在python中，使用生物激励神经网络算法进行水下AUV路径规划和避障算法设计

生物激励神经网络算法可以用于水下AUV的路径规划和避障算法设计。下面是一个简单的示例代码：

import numpy as np
import math

class BiologicalRL:
    def __init__(self, input_size, output_size, alpha=0.1, gamma=0.9, epsilon=0.1):
        self.input_size = input_size
        self.output_size = output_size
        self.alpha = alpha  # 学习率
        self.gamma = gamma  # 折扣因子
        self.epsilon = epsilon  # 探索概率
        
        # 初始化权重矩阵
        self.weights = np.random.rand(input_size, output_size)
        
    def predict(self, state):
        q_values = np.dot(state, self.weights)
        return np.argmax(q_values)
        
    def train(self, state, action, reward, next_state, done):
        # 计算预测值和目标值
        q_values = np.dot(state, self.weights)
        q_value = q_values[action]
        next_q_values = np.dot(next_state, self.weights)
        next_q_value = np.max(next_q_values)
        target = reward + self.gamma * next_q_value * (1 - done)
        
        # 更新权重矩阵
        delta = target - q_value
        self.weights[action] += self.alpha * delta * state
        
    def act(self, state):
        if np.random.rand() < self.epsilon:
            return np.random.randint(self.output_size)
        else:
            return self.predict(state)

class AUV:
    def __init__(self, n_states, n_actions):
        self.n_states = n_states
        self.n_actions = n_actions
        self.current_state = None
        self.current_action = None
        self.current_reward = None
        self.next_state = None
        self.done = False
        self.q_table = BiologicalRL(n_states, n_actions)

    def get_state(self, sensor_data):
        """
        获取状态
        """
        state = np.zeros(self.n_states)
        state[0] = sensor_data[0]  # 水温
        state[1] = sensor_data[1]  # 水深
        state[2] = sensor_data[2]  # 水流速度
        state[3] = sensor_data[3]  # 声呐数据
        state[4] = sensor_data[4]  # AUV当前位置
        return state

    def get_reward(self, sensor_data):
        """
        获取奖励
        """
        reward = -1  # 默认奖励为-1
        if sensor_data[0] > 25:  # 如果水温过高，奖励减少
            reward -= 1
        if sensor_data[1] < 10:  # 如果水深过浅，奖励减少
            reward -= 1
        if sensor_data[2] > 5:  # 如果水流速度过快，奖励减少
            reward -= 1
        if sensor_data[3] < 2:  # 如果声呐数据异常，奖励减少
            reward -= 1
        if sensor_data[4] > 100:  # 如果AUV距离目标过远，奖励减少
            reward -= 1
        if sensor_data[4] < 10:  # 如果AUV距离目标过近，奖励减少
            reward -= 1
        return reward

    def act(self, sensor_data):
        """
        AUV行动
        """
        state = self.get_state(sensor_data)
        action = self.q_table.act(state)
        self.current_state = state
        self.current_action = action
        return action

    def update(self, sensor_data):
        """
        更新Q值
        """
        next_state = self.get_state(sensor_data)
        reward = self.get_reward(sensor_data)
        self.current_reward = reward
        self.next_state = next_state
        self.q_table.train(self.current_state, self.current_action, reward, self.next_state, self.done)
        self.current_state = self.next_state

    def stop(self):
        self.done = True

class Environment:
    def __init__(self):
        self.width = 1000  # 模拟环境宽度
        self.height = 1000  # 模拟环境高度
        self.target_x = self.width/2  # 目标点x坐标
        self.target_y = self.height/2  # 目标点y坐标
        self.obstacles = [(300, 300, 100), (500, 500, 150), (700, 200, 80)]  # 障碍物列表
        self.auv = AUV(5, 4)  # 创建AUV对象

    def get_sensor_data(self):
        """
        获取传感器数据
        """
        x, y = self.get_auv_pos()
        water_temp = self.get_water_temp(x, y)
        water_depth = self.get_water_depth(x, y)
        water_flow_speed = self.get_water_flow_speed(x, y)
        sonar_data = self.get_sonar_data(x, y)
        return (water_temp, water_depth, water_flow_speed, sonar_data, self.get_distance_to_target(x, y))

    def get_auv_pos(self):
        """
        获取AUV当前位置
        """
        return (500, 500)  # 暂时固定AUV位置

    def get_water_temp(self, x, y):
        """
        获取水温
        """
        return 20  # 暂时固定水温

    def get_water_depth(self, x, y):
        """
        获取水深
        """
        return 50  # 暂时固定水深

    def get_water_flow_speed(self, x, y):
        """
        获取水流速度
        """
        return 2  # 暂时固定水流速度

    def get_sonar_data(self, x, y):
        """
        获取声呐数据
        """
        return 3  # 暂时固定声呐数据

    def get_distance_to_target(self, x, y):
        """
        获取AUV到目标点的距离
        """
        return math.sqrt((x - self.target_x)**2 + (y - self.target_y)**2)

    def is_obstacle(self, x, y):
        """
        判断是否碰撞到障碍物
        """
        for obstacle in self.obstacles:
            dist = math.sqrt((x - obstacle[0])**2 + (y - obstacle[1])**2)
            if dist < obstacle[2]:
                return True
        return False

    def is_reached_target(self, x, y):
        """
        判断是否到达目标点
        """
        dist = math.sqrt((x - self.target_x)**2 + (y - self.target_y)**2)
        return dist < 10

    def run(self):
        """
        运行模拟环境
        """
        for i in range(1000):
            sensor_data = self.get_sensor_data()  # 获取传感器数据
            action = self.auv.act(sensor_data)  # AUV行动
            x, y = self.get_auv_pos()
            if action == 0:  # 向上移动
                y -= 10
            elif action == 1:  # 向下移动
                y += 10
            elif action == 2:  # 向左移动
                x -= 10
            elif action == 3:  # 向右移动
                x += 10
            if self.is_obstacle(x, y):  # 碰撞到障碍物
                self.auv.stop()
            elif self.is_reached_target(x, y):  # 到达目标点
                self.auv.stop()
            else:
                self.auv.update(sensor_data)  # 更新Q值

这个代码实现了一个简单的模拟环境，其中包括了AUV、环境、障碍物和目标点。AUV通过获取传感器数据来获取环境信息，然后根据Q-learning算法来选择下一个动作。在每个时间步中，AUV会获取传感器数据、选择动作、更新Q值。如果碰撞到障碍物或到达目标点，AUV会停止运动。