基于DWA算法实现路径规划的代码

以下是一个简单的基于DWA算法的路径规划示例代码，使用Python语言编写：

import numpy as np

# 定义机器人状态
class RobotState:
    def __init__(self, x=0, y=0, theta=0, v=0, omega=0):
        self.x = x
        self.y = y
        self.theta = theta
        self.v = v
        self.omega = omega

# 定义机器人模型
class RobotModel:
    def __init__(self, wheelbase=0.5, max_v=1.0, max_omega=1.0):
        self.wheelbase = wheelbase
        self.max_v = max_v
        self.max_omega = max_omega

    def update_state(self, state, v, omega, dt):
        state.x += v * np.cos(state.theta) * dt
        state.y += v * np.sin(state.theta) * dt
        state.theta += omega * dt
        state.v = v
        state.omega = omega

# 定义DWA算法
class DWAPlanner:
    def __init__(self, robot, dw=0.1, dt=0.1, vmax=1.0, wmax=1.0, dw_v=0.1, dw_w=0.1):
        self.robot = robot
        self.dw = dw
        self.dt = dt
        self.vmax = vmax
        self.wmax = wmax
        self.dw_v = dw_v
        self.dw_w = dw_w

    def predict_trajectory(self, state, v, omega):
        trajectory = [state]
        for i in range(10):
            new_state = RobotState()
            self.robot.update_state(new_state, v, omega, self.dt)
            trajectory.append(new_state)
        return trajectory

    def evaluate_trajectory(self, trajectory, goal):
        distance_to_goal = np.sqrt((trajectory[-1].x - goal.x)**2 + (trajectory[-1].y - goal.y)**2)
        heading_to_goal = np.arctan2(goal.y - trajectory[-1].y, goal.x - trajectory[-1].x)
        diff_heading = np.abs(heading_to_goal - trajectory[-1].theta)
        cost = distance_to_goal + diff_heading
        return cost

    def plan(self, state, goal, obstacle):
        best_trajectory = None
        min_cost = float('inf')
        for v in np.arange(0, self.vmax + self.dw_v, self.dw_v):
            for omega in np.arange(-self.wmax, self.wmax + self.dw_w, self.dw_w):
                trajectory = self.predict_trajectory(state, v, omega)
                if self.check_collision(trajectory, obstacle):
                    continue
                cost = self.evaluate_trajectory(trajectory, goal)
                if cost < min_cost:
                    best_trajectory = trajectory
                    min_cost = cost
        return best_trajectory

    def check_collision(self, trajectory, obstacle):
        for state in trajectory:
            if self.dist_to_obstacle(state, obstacle) < 0.5:
                return True
        return False

    def dist_to_obstacle(self, state, obstacle):
        return np.sqrt((state.x - obstacle.x)**2 + (state.y - obstacle.y)**2)

# 定义主函数
if __name__ == '__main__':
    robot = RobotModel()
    planner = DWAPlanner(robot)
    state = RobotState()
    goal = RobotState(x=5, y=5)
    obstacle = RobotState(x=3, y=3)
    trajectory = planner.plan(state, goal, obstacle)
    print(trajectory)

以上代码实现了一个简单的DWA路径规划器，其中机器人状态和模型、DWA算法、主函数等部分均有详细注释。需要注意的是，以上代码仅为示例，实际应用中可能需要根据具体情况进行修改和优化。