物理世界

import numpy as np
import os

# 模拟传感器输入与物理约束
SENSOR_DATA = np.random.rand(10)  # 替换为真实数据流
MAX_VELOCITY = float(os.getenv("MAX_VELOCITY", "2.0"))

def estimate_state(sensor_data):
    # 简化状态估计，实际应使用滤波或学习模型
    return {"position": sensor_data[:5], "velocity": sensor_data[5:]}

def apply_constraints(action, max_vel):
    return np.clip(action, -max_vel, max_vel)

def generate_control(state):
    # 简化控制策略，实际可接入 MPC 或 RL 模型
    return np.zeros_like(state["velocity"])

# 最小闭环
state = estimate_state(SENSOR_DATA)
raw_action = generate_control(state)
safe_action = apply_constraints(raw_action, MAX_VELOCITY)
print("Safe control output:", safe_action)

展示如何将状态输入、约束检查与控制指令生成串联为最小可用闭环

{
  "timestamp": "2024-06-15T10:30:00Z",
  "state_estimate": {
    "position": [
      0.1,
      0.2,
      0.15,
      0.0,
      0.05
    ],
    "velocity": [
      0.0,
      0.0,
      0.0,
      0.0,
      0.0
    ]
  },
  "control_output": [
    0.0,
    0.0,
    0.0,
    0.0,
    0.0
  ],
  "constraints_applied": true,
  "safety_status": "nominal",
  "log_id": "ctrl_20240615_103000"
}