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reimplemented gradient ascent as central finite differences method

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This commit is contained in:
Kevin Dee
2026-01-11 12:42:48 -08:00
parent c47b7229ba
commit ec202d7790
12 changed files with 101 additions and 176 deletions
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5
@sensingObjective/initialize.m
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@@ -1,10 +1,11 @@
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')};
@@ -12,6 +13,8 @@ function obj = initialize(obj, objectiveFunction, domain, discretizationStep, pr
obj.discretizationStep = discretizationStep;
obj.sensorPerformanceMinimum = sensorPerformanceMinimum;
obj.groundAlt = domain.minCorner(3);
obj.protectedRange = protectedRange;

3
@sensingObjective/sensingObjective.m
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@@ -10,10 +10,11 @@ classdef sensingObjective
Y = [];
values = [];
protectedRange = 1; % keep obstacles from crowding objective
sensorPerformanceMinimum = 1e-6; % 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