用c++语言写一个DWA算法

好的，下面是一个简单的C++实现DWA算法的示例代码，仅供参考：

#include <iostream>
#include <vector>
#include <cmath>
#include <algorithm>

using namespace std;

// 定义机器人的状态结构体
struct RobotState {
    double x, y, theta, v, w;
};

// 定义目标状态结构体
struct TargetState {
    double x, y, theta;
};

// 定义障碍物结构体
struct Obstacle {
    double x, y, r;
};

// 定义动态窗口结构体
struct DynamicWindow {
    double v_min, v_max, w_min, w_max;
    vector<double> vs, ws;
};

// 定义DWA算法类
class DWA {
public:
    DWA(double _dt, double _v_min, double _v_max, double _w_min, double _w_max,
        double _a_max, double _yaw_rate_max, double _dist_eps, double _angle_eps)
        : dt(_dt), v_min(_v_min), v_max(_v_max), w_min(_w_min), w_max(_w_max),
          a_max(_a_max), yaw_rate_max(_yaw_rate_max), dist_eps(_dist_eps), angle_eps(_angle_eps) {}

    // 计算机器人当前状态与目标状态之间的距离和角度差
    void calcDistAndAngle(const RobotState& robot_state, const TargetState& target_state,
                          double& dist, double& angle) {
        dist = sqrt(pow(target_state.x - robot_state.x, 2) + pow(target_state.y - robot_state.y, 2));
        angle = atan2(target_state.y - robot_state.y, target_state.x - robot_state.x) - robot_state.theta;
        angle = normalizeAngle(angle);
    }

    // 计算机器人在某个速度和转向角下的运动轨迹
    vector<RobotState> calcTrajectory(const RobotState& robot_state, double v, double w) {
        vector<RobotState> traj;
        traj.push_back(robot_state);
        for (int i = 0; i < 10; ++i) {
            RobotState next_state;
            next_state.x = traj.back().x + v * cos(traj.back().theta) * dt;
            next_state.y = traj.back().y + v * sin(traj.back().theta) * dt;
            next_state.theta = traj.back().theta + w * dt;
            next_state.v = v;
            next_state.w = w;
            traj.push_back(next_state);
        }
        return traj;
    }

    // 计算机器人在某个速度和转向角下的评分
    double calcScore(const RobotState& robot_state, const TargetState& target_state,
                     const vector<Obstacle>& obstacles, double v, double w) {
        double dist, angle;
        calcDistAndAngle(robot_state, target_state, dist, angle);
        if (dist > dist_eps || fabs(angle) > angle_eps) {
            return 0.0;
        }
        // 计算机器人在当前速度和转向角下的运动轨迹
        vector<RobotState> traj = calcTrajectory(robot_state, v, w);
        // 计算机器人与障碍物的最小距离
        double min_dist = INFINITY;
        for (const auto& obs : obstacles) {
            for (const auto& state : traj) {
                double d = sqrt(pow(obs.x - state.x, 2) + pow(obs.y - state.y, 2)) - obs.r;
                if (d < min_dist) {
                    min_dist = d;
                }
            }
        }
        // 根据最小距离和当前速度和转向角计算评分
        double dist_score = exp(-min_dist / dist_eps);
        double vel_score = (v - v_min) / (v_max - v_min);
        double omega_score = (w - w_min) / (w_max - w_min);
        return dist_score * vel_score * omega_score;
    }

    // 计算机器人的动态窗口
    DynamicWindow calcDynamicWindow(const RobotState& robot_state) {
        double v_prev = robot_state.v;
        double w_prev = robot_state.w;
        DynamicWindow dw;
        dw.v_min = max(v_prev - a_max * dt, v_min);
        dw.v_max = min(v_prev + a_max * dt, v_max);
        dw.w_min = max(w_prev - yaw_rate_max * dt, w_min);
        dw.w_max = min(w_prev + yaw_rate_max * dt, w_max);
        for (double v = dw.v_min; v <= dw.v_max; v += 0.05) {
            for (double w = dw.w_min; w <= dw.w_max; w += 0.1) {
                double score = calcScore(robot_state, target_state, obstacles, v, w);
                if (score > 0.0) {
                    dw.vs.push_back(v);
                    dw.ws.push_back(w);
                }
            }
        }
        return dw;
    }

    // 选择最优速度和转向角
    void selectBestVelocity(const RobotState& robot_state, const TargetState& target_state,
                            const vector<Obstacle>& obstacles, RobotState& next_state) {
        DynamicWindow dw = calcDynamicWindow(robot_state);
        double best_score = -INFINITY;
        double best_v = 0.0;
        double best_w = 0.0;
        for (int i = 0; i < dw.vs.size(); ++i) {
            double score = calcScore(robot_state, target_state, obstacles, dw.vs[i], dw.ws[i]);
            if (score > best_score) {
                best_score = score;
                best_v = dw.vs[i];
                best_w = dw.ws[i];
            }
        }
        next_state = robot_state;
        next_state.v = best_v;
        next_state.w = best_w;
        next_state.theta += best_w * dt;
        next_state.x += best_v * cos(next_state.theta) * dt;
        next_state.y += best_v * sin(next_state.theta) * dt;
    }

    // 角度归一化到[-pi, pi]
    double normalizeAngle(double angle) {
        return atan2(sin(angle), cos(angle));
    }

    // 设置目标状态
    void setTargetState(const TargetState& _target_state) {
        target_state = _target_state;
    }

    // 设置障碍物
    void setObstacles(const vector<Obstacle>& _obstacles) {
        obstacles = _obstacles;
    }

private:
    double dt;              // 时间步长
    double v_min, v_max;    // 速度的最小值和最大值
    double w_min, w_max;    // 转向角速度的最小值和最大值
    double a_max;           // 加速度的最大值
    double yaw_rate_max;    // 转向角速度的最大变化率
    double dist_eps;        // 距离容差
    double angle_eps;       // 角度容差
    TargetState target_state;   // 目标状态
    vector<Obstacle> obstacles; // 障碍物
};

int main() {
    // 初始化机器人状态、目标状态和障碍物
    RobotState robot_state = {0, 0, 0, 0, 0};
    TargetState target_state = {5, 5, 0};
    vector<Obstacle> obstacles = {{2, 2, 1}, {3, 4, 1}, {4, 2, 1}};

    // 初始化DWA算法
    DWA dwa(0.1, 0.0, 1.0, -M_PI / 2, M_PI / 2, 0.5, M_PI / 4, 0.1, 0.1);
    dwa.setTargetState(target_state);
    dwa.setObstacles(obstacles);

    // 执行DWA算法
    while (true) {
        RobotState next_state;
        dwa.selectBestVelocity(robot_state, target_state, obstacles, next_state);
        robot_state = next_state;
        if (sqrt(pow(target_state.x - robot_state.x, 2) + pow(target_state.y - robot_state.y, 2)) < 0.1) {
            break;
        }
        cout << "Robot state: (" << robot_state.x << ", " << robot_state.y << ", " << robot_state.theta << ")" << endl;
    }

    return 0;
}

该示例代码实现了一个简单的DWA算法，包括计算机器人与目标状态之间的距离和角度差、计算机器人在某个速度和转向角下的运动轨迹、计算机器人在某个速度和转向角下的评分、计算机器人的动态窗口、选择最优速度和转向角等功能。通过该示例代码，你可以了解到DWA算法的基本思想和实现方法。