mirror of
https://github.com/alliedmodders/hl2sdk.git
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411 lines
11 KiB
C++
411 lines
11 KiB
C++
//========= Copyright Valve Corporation, All rights reserved. ============//
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//
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// Purpose:
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//
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// $NoKeywords: $
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//=============================================================================//
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#include "cbase.h"
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#include "ai_movesolver.h"
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#include "ndebugoverlay.h"
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// memdbgon must be the last include file in a .cpp file!!!
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#include "tier0/memdbgon.h"
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//-----------------------------------------------------------------------------
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inline float V_round( float f )
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{
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return (float)( (int)( f + 0.5 ) );
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}
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//-----------------------------------------------------------------------------
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// CAI_MoveSolver
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//-----------------------------------------------------------------------------
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// The epsilon used by the solver
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const float AIMS_EPS = 0.01;
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//-----------------------------------------------------------------------------
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// Visualization
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//-----------------------------------------------------------------------------
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void CAI_MoveSolver::VisualizeRegulations( const Vector& origin )
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{
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if ( m_Regulations.Count() )
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{
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CAI_MoveSuggestions regulations;
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regulations.AddVectorToTail( m_Regulations );
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NormalizeSuggestions( ®ulations[0], (®ulations[0]) + regulations.Count() );
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Vector side1, mid, side2;
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for (int i = regulations.Count(); --i >= 0; )
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{
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// Compute the positions of the angles...
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float flMinAngle = regulations[i].arc.center - regulations[i].arc.span * 0.5f;
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float flMaxAngle = regulations[i].arc.center + regulations[i].arc.span * 0.5f;
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side1 = UTIL_YawToVector( flMinAngle );
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side2 = UTIL_YawToVector( flMaxAngle );
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mid = UTIL_YawToVector( regulations[i].arc.center );
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// Stronger weighted ones are bigger
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if ( regulations[i].weight < 0 )
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{
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float flLength = 10 + 40 * ( regulations[i].weight * -1.0);
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side1 *= flLength;
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side2 *= flLength;
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mid *= flLength;
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side1 += origin;
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side2 += origin;
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mid += origin;
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NDebugOverlay::Triangle(origin, mid, side1, 255, 0, 0, 48, true, 0.1f );
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NDebugOverlay::Triangle(origin, side2, mid, 255, 0, 0, 48, true, 0.1f );
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}
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}
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}
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}
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//-------------------------------------
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// Purpose: The actual solver function. Reweights according to type and sums
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// all the suggestions, identifying the best.
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//-------------------------------------
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bool CAI_MoveSolver::Solve( const AI_MoveSuggestion_t *pSuggestions, int nSuggestions, AI_MoveSolution_t *pResult)
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{
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//---------------------------------
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//
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// Quick out
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//
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if ( !nSuggestions )
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return false;
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if ( nSuggestions == 1 && m_Regulations.Count() == 0 && pSuggestions->type == AIMST_MOVE )
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{
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pResult->dir = pSuggestions->arc.center;
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return true;
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}
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//---------------------------------
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//
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// Setup
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//
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CAI_MoveSuggestions suggestions;
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suggestions.EnsureCapacity( m_Regulations.Count() + nSuggestions );
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suggestions.CopyArray( pSuggestions, nSuggestions);
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suggestions.AddVectorToTail( m_Regulations );
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// Initialize the solver
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const int NUM_SOLUTIONS = 120;
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const int SOLUTION_ANG = 360 / NUM_SOLUTIONS;
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COMPILE_TIME_ASSERT( ( 360 % NUM_SOLUTIONS ) == 0 );
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struct Solution_t
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{
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// The sum bias
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float bias;
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float highBias;
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AI_MoveSuggestion_t *pHighSuggestion;
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};
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Solution_t solutions[NUM_SOLUTIONS] = { { 0, 0, NULL } };
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//---------------------------------
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// The first thing we do is reweight and normalize the weights into a range of [-1..1], where
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// a negative weight is a repulsion. This becomes a bias for the solver.
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// @TODO (toml 06-18-02): this can be made sligtly more optimal by precalculating regulation adjusted weights
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Assert( suggestions.Count() >= 1 );
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NormalizeSuggestions( &suggestions[0], (&suggestions[0]) + suggestions.Count() );
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//
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// Add the biased suggestions to the solutions
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//
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for ( int iSuggestion = 0; iSuggestion < suggestions.Count(); ++iSuggestion )
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{
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AI_MoveSuggestion_t ¤t = suggestions[iSuggestion];
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// Convert arc values to solution indices relative to right post. Right is angle down, left is angle up.
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float halfSpan = current.arc.span * 0.5;
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int center = V_round( ( halfSpan * NUM_SOLUTIONS ) / 360 );
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int left = ( current.arc.span * NUM_SOLUTIONS ) / 360;
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float angRight = current.arc.center - halfSpan;
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if (angRight < 0.0)
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angRight += 360;
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int base = ( angRight * NUM_SOLUTIONS ) / 360;
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// Sweep from left to right, summing the bias. For positive suggestions,
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// the bias is further weighted to favor the center of the arc.
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const float positiveDegradePer180 = 0.05; // i.e., lose 5% of weight by the time hit 180 degrees off center
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const float positiveDegrade = ( positiveDegradePer180 / ( NUM_SOLUTIONS * 0.5 ) );
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for ( int i = 0; i < left + 1; ++i )
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{
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float bias = 0.0;
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if ( current.weight > 0)
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{
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int iOffset = center - i;
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float degrade = abs( iOffset ) * positiveDegrade;
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if ( ( (current.flags & AIMS_FAVOR_LEFT ) && i > center ) ||
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( (current.flags & AIMS_FAVOR_RIGHT) && i < center ) )
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{
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degrade *= 0.9;
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}
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bias = current.weight - ( current.weight * degrade );
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}
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else
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bias = current.weight;
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int iCurSolution = (base + i) % NUM_SOLUTIONS;
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solutions[iCurSolution].bias += bias;
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if ( bias > solutions[iCurSolution].highBias )
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{
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solutions[iCurSolution].highBias = bias;
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solutions[iCurSolution].pHighSuggestion = ¤t;
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}
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}
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}
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//
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// Find the best solution
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//
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int best = -1;
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float biasBest = 0;
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for ( int i = 0; i < NUM_SOLUTIONS; ++i )
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{
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if ( solutions[i].bias > biasBest )
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{
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best = i;
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biasBest = solutions[i].bias;
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}
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}
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if ( best == -1 )
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return false; // no solution
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//
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// Construct the results
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//
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float result = best * SOLUTION_ANG;
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// If the matching suggestion is within the solution, use that as the result,
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// as it is valid and more precise.
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const float suggestionCenter = solutions[best].pHighSuggestion->arc.center;
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if ( suggestionCenter > result && suggestionCenter <= result + SOLUTION_ANG )
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result = suggestionCenter;
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pResult->dir = result;
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return true;
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}
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//-------------------------------------
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// Purpose: Adjusts the suggestion weights according to the type of the suggestion,
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// apply the appropriate sign, ensure values are in expected ranges
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//-------------------------------------
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struct AI_MoveSuggWeights
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{
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float min;
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float max;
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};
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static AI_MoveSuggWeights g_AI_MoveSuggWeights[] = // @TODO (toml 06-18-02): these numbers need tuning
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{
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{ 0.20, 1.00 }, // AIMST_MOVE
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{ -0.00, -0.25 }, // AIMST_AVOID_DANGER
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{ -0.00, -0.25 }, // AIMST_AVOID_OBJECT
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{ -0.00, -0.25 }, // AIMST_AVOID_NPC
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{ -0.00, -0.25 }, // AIMST_AVOID_WORLD
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{ -1.00, -1.00 }, // AIMST_NO_KNOWLEDGE
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{ -0.60, -0.60 }, // AIMST_OSCILLATION_DETERRANCE
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{ 0.00, 0.00 }, // AIMST_INVALID
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};
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void CAI_MoveSolver::NormalizeSuggestions( AI_MoveSuggestion_t *pBegin, AI_MoveSuggestion_t *pEnd )
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{
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while ( pBegin != pEnd )
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{
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const float min = g_AI_MoveSuggWeights[pBegin->type].min;
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const float max = g_AI_MoveSuggWeights[pBegin->type].max;
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Assert( pBegin->weight >= -AIMS_EPS && pBegin->weight <= 1.0 + AIMS_EPS );
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if ( pBegin->weight < AIMS_EPS ) // zero normalizes to zero
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pBegin->weight = 0.0;
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else
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pBegin->weight = ( ( max - min ) * pBegin->weight ) + min;
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while (pBegin->arc.center < 0)
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pBegin->arc.center += 360;
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while (pBegin->arc.center >= 360)
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pBegin->arc.center -= 360;
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++pBegin;
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}
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}
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//-------------------------------------
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bool CAI_MoveSolver::HaveRegulationForObstacle( CBaseEntity *pEntity)
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{
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for ( int i = 0; i < m_Regulations.Count(); ++i )
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{
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if ( m_Regulations[i].hObstacleEntity != NULL &&
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pEntity == m_Regulations[i].hObstacleEntity.Get() )
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{
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return true;
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}
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}
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return false;
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}
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//-----------------------------------------------------------------------------
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//
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// Commands and tests
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//
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#ifdef DEBUG
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CON_COMMAND(ai_test_move_solver, "Tests the AI move solver system")
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{
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#ifdef DEBUG
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const float EPS = 0.001;
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#endif
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DevMsg( "Beginning move solver tests...\n" );
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CAI_MoveSolver solver;
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AI_MoveSolution_t solution;
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int i;
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//
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// Value in, no regulations, should yield value out
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//
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{
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DevMsg( "Simple... " );
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for (i = 0; i < 360; ++i)
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{
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Assert( solver.Solve( AI_MoveSuggestion_t( AIMST_MOVE, 1, i, 180 ), &solution ) );
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Assert( solution.dir == (float)i );
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}
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DevMsg( "pass.\n" );
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solver.ClearRegulations();
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}
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//
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// Two values in, should yield the first
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//
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{
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DevMsg( "Two positive... " );
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AI_MoveSuggestion_t suggestions[2];
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suggestions[0].Set( AIMST_MOVE, 1.0, 180, 100 );
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suggestions[1].Set( AIMST_MOVE, 0.5, 0, 100 );
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Assert( solver.Solve( suggestions, 2, &solution ) );
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Assert( solution.dir == (float)suggestions[0].arc.center );
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DevMsg( "pass.\n" );
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solver.ClearRegulations();
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}
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//
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// Two values in, first regulated, should yield the second
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//
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{
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DevMsg( "Avoid one of two... " );
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AI_MoveSuggestion_t suggestions[2];
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solver.AddRegulation(AI_MoveSuggestion_t( AIMST_AVOID_OBJECT, 1, 260, 60 ) );
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suggestions[0].Set( AIMST_MOVE, 1.0, 270, 45 );
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suggestions[1].Set( AIMST_MOVE, 1.0, 0, 45 );
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Assert( solver.Solve( suggestions, 2, &solution ) );
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Assert( solution.dir == (float)suggestions[1].arc.center );
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DevMsg( "pass.\n" );
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solver.ClearRegulations();
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}
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//
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// No solution
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//
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{
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DevMsg( "No solution... " );
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AI_MoveSuggestion_t suggestions[2];
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suggestions[0].Set( AIMST_MOVE, 1.0, 270, 90 );
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suggestions[1].Set( AIMST_AVOID_OBJECT, 1.0, 260, 180 );
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Assert( !solver.Solve( suggestions, 2, &solution ) );
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DevMsg( "pass.\n" );
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solver.ClearRegulations();
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}
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//
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// Nearest solution, in tolerance
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//
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{
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DevMsg( "Nearest solution, in tolerance... " );
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AI_MoveSuggestion_t suggestions[2];
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suggestions[0].Set( AIMST_MOVE, 1.0, 278, 90 );
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suggestions[1].Set( AIMST_AVOID_OBJECT, 1.0, 260, 24 );
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Assert( solver.Solve( suggestions, 2, &solution ) );
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Assert( solution.dir == (float)suggestions[0].arc.center );
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DevMsg( "pass.\n" );
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solver.ClearRegulations();
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}
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//
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// Nearest solution
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//
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{
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DevMsg( "Nearest solution... " );
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AI_MoveSuggestion_t suggestions[2];
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suggestions[0].Set( AIMST_MOVE, 1.0, 270, 90 );
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suggestions[1].Set( AIMST_AVOID_OBJECT, 1.0, 260, 40 );
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Assert( solver.Solve( suggestions, 2, &solution ) );
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Assert( solution.dir - 282 < EPS ); // given 60 solutions
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DevMsg( "pass.\n" );
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solver.ClearRegulations();
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}
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}
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#endif
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//=============================================================================
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