101 lines
5.3 KiB
Matlab
101 lines
5.3 KiB
Matlab
%% Plot AERPAW logs (trajectory, radio)
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resultsPath = fullfile(matlab.project.rootProject().RootFolder, "sandbox", "columns_simulated"); % Define path to results copied from AERPAW platform
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% Check timeline in controller logs
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controller = controllerAnalysis(resultsPath);
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% Plot GPS logged data and scenario information (domain, objective, obstacles)
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seaToGroundLevel = 110; % measured approximately from USGS national map viewer
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plotWholeFlight = true; % do not attempt to automatically trim initial and final positioning and landing from flight plot (buggy)
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[fGlobe, G] = plotGpsLogs(resultsPath, seaToGroundLevel, true);
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% Plot radio statistics (time-based and distance-based)
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[fRadio, fRadioDist, R] = plotRadioLogs(resultsPath, G, controller.timestamp([1, end]));
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%% Run simulation
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% Run miSim using same AERPAW scenario definition CSV
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csvPath = fullfile(matlab.project.rootProject().RootFolder, "aerpaw", "config", "scenario.csv");
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params = readScenarioCsv(csvPath);
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% Visualization settings
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plotCommsGeometry = false;
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makePlots = true;
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makeVideo = true;
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% Define scenario according to CSV specification
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domain = rectangularPrism;
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domain = domain.initialize([params.domainMin; params.domainMax], REGION_TYPE.DOMAIN, "Domain");
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if length(params.objectiveVar) > 4 && length(params.objectivePos) > 2
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objectiveSigma = permute(reshape(params.objectiveVar, [length(params.objectiveVar)/4 2 2]), [3 1 2]);
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objectivePos = reshape(params.objectivePos, [length(params.objectivePos)/2, 2])';
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else
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objectiveSigma = reshape(params.objectiveVar, [1, 2, 2]);
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objectivePos = params.objectivePos;
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end
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domain.objective = domain.objective.initialize(objectiveFunctionWrapper(objectivePos, objectiveSigma), domain, params.discretizationStep, params.protectedRange, params.sensorPerformanceMinimum);
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agents = cell(size(params.initialPositions, 2) / 3, 1);
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for ii = 1:size(agents, 1)
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agents{ii} = agent;
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sensorModel = sigmoidSensor;
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sensorModel = sensorModel.initialize(params.alphaDist(ii), params.betaDist(ii), params.alphaTilt(ii), params.betaTilt(ii));
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collisionGeometry = spherical;
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collisionGeometry = collisionGeometry.initialize(params.initialPositions((((ii - 1) * 3) + 1):(ii * 3)), params.collisionRadius(ii), REGION_TYPE.COLLISION, sprintf("Agent %d collision geometry", ii));
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agents{ii} = agents{ii}.initialize(params.initialPositions((((ii - 1) * 3) + 1):(ii * 3)), collisionGeometry, sensorModel, params.comRange(ii), params.maxIter, params.initialStepSize, 5.0, sprintf("Agent %d", ii), plotCommsGeometry);
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if isfield(params, 'txPower')
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agents{ii}.txPower = params.txPower(ii); % SINR comms model transmit power (W)
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end
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end
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% Create obstacles
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obstacles = cell(params.numObstacles, 1);
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for ii = 1:size(obstacles, 1)
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obstacles{ii} = rectangularPrism;
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obstacles{ii} = obstacles{ii}.initialize([params.obstacleMin((((ii - 1) * 3) + 1):(ii * 3)); params.obstacleMax((((ii - 1) * 3) + 1):(ii * 3))], "OBSTACLE", sprintf("Obstacle %d", ii));
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end
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% SINR communications model parameters (optional CSV columns; default to the
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% fixed-radius path so existing scenarios are unaffected)
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if isfield(params, 'useSinrComms'); useSinrComms = logical(params.useSinrComms); else; useSinrComms = false; end
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if isfield(params, 'sinrThreshold'); sinrThreshold = params.sinrThreshold; else; sinrThreshold = 0; end
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if isfield(params, 'pathLossExponent'); pathLossExponent = params.pathLossExponent; else; pathLossExponent = 2.0; end
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if isfield(params, 'ambientTemp'); ambientTemp = params.ambientTemp; else; ambientTemp = 290.0; end
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if isfield(params, 'centerFreq'); centerFreq = params.centerFreq; else; centerFreq = 2.4e9; end
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if isfield(params, 'bandwidth'); bandwidth = params.bandwidth; else; bandwidth = 20e6; end
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% Set up simulation. The four args after makeVideo (useDoubleIntegrator,
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% dampingCoeff, useFixedTopology, optimizeSensorPointing) are kept at their
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% prior effective defaults to preserve existing analysis behavior; only the
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% SINR comms parameters are threaded through from the scenario.
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sim = miSim;
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sim = sim.initialize(domain, agents, params.barrierGain, params.barrierExponent, params.minAlt, params.timestep, params.maxIter, obstacles, makePlots, makeVideo, ...
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false, 2.0, false, false, useSinrComms, sinrThreshold, pathLossExponent, ambientTemp, centerFreq, bandwidth);
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% Save simulation parameters to output file
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sim.writeInits();
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% Run
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sim = sim.run();
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% Save results
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sim = sim.teardown();
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%% Plot AERPAW trajectory logs onto simulated result for comparison
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% Duplicate plot to overlay with logged trajectories
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comparison = figure;
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copyobj(sim.f.Children, comparison);
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% Plot trajectories on top
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for ii = 1:size(G, 1)
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gpsTimes = G{ii}.Timestamp;
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gpsTimes.TimeZone = '';
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inRange = gpsTimes >= controller.timestamp(1) & gpsTimes <= controller.timestamp(end);
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for jj = 1:size(sim.spatialPlotIndices, 2)
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hold(comparison.Children.Children(sim.spatialPlotIndices(jj)), "on");
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plot3(comparison.Children(1).Children(sim.spatialPlotIndices(jj)), G{ii}.East(inRange), G{ii}.North(inRange), G{ii}.Up(inRange) + seaToGroundLevel, 'Color', 'r', 'LineWidth', 1);
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hold(comparison.Children.Children(sim.spatialPlotIndices(jj)), "off");
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end
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end |