生成基于栅格地图BiLSTM算法的路径规划的python代码

以下是一个简单的基于栅格地图BiLSTM算法的路径规划Python代码，供您参考：

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim

# 定义BiLSTM模型
class BiLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(BiLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size, bidirectional=True)
        self.out = nn.Linear(hidden_size * 2, output_size)

    def forward(self, input):
        output, (hidden, cell) = self.lstm(input)
        output = self.out(output.view(len(input), -1))
        return output

# 定义路径规划类
class PathPlanner():
    def __init__(self, input_size, hidden_size, output_size, lr):
        self.bilstm = BiLSTM(input_size, hidden_size, output_size)
        self.optimizer = optim.Adam(self.bilstm.parameters(), lr=lr)
        self.criterion = nn.CrossEntropyLoss()

    def train(self, input, target):
        self.bilstm.zero_grad()
        output = self.bilstm(input)
        loss = self.criterion(output, target)
        loss.backward()
        self.optimizer.step()
        return loss.item()

    def predict(self, input):
        output = self.bilstm(input)
        return torch.argmax(output, dim=1)

# 定义数据准备函数
def prepare_data(data):
    input = torch.from_numpy(data[:, :-1]).float()
    target = torch.from_numpy(data[:, -1]).long()
    return input, target

# 定义训练函数
def train(planner, data, epochs, batch_size):
    for epoch in range(epochs):
        loss_sum = 0
        for i in range(0, len(data), batch_size):
            input, target = prepare_data(data[i:i+batch_size])
            loss = planner.train(input, target)
            loss_sum += loss
        print("Epoch {}: Loss = {:.5f}".format(epoch+1, loss_sum))

# 测试代码
if __name__ == '__main__':
    # 假设有一个20*20的栅格地图，其中1表示障碍物，0表示通路
    grid_map = np.zeros((20, 20))
    grid_map[5:15, 5:15] = 1

    # 假设起点为(1, 1)，终点为(18, 18)
    start, end = (1, 1), (18, 18)

    # 生成训练数据
    data = []
    for i in range(20):
        for j in range(20):
            if grid_map[i][j] == 0:
                input_data = np.zeros((20, 20))
                input_data[start[0]][start[1]] = 1
                input_data[end[0]][end[1]] = 2
                input_data[i][j] = 3
                target_data = np.array([i*20+j])
                data.append(np.concatenate((input_data.flatten(), target_data)))
    data = np.array(data)

    # 定义路径规划器
    planner = PathPlanner(input_size=400, hidden_size=64, output_size=400, lr=0.001)

    # 训练路径规划器
    train(planner, data, epochs=100, batch_size=32)

    # 测试路径规划器
    input_data = np.zeros((20, 20))
    input_data[start[0]][start[1]] = 1
    input_data[end[0]][end[1]] = 2
    input_data[5][5] = 3
    input_data = torch.from_numpy(input_data.flatten()).float()
    output_data = planner.predict(input_data)
    print("Predicted path: {}".format((output_data//20, output_data%20)))

该代码实现了一个简单的栅格地图路径规划器，使用BiLSTM模型进行训练和预测。在训练过程中，将每个栅格作为一个输入，将栅格的行列值作为输出，使用交叉熵损失函数进行优化。在测试过程中，将起点、终点和一个障碍物的位置作为输入，输出预测的路径。