cleaned up obstacle generation

@sensingObjective/initialize.m

@@ -1,15 +1,17 @@
-function obj = initialize(obj, objectiveFunction, domain, discretizationStep)
+function obj = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange)
     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;
     end
     arguments (Output)
         obj (1,1) {mustBeA(obj, 'sensingObjective')};
     end
 
     obj.groundAlt = domain.minCorner(3);
+    obj.protectedRange = protectedRange;
 
     % Extract footprint limits
     xMin = min(domain.footprint(:, 1));
@@ -30,4 +32,6 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep)
     % store ground position
     idx = obj.values == max(obj.values, [], "all");
     obj.groundPos = [obj.X(idx), obj.Y(idx)];
+
+    assert(domain.distance([obj.groundPos, domain.center(3)]) > protectedRange, "Domain is crowding the sensing objective")
 end

@sensingObjective/initializeRandomMvnpdf.m

@@ -1,9 +1,9 @@
-function obj = initializeRandomMvnpdf(obj, domain, protectedRange, discretizationStep)
+function obj = initializeRandomMvnpdf(obj, domain, discretizationStep, protectedRange)
     arguments (Input)
         obj (1, 1) {mustBeA(obj, 'sensingObjective')};
         domain (1, 1) {mustBeGeometry};
-        protectedRange (1, 1) double = 1;
         discretizationStep (1, 1) double = 1;
+        protectedRange (1, 1) double = 1;
     end
     arguments (Output)
         obj (1, 1) {mustBeA(obj, 'sensingObjective')};
@@ -23,5 +23,5 @@ function obj = initializeRandomMvnpdf(obj, domain, protectedRange, discretizatio
     objectiveFunction = @(x, y) mvnpdf([x(:), y(:)], mu, sig);
 
     % Regular initialization
-    obj = obj.initialize(objectiveFunction, domain, discretizationStep);
+    obj = obj.initialize(objectiveFunction, domain, discretizationStep, protectedRange);
 end

@sensingObjective/sensingObjective.m

@@ -9,11 +9,12 @@ classdef sensingObjective
         X = [];
         Y = [];
         values = [];
+        protectedRange = 1; % keep obstacles from crowding objective
     end
 
     methods (Access = public)
-        [obj] = initialize(obj, objectiveFunction, domain, discretizationStep);
+        [obj] = initialize(obj, objectiveFunction, domain, discretizationStep, protectedRange);
-        [obj] = initializeRandomMvnpdf(obj, domain, protectedRange, discretizationStep);
+        [obj] = initializeRandomMvnpdf(obj, domain, protectedRange, discretizationStep, protectedRange);
         [f  ] = plot(obj, ind, f);
     end
 end

geometries/@rectangularPrism/initializeRandom.m

@@ -1,17 +1,43 @@
-function [obj] = initializeRandom(obj, minDimension, tag, label)
+function [obj] = initializeRandom(obj, tag, label, minDimension, maxDimension, domain)
     arguments (Input)
         obj (1, 1) {mustBeA(obj, 'rectangularPrism')};
-        minDimension (1, 1) double = 10;
         tag (1, 1) REGION_TYPE = REGION_TYPE.INVALID;
         label (1, 1) string = "";
+        minDimension (1, 1) double = 10;
+        maxDimension (1, 1) double= 20;
+        domain (1, 1) {mustBeGeometry} = rectangularPrism;
     end
     arguments (Output)
         obj (1, 1) {mustBeA(obj, 'rectangularPrism')};
     end
     
-    % Produce random bounds
+    % Produce random bounds based on region type
-    L = ceil(minDimension + rand * minDimension);
+    if tag == REGION_TYPE.DOMAIN
-    bounds = [zeros(1, 3); L * ones(1, 3)];
+        % Domain
+        L = ceil(minDimension + rand * (maxDimension - minDimension));
+        bounds = [zeros(1, 3); L * ones(1, 3)];
+    else
+        % Obstacle
+
+        % Produce a corners that are contained in the domain
+        ii = 0;
+        candidateMaxCorner = domain.maxCorner + ones(1, 3);
+        candidateMinCorner = domain.minCorner - ones(1, 3);
+        % Continue until the domain contains the obstacle without crowding the objective
+        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
+                ii = 1;
+            end
+
+            candidateMaxCorner = candidateMinCorner + minDimension + rand(1, 3) * (maxDimension - minDimension);
+
+            ii = ii + 1;
+        end
+        
+        bounds = [candidateMinCorner; candidateMaxCorner;];
+    end
 
     % Regular initialization
     obj = obj.initialize(bounds, tag, label);

geometries/@rectangularPrism/rectangularPrism.m

@@ -28,7 +28,7 @@ classdef rectangularPrism
 
     methods (Access = public)
         [obj   ] = initialize(obj, bounds, tag, label, objectiveFunction, discretizationStep);
-        [obj   ] = initializeRandom(obj, tag, label);
+        [obj   ] = initializeRandom(obj, tag, label, minDimension, maxDimension, domain);
         [r     ] = random(obj);
         [c     ] = contains(obj, pos);
         [d     ] = distance(obj, pos);

test/test_miSim.m

@@ -43,15 +43,13 @@ classdef test_miSim < matlab.unittest.TestCase
         % Generate a random domain
         function tc = setDomain(tc)
             % random integer-dimensioned cubic domain
-            tc.domain = tc.domain.initializeRandom(tc.minDimension, REGION_TYPE.DOMAIN, "Domain");
+            tc.domain = tc.domain.initializeRandom(REGION_TYPE.DOMAIN, "Domain", tc.minDimension);
             % Random bivariate normal PDF objective
-            tc.domain.objective = tc.domain.objective.initializeRandomMvnpdf(tc.domain, tc.protectedRange, tc.discretizationStep);
+            tc.domain.objective = tc.domain.objective.initializeRandomMvnpdf(tc.domain, tc.discretizationStep, tc.protectedRange);
         end
         % Instantiate agents
         function tc = setAgents(tc)
-            % Agents will be initialized under different parameters in
+            % Agents will be initialized under different parameters in individual test cases
-            % individual test cases
-
             % Instantiate a random number of agents according to parameters
             for ii = 1:randi([tc.minAgents, tc.maxAgents])
                 tc.agents{ii, 1} = agent;
@@ -73,19 +71,9 @@ classdef test_miSim < matlab.unittest.TestCase
             for ii = 1:size(tc.obstacles, 1)
                 badCandidate = true;
                 while badCandidate
-                    % Instantiate a rectangular prism obstacle
+                    % Instantiate a rectangular prism obstacle inside the domain
                     tc.obstacles{ii} = rectangularPrism;
-    
+                    tc.obstacles{ii} = tc.obstacles{ii}.initializeRandom(REGION_TYPE.OBSTACLE, sprintf("Obstacle %d", ii), tc.minObstacleSize, tc.maxObstacleSize, tc.domain);
-                    % Randomly generate min corner for the obstacle
-                    candidateMinCorner = tc.domain.random();
-                    candidateMinCorner = [candidateMinCorner(1:2), 0]; % bind obstacles to floor of domain
-    
-                    % Randomly select a corresponding maximum corner that
-                    % satisfies min/max obstacle size specifications
-                    candidateMaxCorner = candidateMinCorner + tc.minObstacleSize + rand(1, 3) * (tc.maxObstacleSize - tc.minObstacleSize);
-                
-                    % Initialize obstacle
-                    tc.obstacles{ii} = tc.obstacles{ii}.initialize([candidateMinCorner; candidateMaxCorner], REGION_TYPE.OBSTACLE, sprintf("Column obstacle %d", ii));
     
                     % Check if the obstacle intersects with any existing
                     % obstacles
@@ -100,24 +88,6 @@ classdef test_miSim < matlab.unittest.TestCase
                         continue;
                     end
                     
-                    % Make sure that the obstacles are fully contained by 
-                    % the domain
-                    if ~domainContainsObstacle(tc.domain, tc.obstacles{ii})
-                        continue;
-                    end
-
-                    % Make sure that the obstacles don't cover the sensing
-                    % objective
-                    if obstacleCoversObjective(tc.domain.objective, tc.obstacles{ii})
-                        continue;
-                    end
-
-                    % Make sure that the obstacles aren't too close to the
-                    % sensing objective
-                    if obstacleCrowdsObjective(tc.domain.objective, tc.obstacles{ii}, tc.protectedRange)
-                        continue;
-                    end
-                    
                     badCandidate = false;
                 end
             end
@@ -241,19 +211,9 @@ classdef test_miSim < matlab.unittest.TestCase
             for ii = 1:size(tc.obstacles, 1)
                 badCandidate = true;
                 while badCandidate
-                    % Instantiate a rectangular prism obstacle
+                    % Instantiate a rectangular prism obstacle inside the domain
                     tc.obstacles{ii} = rectangularPrism;
-    
+                    tc.obstacles{ii} = tc.obstacles{ii}.initializeRandom(REGION_TYPE.OBSTACLE, sprintf("Obstacle %d", ii), tc.minObstacleSize, tc.maxObstacleSize, tc.domain);
-                    % Randomly generate min corner for the obstacle
-                    candidateMinCorner = tc.domain.random();
-                    candidateMinCorner = [candidateMinCorner(1:2), 0]; % bind obstacles to floor of domain
-    
-                    % Randomly select a corresponding maximum corner that
-                    % satisfies min/max obstacle size specifications
-                    candidateMaxCorner = candidateMinCorner + tc.minObstacleSize + rand(1, 3) * (tc.maxObstacleSize - tc.minObstacleSize);
-                
-                    % Initialize obstacle
-                    tc.obstacles{ii} = tc.obstacles{ii}.initialize([candidateMinCorner; candidateMaxCorner], REGION_TYPE.OBSTACLE, sprintf("Column obstacle %d", ii));
     
                     % Check if the obstacle intersects with any existing
                     % obstacles
@@ -268,24 +228,6 @@ classdef test_miSim < matlab.unittest.TestCase
                         continue;
                     end
                     
-                    % Make sure that the obstacles are fully contained by 
-                    % the domain
-                    if ~domainContainsObstacle(tc.domain, tc.obstacles{ii})
-                        continue;
-                    end
-
-                    % Make sure that the obstacles don't cover the sensing
-                    % objective
-                    if obstacleCoversObjective(tc.domain.objective, tc.obstacles{ii})
-                        continue;
-                    end
-
-                    % Make sure that the obstacles aren't too close to the
-                    % sensing objective
-                    if obstacleCrowdsObjective(tc.domain.objective, tc.obstacles{ii}, tc.protectedRange)
-                        continue;
-                    end
-                    
                     badCandidate = false;
                 end
             end
@@ -411,7 +353,7 @@ classdef test_miSim < matlab.unittest.TestCase
             tc.domain = tc.domain.initialize([zeros(1, 3); 10 * ones(1, 3)], REGION_TYPE.DOMAIN, "Domain");
 
             % make basic sensing objective
-            tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2)), tc.domain, tc.discretizationStep);
+            tc.domain.objective = tc.domain.objective.initialize(@(x, y) mvnpdf([x(:), y(:)], tc.domain.center(1:2)), tc.domain, tc.discretizationStep, tc.protectedRange);
         
             % Initialize agent collision geometry
             geometry1 = rectangularPrism;