rbf神经网络机械臂代码

以下是一个使用RBF神经网络控制机械臂的示例代码：

import numpy as np
from scipy.spatial.distance import cdist

class RBFNN:
    def __init__(self, hidden_size, output_size, sigma=1):
        self.hidden_size = hidden_size
        self.output_size = output_size
        self.sigma = sigma
        self.centers = None
        self.weights = None
    
    def fit(self, X, y):
        self.centers = X[np.random.choice(len(X), self.hidden_size, replace=False)]
        distances = cdist(X, self.centers)
        self.weights = np.linalg.pinv(distances) @ y
    
    def predict(self, X):
        distances = cdist(X, self.centers)
        return distances @ self.weights

# 机械臂的关节角度范围
joint_ranges = [
    (-np.pi, np.pi),  # joint 1
    (-np.pi/2, np.pi/2),  # joint 2
    (-np.pi/2, np.pi/2),  # joint 3
    (-np.pi/2, np.pi/2),  # joint 4
    (-np.pi/2, np.pi/2),  # joint 5
    (-np.pi, np.pi),  # joint 6
]

# 生成随机的关节角度
def random_joint_angles():
    return [np.random.uniform(joint_range[0], joint_range[1]) for joint_range in joint_ranges]

# 正向运动学计算末端位置
def forward_kinematics(joint_angles):
    # TODO: 根据机械臂的几何结构计算末端位置
    pass

# 逆向运动学计算关节角度
def inverse_kinematics(position):
    # TODO: 根据机械臂的几何结构和末端位置计算关节角度
    pass

# 生成数据集
X = []
y = []
for _ in range(1000):
    joint_angles = random_joint_angles()
    position = forward_kinematics(joint_angles)
    X.append(joint_angles)
    y.append(position)
X = np.array(X)
y = np.array(y)

# 训练 RBF 神经网络模型
rbf = RBFNN(hidden_size=50, output_size=3)
rbf.fit(X, y)

# 控制机械臂运动
while True:
    # 读取机械臂当前末端位置
    position = # TODO: 读取末端位置
    # 计算目标关节角度
    target_joint_angles = inverse_kinematics(position)
    # 使用 RBF 神经网络计算关节角度控制信号
    control_signal = rbf.predict(np.array([target_joint_angles]))[0]
    # 发送控制信号给机械臂
    # TODO: 发送控制信号