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