DWA路径规划算法代码

以下是一个Python实现的DWA路径规划算法的简单示例代码：

import numpy as np

# 机器人的动力学模型，这里简化为一个点质量模型，可以根据实际情况进行修改
class RobotModel:
    def __init__(self, max_speed, max_yaw_rate):
        self.max_speed = max_speed
        self.max_yaw_rate = max_yaw_rate

    def simulate(self, state, v, w, dt):
        x, y, yaw, _, _ = state
        x += v * np.cos(yaw) * dt
        y += v * np.sin(yaw) * dt
        yaw += w * dt
        return np.array([x, y, yaw, v, w])

# DWA路径规划算法
class DWAPlanner:
    def __init__(self, robot_model, max_speed, max_yaw_rate, max_accel, max_dyaw_rate, dt, predict_time, goal_tolerance):
        self.robot_model = robot_model
        self.max_speed = max_speed
        self.max_yaw_rate = max_yaw_rate
        self.max_accel = max_accel
        self.max_dyaw_rate = max_dyaw_rate
        self.dt = dt
        self.predict_time = predict_time
        self.goal_tolerance = goal_tolerance

    def plan(self, state, goal):
        # 动态窗口的范围
        v_min, v_max = 0, self.max_speed
        w_min, w_max = -self.max_yaw_rate, self.max_yaw_rate
        v_reso, w_reso = 0.1, 0.1

        # 动态窗口评价
        best_traj, best_score = None, float('-inf')
        for v in np.arange(v_min, v_max, v_reso):
            for w in np.arange(w_min, w_max, w_reso):
                traj = self.generate_trajectory(state, v, w)
                score = self.evaluate_trajectory(traj, goal)
                if score > best_score:
                    best_traj, best_score = traj, score

        return best_traj

    # 生成一条轨迹，根据机器人动力学模型模拟运动
    def generate_trajectory(self, state, v, w):
        traj = [state]
        for i in range(int(self.predict_time / self.dt)):
            state = self.robot_model.simulate(state, v, w, self.dt)
            traj.append(state)
        return np.array(traj)

    # 评价轨迹，根据预定义的代价函数进行评分
    def evaluate_trajectory(self, traj, goal):
        dist = np.linalg.norm(traj[-1][:2] - goal[:2])
        heading = np.abs(traj[-1][2] - goal[2])
        speed = traj[-1][3]
        score = np.exp(-dist / self.goal_tolerance) + np.exp(-heading) + np.exp(-speed / self.max_speed)
        return score

这只是一个简单的示例代码，实际上DWA路径规划算法的实现要考虑更多的细节和实际情况，需要在实际应用中进行修改和优化。