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LiuzhengSJ
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kine_ctrl/rm75_mjc.py
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365
kine_ctrl/rm75_mjc.py
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#!/usr/bin/env python3
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"""
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RM75 Robot Controller with True Dynamics for URDF without Actuators
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Run this in your coppeliasim conda environment
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"""
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import mujoco
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import mujoco.viewer
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import numpy as np
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import time
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from pathlib import Path
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from scipy.signal import butter, lfilter
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class RM75Controller:
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def __init__(self, urdf_path: str, enable_viewer: bool = True,
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control_mode='torque', # 'torque' or 'position'
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low_pass_cutoff_hz=10.0):
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"""
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Initialize RM75 robot simulation with dynamic control
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Args:
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urdf_path: Path to RM75-B.urdf file
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enable_viewer: Show visualization window
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control_mode: 'torque' (realistic dynamics) or 'position' (kinematic)
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low_pass_cutoff_hz: Cutoff frequency for command filtering (Hz)
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"""
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# Load model
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self.model = mujoco.MjModel.from_xml_path(urdf_path)
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self.data = mujoco.MjData(self.model)
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# Robot info
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self.n_joints = self.model.njnt
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self.n_actuators = self.model.nu
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print(f"✓ Loaded RM75 robot")
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print(f" - Joints: {self.n_joints}")
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print(f" - Actuators in URDF: {self.n_actuators}")
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print(f" - Bodies: {self.model.nbody}")
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# Get joint names and limits
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self.joint_names = []
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self.joint_lower_limits = []
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self.joint_upper_limits = []
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for i in range(self.n_joints):
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self.joint_names.append(self.model.joint(i).name)
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# Get joint limits from URDF (range is [lower, upper])
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self.joint_lower_limits.append(self.model.jnt_range[i, 0])
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self.joint_upper_limits.append(self.model.jnt_range[i, 1])
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print(f" - Joint limits: {self.joint_lower_limits[0]:.2f} to {self.joint_upper_limits[0]:.2f} rad for joint_1")
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# If no actuators, we need to add them programmatically
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if self.n_actuators == 0:
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print(" No actuators in URDF. Adding torque actuators for dynamic control...")
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self._add_actuators_programmatically()
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# Control parameters
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self.control_mode = control_mode
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# PD gains for torque control (tuned for realistic motion)
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self.kp = np.array([200.0, 200.0, 150.0, 100.0, 80.0, 60.0, 50.0]) # Proportional gains
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self.kd = np.array([20.0, 20.0, 15.0, 10.0, 8.0, 6.0, 5.0]) # Derivative gains
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# State variables
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self.desired_positions = self.get_joint_positions().copy()
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# Low-pass filter for smooth commands
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self.lp_cutoff = low_pass_cutoff_hz
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self.filtered_commands = self.get_joint_positions().copy()
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# Home position
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self.home_position = self.get_joint_positions().copy()
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print(f" - Home position: {self.home_position}")
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# Setup viewer
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self.viewer = None
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if enable_viewer:
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try:
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self.viewer = mujoco.viewer.launch_passive(self.model, self.data)
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print("✓ Viewer launched successfully")
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except Exception as e:
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print(f"Warning: Could not launch viewer: {e}")
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self.viewer = None
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def _add_actuators_programmatically(self):
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"""Add torque actuators programmatically to enable dynamic control"""
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# Save current model state
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n_new_actuators = self.n_joints
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# We need to create a new model with actuators
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# For now, we'll use direct qpos control with custom dynamics
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# This is a workaround by setting control_mode to 'position' for kinematic control
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# and using manual velocity/acceleration limits
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print(f" Adding {n_new_actuators} virtual torque actuators")
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# Alternative approach: Use qfrc_applied to apply forces directly
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# This bypasses the need for actuators in the URDF
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self.use_qfrc_applied = True
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self.n_actuators = n_new_actuators
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# Create virtual control array
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self.virtual_ctrl = np.zeros(self.n_joints)
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def _low_pass_filter(self, new_target):
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"""Apply simple first-order low-pass filter to target positions"""
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dt = self.model.opt.timestep
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alpha = 2 * np.pi * self.lp_cutoff * dt
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alpha = min(alpha, 1.0) # Clamp for stability
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for i in range(self.n_joints):
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self.filtered_commands[i] = alpha * new_target[i] + (1 - alpha) * self.filtered_commands[i]
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return self.filtered_commands.copy()
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def get_joint_positions(self):
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"""Get current joint angles (radians)"""
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return self.data.qpos[:self.n_joints].copy()
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def get_joint_velocities(self):
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"""Get current joint velocities (rad/s)"""
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return self.data.qvel[:self.n_joints].copy()
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def set_desired_positions(self, target_positions, apply_filter=True):
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"""
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Set desired joint positions
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Args:
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target_positions: Target joint angles in radians
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apply_filter: Apply low-pass filter for smooth motion
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"""
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target = np.array(target_positions[:self.n_joints])
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# Apply joint limits
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for i in range(self.n_joints):
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target[i] = np.clip(target[i], self.joint_lower_limits[i], self.joint_upper_limits[i])
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# Apply low-pass filter if enabled
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if apply_filter:
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target = self._low_pass_filter(target)
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self.desired_positions = target
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def compute_torques(self):
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"""Compute torques using PD control with velocity damping"""
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current_pos = self.get_joint_positions()
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current_vel = self.get_joint_velocities()
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# Position error
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pos_error = self.desired_positions - current_pos
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# PD control law (negative feedback)
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torques = self.kp[:self.n_joints] * pos_error - self.kd[:self.n_joints] * current_vel
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# Apply torque limits (safety)
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max_torque = 50.0 # Nm limit
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torques = np.clip(torques, -max_torque, max_torque)
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return torques
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def step(self):
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"""Step the simulation with control applied"""
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if self.control_mode == 'torque':
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# Compute torques
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torques = self.compute_torques()
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# Apply torques directly to joints using qfrc_applied
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# This bypasses the need for actuators in the URDF
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self.data.qfrc_applied[:self.n_joints] = torques
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elif self.control_mode == 'position':
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# Direct position control (kinematic)
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# Add velocity damping for smoother motion
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current_pos = self.get_joint_positions()
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pos_error = self.desired_positions - current_pos
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# Simple proportional velocity control
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kp_vel = 50.0
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target_vel = kp_vel * pos_error
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# Limit velocity
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max_vel = 3.0 # rad/s
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target_vel = np.clip(target_vel, -max_vel, max_vel)
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# Apply velocity
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self.data.qvel[:self.n_joints] = target_vel
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# For position mode, we also apply small corrective torques
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kp_correct = 100.0
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kd_correct = 10.0
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correction = kp_correct * pos_error - kd_correct * self.get_joint_velocities()
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self.data.qfrc_applied[:self.n_joints] = correction
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# Step physics
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mujoco.mj_step(self.model, self.data)
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# If using direct qpos control (position mode extreme)
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if self.control_mode == 'position_direct':
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self.data.qpos[:self.n_joints] = self.desired_positions
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self.data.qvel[:self.n_joints] = 0
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mujoco.mj_step(self.model, self.data)
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# Sync viewer if active
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if self.viewer:
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self.viewer.sync()
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def step_n(self, n_steps: int):
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"""Advance simulation by N steps"""
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for _ in range(n_steps):
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self.step()
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def move_to_position(self, target_positions, duration=1.0, apply_filter=True):
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"""
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Smoothly move to target position with dynamics
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Args:
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target_positions: Target joint angles
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duration: Movement duration (seconds)
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apply_filter: Apply low-pass filtering
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"""
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# Calculate number of steps for smooth interpolation
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n_steps = int(duration / self.model.opt.timestep)
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start_positions = self.get_joint_positions()
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target = np.array(target_positions[:self.n_joints])
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print(f" Moving with dynamics: {n_steps} steps over {duration}s")
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for i in range(n_steps):
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# Linear interpolation for desired positions
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alpha = (i + 1) / n_steps
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desired = start_positions + alpha * (target - start_positions)
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# Set desired position (filter will apply smoothing)
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self.set_desired_positions(desired, apply_filter=apply_filter)
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# Step simulation
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self.step()
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# Optional: print progress
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if (i + 1) % 200 == 0:
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progress = (i + 1) / n_steps * 100
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print(f" Progress: {progress:.0f}%")
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def run_trajectory(self, trajectory_points, duration_per_point=1.0, apply_filter=True):
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"""
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Execute a trajectory with smooth dynamics
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Args:
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trajectory_points: List of joint position arrays
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duration_per_point: Time per trajectory point (seconds)
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apply_filter: Apply low-pass filtering
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"""
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print(f"\nExecuting trajectory with {len(trajectory_points)} points...")
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for i, target in enumerate(trajectory_points):
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print(f" Moving to point {i + 1}/{len(trajectory_points)}")
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self.move_to_position(target, duration_per_point, apply_filter)
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print("✓ Trajectory complete")
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def run_interactive(self):
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"""Run simulation with real-time control"""
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print("\n✓ Simulation running with dynamic control")
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print(f" Control mode: {self.control_mode}")
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print(f" Low-pass cutoff: {self.lp_cutoff} Hz")
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print(" Close viewer window to exit.\n")
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try:
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last_time = time.time()
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while self.viewer and self.viewer.is_running():
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self.step()
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# Maintain real-time speed
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elapsed = time.time() - last_time
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sleep_time = self.model.opt.timestep - elapsed
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if sleep_time > 0:
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time.sleep(sleep_time)
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last_time = time.time()
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except KeyboardInterrupt:
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print("\n✓ Stopped by user")
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finally:
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if self.viewer:
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self.viewer.close()
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def print_state(self):
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"""Print current robot state"""
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positions = self.get_joint_positions()
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velocities = self.get_joint_velocities()
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print(f"Positions (rad): {[f'{p:.3f}' for p in positions[:4]]}...")
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print(f"Velocities (rad/s): {[f'{v:.3f}' for v in velocities[:4]]}...")
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# Demo with different dynamic behaviors
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if __name__ == "__main__":
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# Path to your URDF
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urdf_file = "/home/zl/Downloads/urdf_rm75/RM75-B.urdf"
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if not Path(urdf_file).exists():
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print(f"Error: URDF file not found at {urdf_file}")
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exit(1)
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print("=" * 60)
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print("RM75 Robot with Realistic Dynamics")
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print("=" * 60)
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# Test 1: Torque control with low-pass filter (most realistic)
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print("\n>>> Test 1: Torque Control with Low-Pass Filter (5 Hz)")
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robot1 = RM75Controller(urdf_file, enable_viewer=True,
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control_mode='torque',
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low_pass_cutoff_hz=5.0) # Smooth, natural response
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# Wait a moment for viewer to initialize
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time.sleep(1)
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# Move to a pose with dynamics
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target_pose = robot1.home_position.copy()
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target_pose[0] = 0.8 # Joint 1 (base rotation)
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target_pose[1] = -0.5 # Joint 2 (shoulder)
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target_pose[2] = 0.4 # Joint 3 (elbow)
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target_pose[3] = 0.3 # Joint 4 (wrist 1)
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print("\nMoving to target pose with realistic dynamics...")
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robot1.move_to_position(target_pose, duration=2.0, apply_filter=True)
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print("\nRobot state after movement:")
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robot1.print_state()
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# Test 2: Return to home with different filter
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print("\n>>> Returning to home with softer filter (2 Hz)")
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robot1.lp_cutoff = 2.0 # Change to slower response
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robot1.move_to_position(robot1.home_position, duration=2.0, apply_filter=True)
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# Test 3: Demonstrate different control modes
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print("\n>>> Testing position control mode (faster response)")
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robot2 = RM75Controller(urdf_file, enable_viewer=False,
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control_mode='position',
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low_pass_cutoff_hz=15.0)
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test_pose = robot2.home_position.copy()
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test_pose[0] = 0.5
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test_pose[1] = -0.3
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print("Moving in position control mode...")
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robot2.move_to_position(test_pose, duration=1.0, apply_filter=True)
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robot2.print_state()
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print("\n" + "=" * 60)
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print("Dynamic Behavior Summary")
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print("=" * 60)
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print("✓ Torque Control: Realistic physics with inertia and damping")
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print("✓ Low-pass filter: Adjustable smoothing (lower = smoother)")
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print("✓ Joint limits: Automatically enforced from URDF")
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print("✓ Programmatic actuation: No URDF modification needed")
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print("\nTips for tuning:")
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print(" - Lower cutoff (2-5 Hz): Smooth, natural motion")
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print(" - Higher cutoff (10-20 Hz): More responsive")
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print(" - Adjust kp/kd gains for stiffness/damping")
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# Run interactive simulation
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print("\nStarting interactive simulation...")
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print("Press Ctrl+C in terminal to exit, or close the viewer window.")
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robot1.run_interactive()
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