#!/usr/bin/env python

import numpy as np
from WheeledRobot import OmniRobotEnv

# OmniRobot kinematics
a1 = 0.0;         b1 = 0.0; l1 = 0.5
a2 = (2/3)*np.pi; b2 = 0.0; l2 = 0.5
a3 = (4/3)*np.pi; b3 = 0.0; l3 = 0.5
r = 0.1

# Kinematics matrix
J = np.array([
    [1/r*np.sin(a1+b1), -1/r*np.cos(a1+b1), -l1/r*np.cos(b1)],
    [1/r*np.sin(a2+b2), -1/r*np.cos(a2+b2), -l2/r*np.cos(b2)],
    [1/r*np.sin(a3+b3), -1/r*np.cos(a3+b3), -l3/r*np.cos(b3)]
])
F = np.linalg.inv(J)

def controller_omni_pid(t, X_I, dX_I, target_position):
    """PID Controller with dynamic target position"""
    global int_err

    # PID parameters
    Kp_t = 2; Kp_r = 2
    Kd_t = 0.5; Kd_r = 0.5
    Ki_t = 0.1; Ki_r = 0.1

    # Use target position from parameters instead of hardcoded values
    X_I_des = target_position.reshape(-1, 1)
    pos_err = X_I_des - X_I
    vel_err = np.zeros((3, 1)) - dX_I
    int_err = int_err + pos_err

    # PID control output
    dX_I_des = np.array([ 
        [Kp_t*pos_err[0,0] + Kd_t*vel_err[0,0] + Ki_t*int_err[0,0]], 
        [Kp_t*pos_err[1,0] + Kd_t*vel_err[1,0] + Ki_t*int_err[1,0]], 
        [Kp_r*pos_err[2,0] + Kd_r*vel_err[2,0] + Ki_r*int_err[2,0]] 
    ])

    # Coordinate transform
    R_RI = np.array([[ np.cos(X_I[2,0]), np.sin(X_I[2,0]), 0.0],
                     [-np.sin(X_I[2,0]), np.cos(X_I[2,0]), 0.0],
                     [             0.0,               0.0, 1.0]])
    dX_R_des = R_RI @ dX_I_des
    U = J @ dX_R_des
    return U

def run_simulation():
    """Run simulation using Gymnasium environment with PID control to dynamic target"""
    global int_err
    
    # Initialize environment with fixed target for reproducible results
    # You can set random_target=True for random target generation
    env = OmniRobotEnv(render_mode="human", random_target=False, speed_limit_enabled=True)
    observation, _ = env.reset()
    
    # Expand render bounds to show target position (default target is at [10,5])
    env.set_render_bounds((-2, 12), (-2, 8))
    
    # Initialize integral error
    int_err = np.zeros((3, 1))
    
    print("Starting Omnidirectional Robot PID Control Simulation")
    print("Controller: PID control to dynamic target position")
    print(f"Target position: [{observation[7]:.2f}, {observation[8]:.2f}, {observation[9]:.2f}]")
    
    for step in range(1000):
        # Extract controller inputs from observation
        # New observation format: [x, y, theta, dx, dy, dtheta, time, target_x, target_y, target_theta]
        time = observation[6]                    # Current time
        X_I = observation[:3].reshape(-1, 1)     # State [x, y, theta]
        dX_I = observation[3:6].reshape(-1, 1)   # Derivatives [dx, dy, dtheta]
        target_position = observation[7:10]      # Target [target_x, target_y, target_theta]
        
        # Call PID controller with dynamic target
        U = controller_omni_pid(time, X_I, dX_I, target_position)
        
        # Step environment
        observation, reward, terminated, truncated, _ = env.step(U.flatten())
        
        # Render the environment
        env.render()
        
        # Check if target reached
        if terminated:
            print(f"Target reached at step {step}! Reward: {reward:.2f}")
            break
        elif truncated:
            print(f"Maximum steps reached at step {step}")
            break
    
    input("Press Enter to close the simulation window...")
    env.close()
    print("Simulation completed")

if __name__ == "__main__":
    run_simulation()