35 lines
1.7 KiB
Matlab
35 lines
1.7 KiB
Matlab
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, x.pan, x.tilt, [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 |