1, 1) logical = false;
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
% Update agent positions, collision geometries
% Fast exit when plotting is disabled
if ~obj.makePlots
return;
end
% Update agent positions, collision/communication geometries
for ii = 1:size(obj.agents, 1)
obj.agents{ii}.updatePlots();
end
% The remaining updates might be possible to do in a clever way
% that moves existing lines instead of clearing and
% The remaining updates might should all be possible to do in a clever
% way that moves existing lines instead of clearing and
% re-plotting, which is much better for performance boost
% Update agent connections plot
@@ -25,10 +29,8 @@ function [obj] = updatePlots(obj, updatePartitions)
obj = obj.plotGraph();
% Update partitioning plot
if updatePartitions
delete(obj.partitionPlot);
obj = obj.plotPartitions();
end
% reset plot limits to fit domain
for ii = 1:size(obj.spatialPlotIndices, 2)
@@ -36,17 +38,34 @@ function [obj] = updatePlots(obj, updatePartitions)
ylim(obj.f.Children(1).Children(obj.spatialPlotIndices(ii)), [obj.domain.minCorner(2), obj.domain.maxCorner(2)]);
zlim(obj.f.Children(1).Children(obj.spatialPlotIndices(ii)), [obj.domain.minCorner(3), obj.domain.maxCorner(3)]);
end
% Update agent trails
for ii = 1:size(obj.agents, 1)
obj.trailPlot(ii).XData(obj.timestepIndex) = obj.posHist(ii, obj.timestepIndex, 1);
obj.trailPlot(ii).YData(obj.timestepIndex) = obj.posHist(ii, obj.timestepIndex, 2);
obj.trailPlot(ii).ZData(obj.timestepIndex) = obj.posHist(ii, obj.timestepIndex, 3);
end
drawnow;
% Update performance plot
if updatePartitions
nowIdx = [0; obj.partitioningTimes] == obj.t;
% set(obj.performancePlot(1), 'YData', obj.perf(end, 1:find(nowIdx)));
obj.performancePlot(1).YData(nowIdx) = obj.perf(end, nowIdx);
for ii = 2:size(obj.performancePlot, 1)
obj.performancePlot(ii).YData(nowIdx) = obj.perf(ii, nowIdx);
end
drawnow;
% Re-normalize performance plot
normalizingFactor = 1/max(obj.performance);
obj.performancePlot(1).YData(1:(length(obj.performance) + 1)) = [obj.performance, 0] * normalizingFactor;
obj.performancePlot(1).XData([obj.timestepIndex, obj.timestepIndex + 1]) = [obj.t, obj.t + obj.timestep];
for ii = 1:(size(obj.agents, 1))
obj.performancePlot(ii + 1).YData(1:(length(obj.performance) + 1)) = [obj.agents{ii}.performance(1:length(obj.performance)), 0] * normalizingFactor;
obj.performancePlot(ii + 1).XData([obj.timestepIndex, obj.timestepIndex + 1]) = [obj.t, obj.t + obj.timestep];
end
% Update h function plots
for ii = 1:size(obj.caPlot, 1)
obj.caPlot(ii).YData(obj.timestepIndex) = obj.h(ii, obj.timestepIndex);
end
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.h(ii + size(obj.caPlot, 1) + size(obj.obsPlot, 1), obj.timestepIndex);
end
end

27
@miSim/validate.m Normal file
View File

@@ -0,0 +1,27 @@
function validate(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
arguments (Output)
end
%% Communications Network Validators
if max(conncomp(graph(obj.adjacency))) ~= 1
warning("Network is not connected");
end
if any(obj.adjacency - obj.constraintAdjacencyMatrix < 0, 'all')
warning("Eliminated network connections that were necessary");
end
%% Obstacle Validators
AO_collisions = cellfun(@(a) cellfun(@(o) o.contains(a.pos), obj.obstacles), obj.agents, 'UniformOutput', false);
AO_collisions = vertcat(AO_collisions{:});
if any(AO_collisions)
[idx, idy] = find(AO_collisions);
for ii = 1:size(idx, 1)
error("Agent(s) %d colliding with obstacle(s) %d", idx(ii), idy(ii));
end
end
end

25
@miSim/writeParams.m Normal file
View File

@@ -0,0 +1,25 @@
function writeParams(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'miSim')};
end
arguments (Output)
end
% 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.alphaDist, obj.agents);
comRange = cellfun(@(x) x.commsGeometry.radius, obj.agents);
% Combine with simulation parameters
params = struct('timestep', obj.timestep, 'maxIter', obj.maxIter, 'minAlt', obj.obstacles{end}.maxCorner(3), 'discretizationStep', obj.domain.objective.discretizationStep, ...
'collisionRadius', collisionRadii, 'alphaDist', alphaDist, 'betaDist', betaDist, ...
'alphaTilt', alphaTilt, 'betaTilt', betaTilt, 'comRange', comRange);
% Save all parameters to output file
paramsFile = strcat(obj.artifactName, "_miSimParams");
paramsFile = fullfile(matlab.project.rootProject().RootFolder, 'sandbox', paramsFile);
save(paramsFile, "-struct", "params");
end

View File

@@ -1,15 +1,20 @@
function obj = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange)
function obj = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange, sensorPerformanceMinimum)
arguments (Input)
obj (1,1) {mustBeA(obj, 'sensingObjective')};
objectiveFunction (1, 1) {mustBeA(objectiveFunction, 'function_handle')};
domain (1, 1) {mustBeGeometry};
discretizationStep (1, 1) double = 1;
protectedRange (1, 1) double = 1;
sensorPerformanceMinimum (1, 1) double = 1e-6;
end
arguments (Output)
obj (1,1) {mustBeA(obj, 'sensingObjective')};
end
obj.discretizationStep = discretizationStep;
obj.sensorPerformanceMinimum = sensorPerformanceMinimum;
obj.groundAlt = domain.minCorner(3);
obj.protectedRange = protectedRange;
@@ -19,8 +24,8 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
yMin = min(domain.footprint(:, 2));
yMax = max(domain.footprint(:, 2));
xGrid = unique([xMin:discretizationStep:xMax, xMax]);
yGrid = unique([yMin:discretizationStep:yMax, yMax]);
xGrid = unique([xMin:obj.discretizationStep:xMax, xMax]);
yGrid = unique([yMin:obj.discretizationStep:yMax, yMax]);
% Store grid points for plotting later
[obj.X, obj.Y] = meshgrid(xGrid, yGrid);
@@ -29,9 +34,13 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
obj.objectiveFunction = objectiveFunction;
obj.values = reshape(obj.objectiveFunction(obj.X, obj.Y), size(obj.X));
% Normalize
obj.values = obj.values ./ max(obj.values, [], "all");
% store ground position
idx = obj.values == max(obj.values, [], "all");
idx = obj.values == 1;
obj.groundPos = [obj.X(idx), obj.Y(idx)];
obj.groundPos = obj.groundPos(1, 1:2); % for safety, in case 2 points are maximal (somehow)
assert(domain.distance([obj.groundPos, domain.center(3)]) > protectedRange, "Domain is crowding the sensing objective")
end

View File

@@ -2,18 +2,19 @@ classdef sensingObjective
% Sensing objective definition parent class
properties (SetAccess = private, GetAccess = public)
label = "";
groundAlt = 0;
groundPos = [0, 0];
discretizationStep = 1;
objectiveFunction = @(x, y) 0; % define objective functions over a grid in this manner
groundAlt = NaN;
groundPos = [NaN, NaN];
discretizationStep = NaN;
objectiveFunction = @(x, y) NaN; % define objective functions over a grid in this manner
X = [];
Y = [];
values = [];
protectedRange = 1; % keep obstacles from crowding objective
protectedRange = NaN; % keep obstacles from crowding objective
sensorPerformanceMinimum = NaN; % minimum sensor performance to allow assignment of a point in the domain to a partition
end
methods (Access = public)
[obj] = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange);
[obj] = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange, sensorPerformanceMinimum);
[obj] = initializeRandomMvnpdf(obj, domain, protectedRange, discretizationStep, protectedRange);
[f ] = plot(obj, ind, f);
end

View File

@@ -1,10 +1,8 @@
function obj = initialize(obj, alphaDist, betaDist, alphaPan, betaPan, alphaTilt, betaTilt)
function obj = initialize(obj, alphaDist, betaDist, alphaTilt, betaTilt)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'sigmoidSensor')}
alphaDist (1, 1) double;
betaDist (1, 1) double;
alphaPan (1, 1) double;
betaPan (1, 1) double;
alphaTilt (1, 1) double;
betaTilt (1, 1) double;
end
@@ -14,8 +12,6 @@ function obj = initialize(obj, alphaDist, betaDist, alphaPan, betaPan, alphaTilt
obj.alphaDist = alphaDist;
obj.betaDist = betaDist;
obj.alphaPan = alphaPan;
obj.betaPan = betaPan;
obj.alphaTilt = alphaTilt;
obj.betaTilt = betaTilt;
end

View File

@@ -12,7 +12,7 @@ function f = plotParameters(obj)
% Sample membership functions
d_x = obj.distanceMembership(d);
t_x = obj.tiltMembership(deg2rad(t));
t_x = obj.tiltMembership(t);
% Plot resultant sigmoid curves
f = figure;

View File

@@ -1,18 +1,19 @@
function value = sensorPerformance(obj, agentPos, agentPan, agentTilt, targetPos)
function value = sensorPerformance(obj, agentPos, targetPos)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'sigmoidSensor')};
agentPos (1, 3) double;
agentPan (1, 1) double;
agentTilt (1, 1) double;
targetPos (:, 3) double;
end
arguments (Output)
value (:, 1) double;
end
% compute direct distance and distance projected onto the ground
d = vecnorm(agentPos - targetPos, 2, 2); % distance from sensor to target
x = vecnorm(agentPos(1:2) - targetPos(:, 1:2), 2, 2); % distance from sensor nadir to target nadir (i.e. distance ignoring height difference)
tiltAngle = atan2(targetPos(:, 3) - agentPos(3), x) - agentTilt;
% compute tilt angle
tiltAngle = (180 - atan2d(x, targetPos(:, 3) - agentPos(3))); % degrees
% Membership functions
mu_d = obj.distanceMembership(d);

View File

@@ -3,15 +3,12 @@ classdef sigmoidSensor
% Sensor parameters
alphaDist = NaN;
betaDist = NaN;
alphaPan = NaN;
betaPan = NaN;
alphaTilt = NaN;
alphaTilt = NaN; % degrees
betaTilt = NaN;
end
methods (Access = public)
[obj] = initialize(obj, alphaDist, betaDist, alphaPan, betaPan, alphaTilt, betaTilt);
[values, positions] = sense(obj, agent, sensingObjective, domain, partitioning);
[obj] = initialize(obj, alphaDist, betaDist, alphaTilt, betaTilt);
[value] = sensorPerformance(obj, agentPos, agentPan, agentTilt, targetPos);
[f] = plotParameters(obj);
end

View File

@@ -1,7 +1,7 @@
function x = tiltMembership(obj, t)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'sigmoidSensor')};
t (:, 1) double;
t (:, 1) double; % degrees
end
arguments (Output)
x (:, 1) double;

View File

@@ -17,6 +17,6 @@ classdef cone
methods (Access = public)
[obj ] = initialize(obj, center, radius, height, tag, label);
[obj, f] = plot(obj, ind, f);
[obj, f] = plot(obj, ind, f, maxAlt);
end
end

View File

@@ -1,8 +1,9 @@
function [obj, f] = plot(obj, ind, f)
function [obj, f] = plot(obj, ind, f, maxAlt)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'cone')};
ind (1, :) double = NaN;
f (1, 1) {mustBeA(f, 'matlab.ui.Figure')} = figure;
maxAlt (1, 1) = 10;
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'cone')};
@@ -12,16 +13,18 @@ function [obj, f] = plot(obj, ind, f)
% Create axes if they don't already exist
f = firstPlotSetup(f);
scalingFactor = (maxAlt / obj.height);
% Plot cone
[X, Y, Z] = cylinder([obj.radius, 0], obj.n);
[X, Y, Z] = cylinder([scalingFactor * obj.radius, 0], obj.n);
% Scale to match height
Z = Z * obj.height;
Z = Z * maxAlt;
% Move to center location
X = X + obj.center(1);
Y = Y + obj.center(2);
Z = Z + obj.center(3);
Z = Z + obj.center(3) - maxAlt;
% Plot
if isnan(ind)

View File

@@ -0,0 +1,19 @@
function cPos = closestToPoint(obj, pos)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'rectangularPrism')};
pos (:, 3) double;
end
arguments (Output)
cPos (:, 3) double;
end
cPos = NaN(1, 3);
for ii = 1:3
if pos(ii) < obj.minCorner(ii)
cPos(ii) = obj.minCorner(ii);
elseif pos(ii) > obj.maxCorner(ii)
cPos(ii) = obj.maxCorner(ii);
else
cPos(ii) = pos(ii);
end
end
end

View File

@@ -10,32 +10,37 @@ function c = containsLine(obj, pos1, pos2)
d = pos2 - pos1;
% edge case where the line is parallel to the geometry
if abs(d) < 1e-12
% check if it happens to start or end inside or outside of
% the geometry
% endpoint contained (trivial case)
if obj.contains(pos1) || obj.contains(pos2)
c = true;
else
c = false;
end
return;
end
tmin = -inf;
tmax = inf;
% Standard case
% parameterize the line segment to check for an intersection
tMin = 0;
tMax = 1;
for ii = 1:3
% line is parallel to geometry
if abs(d(ii)) < 1e-12
if pos1(ii) < obj.minCorner(ii) || pos1(ii) > obj.maxCorner(ii)
c = false;
return;
end
else
t1 = (obj.minCorner(ii) - pos1(ii)) / d(ii);
t2 = (obj.maxCorner(ii) - pos2(ii)) / d(ii);
tmin = max(tmin, min(t1, t2));
tmax = min(tmax, max(t1, t2));
if tmin > tmax
t2 = (obj.maxCorner(ii) - pos1(ii)) / d(ii);
tLow = min(t1, t2);
tHigh = max(t1, t2);
tMin = max(tMin, tLow);
tMax = min(tMax, tHigh);
if tMin > tMax
c = false;
return;
end
end
c = (tmax >= 0) && (tmin <= 1);
end
c = true;
end

View File

@@ -4,7 +4,7 @@ function d = distance(obj, pos)
pos (:, 3) double;
end
arguments (Output)
d (:, 1) double
d (:, 1) double;
end
if obj.contains(pos)
% Queried point is inside geometry

View File

@@ -0,0 +1,42 @@
function g = distanceGradient(obj, pos)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'rectangularPrism')};
pos (:, 3) double;
end
arguments (Output)
g (:, 3) double
end
% find nearest point on surface to query position
q = min(max(pos, obj.minCorner), obj.maxCorner);
% Find distance and direction between pos and q
v = pos - q;
vNorm = norm(v);
% position is outside geometry
if vNorm > 0
% gradient is normalized vector from q to p
g = v / vNorm;
return;
end
% position is on or in geometry
% find distances to each face in each dimension
distances = [pos(1) - obj.minCorner(1), obj.maxCorner(1) - pos(1), pos(2) - obj.minCorner(2), obj.maxCorner(2) - pos(2), pos(3) - obj.minCorner(3), obj.maxCorner(3) - pos(3)];
[~, idx] = min(distances);
% I think there needs to be additional handling here for the
% edge/corner cases, where there are ways to balance or resolve ties
% when two faces are equidistant to the query position
assert(sum(idx) == idx, "Implement edge case handling");
% select gradient that brings us quickest to the nearest face
g = [ 1, 0, 0; ...
-1, 0, 0; ...
0, 1, 0; ...
0, -1, 0; ...
0, 0, 1; ...
0, 0, -1;];
g = g(idx, :);
end

View File

@@ -24,6 +24,10 @@ function obj = initialize(obj, bounds, tag, label, objectiveFunction, discretiza
% Compute center
obj.center = obj.minCorner + obj.dimensions ./ 2;
% Compute a (fake) radius
% fully contains the rectangular prism from the center
obj.radius = (1/2) * sqrt(sum(obj.dimensions.^2));
% Compute vertices
obj.vertices = [obj.minCorner;
obj.maxCorner(1), obj.minCorner(2:3);

View File

@@ -1,11 +1,12 @@
function [obj] = initializeRandom(obj, tag, label, minDimension, maxDimension, domain)
function [obj] = initializeRandom(obj, tag, label, minDimension, maxDimension, domain, minAlt)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'rectangularPrism')};
tag (1, 1) REGION_TYPE = REGION_TYPE.INVALID;
label (1, 1) string = "";
minDimension (1, 1) double = 10;
maxDimension (1, 1) double= 20;
maxDimension (1, 1) double = 20;
domain (1, 1) {mustBeGeometry} = rectangularPrism;
minAlt (1, 1) double = 1;
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'rectangularPrism')};
@@ -27,7 +28,7 @@ function [obj] = initializeRandom(obj, tag, label, minDimension, maxDimension, d
while ~domain.contains(candidateMaxCorner) || all(domain.objective.groundPos + domain.objective.protectedRange >= candidateMinCorner(1:2), 2) && all(domain.objective.groundPos - domain.objective.protectedRange <= candidateMaxCorner(1:2), 2)
if ii == 0 || ii > 10
candidateMinCorner = domain.random();
candidateMinCorner(3) = 0; % bind to floor
candidateMinCorner(3) = minAlt; % bind to floor (plus minimum altitude constraint)
ii = 1;
end

View File

@@ -3,7 +3,6 @@ classdef rectangularPrism
properties (SetAccess = private, GetAccess = public)
% Meta
tag = REGION_TYPE.INVALID;
label = "";
% Spatial
minCorner = NaN(1, 3);
@@ -11,6 +10,7 @@ classdef rectangularPrism
dimensions = NaN(1, 3);
center = NaN;
footprint = NaN(4, 2);
radius = NaN; % fake radius
% Graph
vertices = NaN(8, 3);
@@ -22,6 +22,7 @@ classdef rectangularPrism
lines;
end
properties (SetAccess = public, GetAccess = public)
label = "";
% Sensing objective (for DOMAIN region type only)
objective;
end
@@ -31,7 +32,9 @@ classdef rectangularPrism
[obj ] = initializeRandom(obj, tag, label, minDimension, maxDimension, domain);
[r ] = random(obj);
[c ] = contains(obj, pos);
[cPos ] = closestToPoint(obj, pos);
[d ] = distance(obj, pos);
[g ] = distanceGradient(obj, pos);
[c ] = containsLine(obj, pos1, pos2);
[obj, f] = plotWireframe(obj, ind, f);
end

View File

@@ -0,0 +1,10 @@
function c = contains(obj, pos)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'spherical')};
pos (:, 3) double;
end
arguments (Output)
c (:, 1) logical
end
c = norm(obj.center - pos) <= obj.radius;
end

View File

@@ -0,0 +1,28 @@
function c = containsLine(obj, pos1, pos2)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'spherical')};
pos1 (1, 3) double;
pos2 (1, 3) double;
end
arguments (Output)
c (1, 1) logical
end
d = pos2 - pos1;
f = pos1 - obj.center;
a = dot(d, d);
b = 2 * dot(f, d);
c = dot(f, f) - obj.radius^2;
disc = b^2 - 4*a*c;
if disc < 0
c = false;
return;
end
t = [(-b - sqrt(disc)) / (2 * a), (-b + sqrt(disc)) / (2 * a)];
c = (t(1) >= 0 && t(1) <= 1) || (t(2) >= 0 && t(2) <= 1);
end

View File

@@ -0,0 +1,37 @@
function obj = initialize(obj, center, radius, tag, label)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'spherical')};
center (1, 3) double;
radius (1, 1) double;
tag (1, 1) REGION_TYPE = REGION_TYPE.INVALID;
label (1, 1) string = "";
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'spherical')};
end
obj.tag = tag;
obj.label = label;
% Define geometry
obj.center = center;
obj.radius = radius;
obj.diameter = 2 * obj.radius;
% fake vertices in a cross pattern
obj.vertices = [obj.center + [obj.radius, 0, 0]; ...
obj.center - [obj.radius, 0, 0]; ...
obj.center + [0, obj.radius, 0]; ...
obj.center - [0, obj.radius, 0]; ...
obj.center + [0, 0, obj.radius]; ...
obj.center - [0, 0, obj.radius]];
% fake edges in two perpendicular rings
obj.edges = [1, 3; ...
3, 2; ...
2, 4; ...
4, 1; ...
1, 5; ...
5, 2; ...
2, 6; ...
6, 1];
end

View File

@@ -0,0 +1,43 @@
function [obj, f] = plotWireframe(obj, ind, f)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'spherical')};
ind (1, :) double = NaN;
f (1, 1) {mustBeA(f, 'matlab.ui.Figure')} = figure;
end
arguments (Output)
obj (1, 1) {mustBeA(obj, 'spherical')};
f (1, 1) {mustBeA(f, 'matlab.ui.Figure')};
end
% Create axes if they don't already exist
f = firstPlotSetup(f);
% Create plotting inputs
[X, Y, Z] = sphere(8);
% Scale
X = X * obj.radius;
Y = Y * obj.radius;
Z = Z * obj.radius;
% Shift
X = X + obj.center(1);
Y = Y + obj.center(2);
Z = Z + obj.center(3);
% Plot the boundaries of the geometry into 3D view
if isnan(ind)
o = plot3(f.CurrentAxes, X, Y, Z, '-', 'Color', obj.tag.color, 'LineWidth', 2);
else
hold(f.Children(1).Children(ind(1)), "on");
o = plot3(f.Children(1).Children(ind(1)), X, Y, Z, '-', 'Color', obj.tag.color, 'LineWidth', 2);
hold(f.Children(1).Children(ind(1)), "off");
end
% Copy to other requested tiles
if numel(ind) > 1
for ii = 2:size(ind, 2)
o = [o, copyobj(o(:, 1), f.Children(1).Children(ind(ii)))];
end
end
obj.lines = o;
end

View File

@@ -0,0 +1,15 @@
function r = random(obj)
arguments (Input)
obj (1, 1) {mustBeA(obj, 'spherical')};
end
arguments (Output)
r (1, 3) double
end
y = (rand - 0.5) * 2; % uniform draw on [-1, 1]
R = sqrt(1 - y^2);
lon = (rand - 0.5) * 2 * pi; % uniform draw on [-pi, pi]
s = [R * sin(lon), y, R * cos(lon)]; % random point on surface
r = s * rand^(1/3); % scaled to random normalized radius [0, 1]
r = obj.center + obj.radius * r;
end

View File

@@ -0,0 +1,33 @@
classdef spherical
% Rectangular prism geometry
properties (SetAccess = private, GetAccess = public)
% Spatial
center = NaN;
radius = NaN;
diameter = NaN;
vertices; % fake vertices
edges; % fake edges
% Plotting
lines;
end
properties (SetAccess = public, GetAccess = public)
% Meta
tag = REGION_TYPE.INVALID;
label = "";
% Sensing objective (for DOMAIN region type only)
objective;
end
methods (Access = public)
[obj ] = initialize(obj, center, radius, tag, label);
[r ] = random(obj);
[c ] = contains(obj, pos);
[d ] = distance(obj, pos);
[g ] = distanceGradient(obj, pos);
[c ] = containsLine(obj, pos1, pos2);
[obj, f] = plotWireframe(obj, ind, f);
end
end

View File

@@ -9,6 +9,7 @@ classdef REGION_TYPE
OBSTACLE (2, [255, 127, 127]); % obstacle region
COLLISION (3, [255, 255, 128]); % collision avoidance region
FOV (4, [255, 165, 0]); % field of view region
COMMS (5, [0, 255, 0]); % comunications region
end
methods
function obj = REGION_TYPE(id, color)

View File

@@ -1,26 +0,0 @@
function nextPos = gradientAscent(sensedValues, sensedPositions, pos, rate)
arguments (Input)
sensedValues (:, 1) double;
sensedPositions (:, 3) double;
pos (1, 3) double;
rate (1, 1) double = 0.1;
end
arguments (Output)
nextPos(1, 3) double;
end
% As a default, maintain current position
if size(sensedValues, 1) == 0 && size(sensedPositions, 1) == 0
nextPos = pos;
return;
end
% Select next position by maximum sensed value
nextPos = sensedPositions(sensedValues == max(sensedValues), :);
nextPos = [nextPos(1, 1:2), pos(3)]; % just in case two get selected, simply pick one
% rate-limit motion
v = nextPos - pos;
nextPos = pos + (v / norm(v, 2)) * rate;
end

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@@ -1,2 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
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<?xml version="1.0" encoding="UTF-8"?>
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<?xml version="1.0" encoding="UTF-8"?>
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<?xml version="1.0" encoding="UTF-8"?>
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<?xml version="1.0" encoding="UTF-8"?>
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<?xml version="1.0" encoding="UTF-8"?>
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