Increasing Speed Limits

本文针对Google Code Jam中的一道题“Increasing Speed Limits”,介绍了如何通过优化动态规划算法来解决寻找递增子序列数量的问题。通过对原始序列进行排序和离散化处理,并利用线段树进行区间求和,将复杂度降至NlogN。
Google Cod Jam Round1C C题 Increasing Speed Limits

Problem

You were driving along a highway when you got caught by the road police for speeding. It turns out that they've been following you, and they were amazed by the fact that you were accelerating the whole time without using the brakes! And now you desperately need an excuse to explain that.

You've decided that it would be reasonable to say "all the speed limit signs I saw were in increasing order, that's why I've been accelerating". The police officer laughs in reply, and tells you all the signs that are placed along the segment of highway you drove, and says that's unlikely that you were so lucky just to see some part of these signs that were in increasing order.

Now you need to estimate that likelihood, or, in other words, find out how many different subsequences of the given sequence are strictly increasing. The empty subsequence does not count since that would imply you didn't look at any speed limits signs at all!

For example, (1, 2, 5) is an increasing subsequence of (1, 4, 2, 3, 5, 5), and we count it twice because there are two ways to select (1, 2, 5) from the list.

Input

The first line of input gives the number of cases, N. N test cases follow. The first line of each case contains n, m, X, Y and Z each separated by a space. n will be the length of the sequence of speed limits. m will be the length of the generating array A. The next m lines will contain the m elements of A, one integer per line (from A[0] to A[m-1]).

Using A, X, Y and Z, the following pseudocode will print the speed limit sequence in order. mod indicates the remainder operation.

for i = 0 to n-1
print A[i mod m]
A[i mod m] = (X * A[i mod m] + Y * (i + 1)) mod Z

Note: The way that the input is generated has nothing to do with the intended solution and exists solely to keep the size of the input files low.

Output

For each test case you should output one line containing "Case #T: S" (quotes for clarity) where T is the number of the test case and S is the number of non-empty increasing subsequences mod 1 000 000 007.

Limits

1 ≤ N ≤ 20
1 ≤ m ≤ 100
0 ≤ X ≤ 109
0 ≤ Y ≤ 109
1 ≤ Z ≤ 109
0 ≤ A[i] < Z

Small dataset

1 ≤ mn ≤ 1000

Large dataset

1 ≤ mn ≤ 500 000

Sample


Input
 

Output
 
2
5 5 0 0 5
1
2
1
2
3
6 2 2 1000000000 6
1
2

Case #1: 15
Case #2: 13

The sequence of speed limit signs for case 2 should be 1, 2, 0, 0, 0, 4.

没赶上Round1A 郁闷。
Round1C Solve1和2,3的large不会做,菜。Rank好像是60多,能过。

赛后学习了下,也不算太难。
本来DP方程是这样的
for(i = 0; i < n; ++i) {
 for(j = 0; j < i; ++j) {
  if(A[j] < A[i]) {
    dp[i] += dp[j];
  }
 }
}
如果对A排序并且离散化,则变成了
for(i=0; i < n; ++i) {
  for(j = 0; j < A[i]; ++j) {
   dp[A[i]] += dp[j];
  }
}


大家注意看,内循环其实是一个区间求和。那么对于这种求和,线段树只可以做到NlogN的。
记得以前写过一道题的解题报告,是类似的。
pku1769 点树解决块查询点操作

下面是代码:(solve2函数是一个n^2的DP,偶水small input用的)
// Solution by alpc12  
#include 
< stdio.h >
#include 
< cassert >
#include 
< map >
#include 
< algorithm >
using   namespace  std;

const   int  M  =   100 ;
const   int  N  =   500010 ;
const   int  MOD  =   1000000007 ;

typedef 
long   long  LL;

int  n, m, X, Y, Z;
int  A[N], S[N];
int  st[ 1048576 ];
int  upperbound  =   524288 ;
int  dp[N];

void  generate() {
    
int  i;
    
for (i  =   0 ; i  <  n;  ++ i) {
        S[i] 
=  A[i % m];
        A[i
% m]  =  ((LL)X * A[i % m] + (LL)Y * (i + 1 )) % Z;
    }
    
for (i  =   0 ; i  <  n;  ++ i) {
        A[i] 
=  S[i];  
    }
}

int   get ( int  x,  int  y) {  //  左闭右开
    x  +=  upperbound, y  +=  upperbound;
    
int  ans  =   0 ;
    
while (x  +   1   <  y) {
        
if (x & 1 ) {  //  x是右子树 
            ans  =  (ans  +  st[x])  %  MOD;
            x
++ ;
        }
        
if (y & 1 ) {  //  y是右子树
            y -- ;
            ans 
=  (ans  +  st[y])  %  MOD;
        }
        x 
>>=   1 ;
        y 
>>=   1 ;
    }
    
if (x  <  y) 
        ans 
=  (ans  +  st[x])  %  MOD;
    
return  ans;
}

void  ins( int  x,  int  a) {
    x 
+=  upperbound;
    
while (x  >   0 ) {
        st[x] 
=  (st[x]  +  a)  %  MOD;
        x 
>>=   1 ;
    }
}

void  solve() {
    memset(st, 
0 sizeof (st));
    sort(S, S 
+  n);
    map
< int int >  mm;
    
int  i, j  =   0 , ans  =   0 ;
    
for (i  =   0 ; i  <  n;  ++ i) {
        
if ( ! mm.count(S[i])) {
            mm[S[i]] 
=   ++ j;
        }
    }
    ins(
0 1 );
    
for (i  =   0 ; i  <  n;  ++ i) {
        A[i] 
=  mm[A[i]];
        
int  sum  =   get ( 0 , A[i]);
        ans 
=  (ans  +  sum)  %  MOD;
        ins(A[i], sum);
    }
    printf(
" %d/n " , ans);
}

void  solve2() {
    
int  i, j, k;
    
for (i  =   0 ; i  <  n;  ++ i) dp[i]  =   1 ;
    
for (i  =   1 ; i  <  n;  ++ i) {
        
for (j  =   0 ; j  <  i;  ++ j) {
            
if (S[j]  <  S[i]) {
                dp[i] 
+=  dp[j];
                dp[i] 
%=  MOD;
            }
        }
    }
    LL sum 
=   0 ;
    
for (i  =   0 ; i  <  n;  ++ i) {
        sum 
+=  dp[i];
        sum 
%=  MOD;
    }
    printf(
" %I64d/n " , sum);
}

int  main()
{
//     freopen("C-large.in", "r", stdin);
//     freopen("C-large.txt", "w", stdout);

    
int  ntc, i, j, k, tc = 0 ;
    scanf(
" %d " & ntc);
    
while (ntc -- ) {
        printf(
" Case #%d:  " ++ tc);
        scanf(
" %d%d%d%d%d " & n,  & m,  & X,  & Y,  & Z);
        
for (i  =   0 ; i  <  m;  ++ i) scanf( " %d " , A + i);
        generate();
//         solve2();
        solve();
    }
    
return   0 ;
}

 

文章来自:http://www.cppblog.com/sicheng/archive/2008/08/02/57824.html

基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制问题,并提供完整的Matlab代码实现。文章结合数据驱动方法与Koopman算子理论,利用递归神经网络(RNN)对非线性系统进行建模与线性化处理,从而提升纳米级定位系统的精度与动态响应性能。该方法通过提取系统隐含动态特征,构建近似线性模型,便于后续模型预测控制(MPC)的设计与优化,适用于高精度自动化控制场景。文中还展示了相关实验验证与仿真结果,证明了该方法的有效性和先进性。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事精密控制、智能制造、自动化或相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能控制设计;②为非线性系统建模与线性化提供一种结合深度学习与现代控制理论的新思路;③帮助读者掌握Koopman算子、RNN建模与模型预测控制的综合应用。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现流程,重点关注数据预处理、RNN结构设计、Koopman观测矩阵构建及MPC控制器集成等关键环节,并可通过更换实际系统数据进行迁移验证,深化对方法泛化能力的理解。
#include <iostream> #include <vector> #include <cmath> #include <algorithm> #include <stdexcept> #include <fstream> #include <iomanip> #include <limits> using namespace std; const double MIN_RADIUS = 10.0; // 最小转弯半径约束 // 二维点结构 struct Point { double x, y; Point(double x = 0, double y = 0) : x(x), y(y) {} Point operator+(const Point& p) const { return Point(x + p.x, y + p.y); } Point operator-(const Point& p) const { return Point(x - p.x, y - p.y); } Point operator*(double s) const { return Point(x * s, y * s); } Point operator/(double s) const { return Point(x / s, y / s); } double dot(const Point& p) const { return x * p.x + y * p.y; } double cross(const Point& p) const { return x * p.y - y * p.x; } double norm() const { return sqrt(x*x + y*y); } Point normalized() const { double n = norm(); return n > 0 ? *this / n : *this; } Point perpendicular() const { return Point(-y, x); } // 垂直向量 friend ostream& operator<<(ostream& os, const Point& p) { os << "(" << p.x << ", " << p.y << ")"; return os; } }; // 圆弧段 struct ArcSegment { Point center; double radius; double start_angle; // 弧度 double end_angle; // 弧度 vector<Point> generatePoints(int num_points = 30) const { vector<Point> points; double angle_step = (end_angle - start_angle) / num_points; for (int i = 0; i <= num_points; i++) { double angle = start_angle + i * angle_step; double x = center.x + radius * cos(angle); double y = center.y + radius * sin(angle); points.push_back(Point(x, y)); } return points; } }; // 直线段 struct LineSegment { Point start; Point end; vector<Point> generatePoints(int num_points = 30) const { vector<Point> points; Point dir = (end - start); double length = dir.norm(); if (length < 1e-10) return points; dir = dir / length; for (int i = 0; i <= num_points; i++) { double t = static_cast<double>(i) / num_points; Point p = start + dir * (t * length); points.push_back(p); } return points; } }; // 路径规划器 class PathPlanner { private: double min_radius; public: PathPlanner(double min_radius = MIN_RADIUS) : min_radius(min_radius) {} // 生成满足转弯半径的几何路径 vector<Point> generateGeometricPath(const Point& start, const Point& end) { // 设计关键点 Point A = start; Point B(-2, 4); // 第一个转弯起点 Point C(4, 11); // 第二个转弯起点 Point D = end; // 计算转弯参数 - 确保半径至少为min_radius double R = min_radius * 1.5; // 使用1.5倍最小转弯半径 // 第一个转弯:从垂直转向水平 Point center1(B.x + R, B.y); double angle1_start = M_PI; // 180度 double angle1_end = M_PI_2; // 90度 // 第二个转弯:从水平转向垂直 Point center2(C.x, C.y - R); double angle2_start = -M_PI_2; // -90度 double angle2_end = 0; // 0度 // 生成路径点 vector<Point> path_points; // 第一段直线: A to B LineSegment line1 = {A, B}; vector<Point> line1_points = line1.generatePoints(40); path_points.insert(path_points.end(), line1_points.begin(), line1_points.end()); // 第一段圆弧: B to arc_end1 ArcSegment arc1 = {center1, R, angle1_start, angle1_end}; Point arc_end1(center1.x, center1.y - R); vector<Point> arc1_points = arc1.generatePoints(50); path_points.insert(path_points.end(), arc1_points.begin(), arc1_points.end()); // 第二段直线: arc_end1 to arc_start2 Point arc_start2(center2.x - R, center2.y); LineSegment line2 = {arc_end1, arc_start2}; vector<Point> line2_points = line2.generatePoints(40); path_points.insert(path_points.end(), line2_points.begin(), line2_points.end()); // 第二段圆弧: arc_start2 to C ArcSegment arc2 = {center2, R, angle2_start, angle2_end}; vector<Point> arc2_points = arc2.generatePoints(50); path_points.insert(path_points.end(), arc2_points.begin(), arc2_points.end()); // 第三段直线: C to D LineSegment line3 = {C, D}; vector<Point> line3_points = line3.generatePoints(40); path_points.insert(path_points.end(), line3_points.begin(), line3_points.end()); return path_points; } // 从几何路径生成贝塞尔曲线控制点(增加控制点数量) vector<Point> generateBezierControlPoints(const vector<Point>& path_points) { vector<Point> control_points; if (path_points.size() < 4) return control_points; // 增加控制点数量 - 每10个路径点取一个控制点 int step = max(1, (int)path_points.size() / 20); for (int i = 0; i < path_points.size(); i += step) { control_points.push_back(path_points[i]); } // 确保包含起点和终点 if (control_points.back() != path_points.back()) { control_points.push_back(path_points.back()); } // 在转弯区域添加额外控制点 int turn1_start = path_points.size() * 40 / 100; // 第一个转弯开始位置 int turn1_end = path_points.size() * 50 / 100; // 第一个转弯结束位置 int turn2_start = path_points.size() * 70 / 100; // 第二个转弯开始位置 int turn2_end = path_points.size() * 80 / 100; // 第二个转弯结束位置 // 添加第一个转弯区域的额外控制点 for (int i = turn1_start; i <= turn1_end; i += step/2) { if (i < path_points.size()) { control_points.push_back(path_points[i]); } } // 添加第二个转弯区域的额外控制点 for (int i = turn2_start; i <= turn2_end; i += step/2) { if (i < path_points.size()) { control_points.push_back(path_points[i]); } } // 排序并去重 sort(control_points.begin(), control_points.end(), [](const Point& a, const Point& b) { if (a.x != b.x) return a.x < b.x; return a.y < b.y; }); auto last = unique(control_points.begin(), control_points.end(), [](const Point& a, const Point& b) { return abs(a.x - b.x) < 0.1 && abs(a.y - b.y) < 0.1; }); control_points.erase(last, control_points.end()); // 确保控制点间距足够 for (int i = 1; i < control_points.size() - 1; i++) { double dist1 = (control_points[i] - control_points[i-1]).norm(); double dist2 = (control_points[i+1] - control_points[i]).norm(); // 如果间距小于转弯半径,调整位置 if (dist1 < min_radius || dist2 < min_radius) { Point dir = (control_points[i+1] - control_points[i-1]).normalized(); control_points[i] = control_points[i-1] + dir * min_radius * 1.5; } } return control_points; } }; // 贝塞尔曲线类 class BezierCurve { private: vector<Point> controlPoints; // 计算二项式系数 double binomialCoefficient(int n, int k) const { if (k < 0 || k > n) return 0; if (k == 0 || k == n) return 1; double result = 1; for (int i = 1; i <= k; i++) { result = result * (n - i + 1) / i; } return result; } // 计算伯恩斯坦基函数 double bernstein(int n, int i, double t) const { return binomialCoefficient(n, i) * pow(t, i) * pow(1 - t, n - i); } public: BezierCurve(const vector<Point>& points) : controlPoints(points) { if (points.size() < 2) { throw invalid_argument("At least 2 control points required"); } } // 计算曲线上的点 Point evaluate(double t) const { if (t < 0 || t > 1) { t = max(0.0, min(1.0, t)); } Point result(0, 0); int n = controlPoints.size() - 1; for (int i = 0; i <= n; i++) { double basis = bernstein(n, i, t); result = result + controlPoints[i] * basis; } return result; } // 计算一阶导数(速度) Point derivative(double t) const { if (t < 0 || t > 1) { t = max(0.0, min(1.0, t)); } Point result(0, 0); int n = controlPoints.size() - 1; for (int i = 0; i < n; i++) { double basis = bernstein(n - 1, i, t); Point diff = controlPoints[i + 1] - controlPoints[i]; result = result + diff * (n * basis); } return result; } // 计算二阶导数(加速度) Point secondDerivative(double t) const { if (t < 0 || t > 1) { t = max(0.0, min(1.0, t)); } Point result(0, 0); int n = controlPoints.size() - 1; if (n < 2) { return result; } for (int i = 0; i < n - 1; i++) { double basis = bernstein(n - 2, i, t); Point diff = (controlPoints[i + 2] - controlPoints[i + 1]) - (controlPoints[i + 1] - controlPoints[i]); result = result + diff * (n * (n - 1) * basis); } return result; } // 计算曲率 double curvature(double t) const { Point vel = derivative(t); Point acc = secondDerivative(t); double speed = vel.norm(); if (speed < 1e-10) { return numeric_limits<double>::infinity(); } double crossProd = fabs(vel.cross(acc)); return crossProd / pow(speed, 3); } // 计算曲率半径 double curvatureRadius(double t) const { double k = curvature(t); if (k < 1e-10 || k > 1e10) { return numeric_limits<double>::infinity(); } return 1.0 / k; } // 检查曲率约束 bool checkCurvatureConstraint(double minRadius, int samples = 200) const { for (int i = 0; i <= samples; i++) { double t = static_cast<double>(i) / samples; double radius = curvatureRadius(t); if (radius < minRadius && !isinf(radius)) { cerr << "Curvature violation at t=" << t << ": radius=" << radius << "m < minRadius=" << minRadius << "m" << endl; return false; } } return true; } // 获取曲线上的点集 vector<Point> getCurvePoints(int samples = 200) const { vector<Point> points; points.reserve(samples + 1); for (int i = 0; i <= samples; i++) { double t = static_cast<double>(i) / samples; points.push_back(evaluate(t)); } return points; } // 获取最小曲率半径 double getMinCurvatureRadius(int samples = 500) const { double minRadius = numeric_limits<double>::infinity(); for (int i = 0; i <= samples; i++) { double t = static_cast<double>(i) / samples; double radius = curvatureRadius(t); if (!isinf(radius) { if (radius < minRadius) { minRadius = radius; // 调试信息 // cerr << "New min radius: " << minRadius << " at t=" << t << endl; } } } return minRadius; } }; // 导出点集到CSV文件 void exportToCSV(const string& filename, const vector<Point>& points) { ofstream file(filename); if (!file) { cerr << "Error opening file: " << filename << endl; return; } file << "x,y" << endl; file << fixed << setprecision(6); for (const auto& p : points) { file << p.x << "," << p.y << endl; } cout << "Exported " << points.size() << " points to " << filename << endl; } // 可视化路径 void visualizePath() { ofstream script("visualize.py"); if (!script) { cerr << "Error creating visualization script" << endl; return; } script << R"( import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Circle # 加载数据 control = np.genfromtxt('control_points.csv', delimiter=',', skip_header=1) original = np.genfromtxt('original_curve.csv', delimiter=',', skip_header=1) geometric = np.genfromtxt('geometric_path.csv', delimiter=',', skip_header=1) constrained_controls = np.genfromtxt('constrained_control_points.csv', delimiter=',', skip_header=1) constrained = np.genfromtxt('constrained_curve.csv', delimiter=',', skip_header=1) # 可视化 plt.figure(figsize=(12, 10)) plt.plot(control[:,0], control[:,1], 'ro-', markersize=4, label='Original Control Points') plt.plot(original[:,0], original[:,1], 'b-', linewidth=1.5, alpha=0.7, label='Original Curve') plt.plot(geometric[:,0], geometric[:,1], 'm--', linewidth=2, alpha=0.5, label='Geometric Path') plt.plot(constrained_controls[:,0], constrained_controls[:,1], 'go-', markersize=5, label='Constrained Control Points') plt.plot(constrained[:,0], constrained[:,1], 'g-', linewidth=2.5, label='Constrained Curve') # 添加标签和标题 plt.legend(loc='upper left') plt.grid(True) plt.axis('equal') plt.title('Path Planning with Curvature Constraint (Min Radius: 10m)') plt.xlabel('X') plt.ylabel('Y') # 显示曲率约束区域 plt.text(-3, 16, "Min Turning Radius: 10m", fontsize=12, bbox=dict(facecolor='white', alpha=0.8)) # 绘制转弯半径圆 circle1 = Circle((-2+15, 4), 10, color='orange', fill=False, linestyle='--', label='Turning Radius (15m)') circle2 = Circle((4, 11-15), 10, color='purple', fill=False, linestyle='--') plt.gca().add_patch(circle1) plt.gca().add_patch(circle2) # 添加圆弧中心标记 plt.plot(-2+15, 4, 'yo', markersize=8, label='Turn Center 1') plt.plot(4, 11-15, 'co', markersize=8, label='Turn Center 2') # 保存并显示 plt.savefig('path_comparison.png', dpi=300) plt.show() )"; cout << "Visualization script saved as visualize.py" << endl; cout << "Run 'python visualize.py' to generate the plot" << endl; } int main() { // 测试用例控制点 vector<Point> controlPoints = { {-2, 0}, {-2, 0.5}, {-2, 1}, {-2, 1.5}, {-2, 2}, {-2, 2.5}, {-2, 3}, {-2, 3.5}, {-2, 4}, {4, 11}, {4, 11.5}, {4, 12}, {4, 12.5}, {4, 13}, {4, 13.5}, {4, 14}, {4, 14.5}, {4, 15} }; try { // 1. 创建原始贝塞尔曲线 BezierCurve originalCurve(controlPoints); // 检查原始曲线的曲率约束 bool originalSatisfies = originalCurve.checkCurvatureConstraint(MIN_RADIUS); double originalMinRadius = originalCurve.getMinCurvatureRadius(); cout << "Original curve min curvature radius: " << originalMinRadius << " m" << endl; cout << "Original curve satisfies curvature constraint: " << (originalSatisfies ? "YES" : "NO") << endl; // 导出原始曲线和控制点 exportToCSV("control_points.csv", controlPoints); vector<Point> originalCurvePoints = originalCurve.getCurvePoints(200); exportToCSV("original_curve.csv", originalCurvePoints); // 2. 创建几何路径和满足约束的贝塞尔曲线 PathPlanner planner(MIN_RADIUS); // 生成几何路径(直线+圆弧) vector<Point> geometricPath = planner.generateGeometricPath(controlPoints[0], controlPoints.back()); exportToCSV("geometric_path.csv", geometricPath); // 从几何路径生成控制点(增加控制点数量) vector<Point> constrainedControlPoints = planner.generateBezierControlPoints(geometricPath); exportToCSV("constrained_control_points.csv", constrainedControlPoints); // 创建约束曲线 BezierCurve constrainedCurve(constrainedControlPoints); // 检查优化后的曲线 bool constrainedSatisfies = constrainedCurve.checkCurvatureConstraint(MIN_RADIUS); double constrainedMinRadius = constrainedCurve.getMinCurvatureRadius(); cout << "\nConstrained curve min curvature radius: " << constrainedMinRadius << " m" << endl; cout << "Constrained curve satisfies curvature constraint: " << (constrainedSatisfies ? "YES" : "NO") << endl; // 导出优化后的曲线 vector<Point> constrainedCurvePoints = constrainedCurve.getCurvePoints(200); exportToCSV("constrained_curve.csv", constrainedCurvePoints); // 3. 生成可视化脚本 visualizePath(); if (constrainedSatisfies) { cout << "\nSUCCESS: Generated path satisfies minimum turning radius of " << MIN_RADIUS << " meters with " << constrainedControlPoints.size() << " control points." << endl; } else { cerr << "\nWARNING: Curve does not fully satisfy curvature constraints. " << "Consider further increasing the number of control points." << endl; } } catch (const exception& e) { cerr << "Error: " << e.what() << endl; return 1; } return 0; }最小转弯半径为10m,优化一下它的几何路径
06-01
<think>好的,用户的问题是关于如何在C++中通过调整控制点和贝塞尔曲线优化几何路径,以满足最小转弯半径10米的约束。我需要结合之前的对话和提供的引用内容来构建答案。首先,回顾用户之前的提问,他们了解了几何路径的不同定义,现在转向优化问题。用户提到的引用中,特别是引用[2]关于Dubins曲线和引用[3]、[4]关于路径规划和优化的部分,应该与当前问题相关。用户需要的是具体的方法,如何在C++中实现贝塞尔曲线的调整。贝塞尔曲线的控制点调整是关键,因为控制点决定了曲线的形状。而最小转弯半径涉及到曲线的曲率,所以需要计算曲率并确保其不超过限制(即曲率半径不小于10米)。 首先,需要解释贝塞尔曲线的基本概念,尤其是曲率的计算。贝塞尔曲线的曲率公式需要给出,可能需要用二阶导数。然后,如何通过调整控制点来满足曲率约束,这可能涉及到优化算法,比如梯度下降或者遗传算法,来迭代调整控制点,使得曲率在整条曲线上都满足条件。 另外,引用[3]中提到解的形式、解的空间和解的评价,这可能对应到贝塞尔曲线的参数化(解的形式)、控制点的调整范围(解的空间),以及成本函数的设计(解的评价)。需要将这些概念融入回答中,说明如何构建优化问题。引用[4]提到人工势场法,虽然APF可能不是直接的方法,但其中的成本函数和迭代优化思想可以借鉴。比如,将曲率约束作为斥力,目标路径作为引力,进行优化。接下来,步骤可能包括:1.定义贝塞尔曲线的参数化,使用控制点。2.计算曲率,并确保所有点的曲率半径≥10米。 3.设计优化问题,包括目标函数(如路径长度)和约束条件(曲率)。4.在C++中实现优化算法,调整控制点以满足约束。 需要给出具体的数学公式,比如曲率的表达式,以及优化问题的数学描述。然后提供C++代码示例,展示如何计算曲率和优化控制点。可能还需要考虑数值优化方法,比如使用非线性求解器或者遗传算法。还要注意用户提到的系统指令,正确使用LaTeX格式,引用相关文献,如引用[2]和[3]。同时生成相关问题,比如关于曲率计算、优化算法选择或其他路径优化方法的问题。 需要验证思路是否正确,比如贝塞尔曲线的曲率计算是否正确,优化方法是否可行。可能存在多个控制点,调整时如何高效计算和约束所有点的曲率,可能需要离散化曲线,采样多个点进行检查。另外,可能需要考虑贝塞尔曲线的阶次,高阶曲线可能更灵活,但计算更复杂。或者使用B样条曲线,如引用[3]提到的曲线优化,但用户明确提到贝塞尔,所以应专注于此。总结,回答需要分步骤,结合数学公式和代码示例,引用相关文献,并确保满足用户的所有要求。</think>### 分步解决方案:贝塞尔曲线优化路径满足最小转弯半径约束 --- #### **1. 贝塞尔曲线与曲率计算** 贝塞尔曲线的$n$阶参数化形式为: $$B(t) = \sum_{i=0}^n \binom{n}{i} (1-t)^{n-i} t^i P_i, \quad t \in [0,1]$$ 其中$P_i$为控制点坐标。曲率$\kappa(t)$的计算公式为: $$\kappa(t) = \frac{\|B'(t) \times B''(t)\|}{\|B'(t)\|^3}$$ 最小转弯半径$R_{\text{min}} = 10$米对应曲率约束$\kappa(t) \leq 1/R_{\text{min}} = 0.1$[^2]。 --- #### **2. 控制点调整策略** 通过优化控制点位置使整条曲线的最大曲率满足约束: 1. **参数化控制点**: 将控制点坐标$(x_i,y_i)$作为优化变量 2. **曲率约束**: 对曲线进行离散采样,确保所有采样点满足$\kappa(t_j) \leq 0.1$ 3. **优化目标**: 最小化路径长度或控制点移动距离 4. **优化方法**: 使用非线性优化算法(如Levenberg-Marquardt) ```cpp // C++示例:计算贝塞尔曲线曲率 struct Point { double x, y; }; Point bezier_derivative(const vector<Point>& ctrl_pts, double t, int order) { // 实现贝塞尔曲线导数的递推计算 } double compute_curvature(const vector<Point>& ctrl_pts, double t) { Point B_prime = bezier_derivative(ctrl_pts, t, 1); Point B_double_prime = bezier_derivative(ctrl_pts, t, 2); double cross = B_prime.x * B_double_prime.y - B_prime.y * B_double_prime.x; return abs(cross) / pow(B_prime.x*B_prime.x + B_prime.y*B_prime.y, 1.5); } ``` --- #### **3. 优化问题建模** 建立带约束的非线性规划问题: $$ \begin{aligned} \text{minimize} \quad & \sum_{i=1}^n \|P_i - P_{i}^{\text{init}}\|^2 \\ \text{subject to} \quad & \kappa(t_j) \leq 0.1, \quad j=1,...,N_{\text{samples}} \end{aligned} $$ 其中$P_{i}^{\text{init}}$为初始控制点位置[^3]。 --- #### **4. 实现步骤** 1. **初始化路径**: 生成初始贝塞尔曲线控制点 2. **曲率采样**: 在$t \in [0,1]$区间均匀采样100-200个点 3. **约束检查**: 标记违反$\kappa > 0.1$的采样点 4. **迭代优化**: - 使用C++库(如Ceres Solver)处理带约束优化 - 调整控制点使违反约束的采样点数量减少 5. **收敛条件**: 所有采样点满足曲率约束 --- #### **5. 关键优化技巧** - **控制点对称约束**: 保持路径对称性时添加等式约束 - **曲率敏感点加强采样**: 在曲率变化剧烈区域增加采样密度 - **多目标优化**: 同时优化路径长度和平滑度 - **热启动策略**: 用Dubins曲线生成初始路径[^2] --- ### 示例代码框架 ```cpp #include <ceres/ceres.h> struct CurvatureConstraint { CurvatureConstraint(const vector<Point>& ctrl, double t) : ctrl_pts(ctrl), t(t) {} template<typename T> bool operator()(const T* const params, T* residual) const { // 将参数映射到控制点 // 计算曲率并施加约束 residual[0] = T(max_curvature) - computed_curvature; return true; } private: vector<Point> ctrl_pts; double t; }; // 主优化逻辑 void optimize_path(vector<Point>& ctrl_pts) { ceres::Problem problem; for (auto t : sampled_points) { problem.AddResidualBlock( new CurvatureConstraint(ctrl_pts, t), nullptr, ctrl_pts.data() ); } // 调用求解器 } ``` --- ###
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