136 lines
6.4 KiB
Python
136 lines
6.4 KiB
Python
import sys
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import numpy as np
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from scipy.spatial.transform import Rotation as R
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from .base_controller import BaseController
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from .arm_state import ArmState
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from pathlib import Path
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sys.path.append(str(Path(__file__).resolve().parent.parent))
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import time
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from tools.yaml_operator import read_yaml
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import random
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class AdmittanceController(BaseController):
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def __init__(self, name, state:ArmState,config_path) -> None:
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super().__init__(name, state)
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self.load_config(config_path)
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def load_config(self, config_path):
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config_dict = read_yaml(config_path)
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if self.name != config_dict['name']:
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raise ValueError(f"Controller name {self.name} does not match config name {config_dict['name']}")
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mass_tran = np.array(config_dict['mass_tran'])
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mass_rot = np.array(config_dict['mass_rot'])
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stiff_tran = np.array(config_dict['stiff_tran'])
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stiff_rot = np.array(config_dict['stiff_rot'])
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desired_xi = np.array(config_dict['desired_xi'])
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damp_tran = np.array(config_dict['damp_tran'])
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damp_rot = np.array(config_dict['damp_rot'])
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self.pos_scale_factor = config_dict['pos_scale_factor']
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self.rot_scale_factor = config_dict['rot_scale_factor']
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for i in range(3):
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if damp_tran[i] < 0:
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damp_tran[i] = 2 * desired_xi * np.sqrt(stiff_tran[i] * mass_tran[i])
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if damp_rot[i] < 0:
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damp_rot[i] = 2 * desired_xi * np.sqrt(stiff_rot[i] * mass_rot[i])
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self.M = np.diag(np.concatenate([mass_tran, mass_rot]))
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self.M_inv = np.linalg.inv(self.M)
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self.K = np.diag(np.concatenate([stiff_tran, stiff_rot]))
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self.D = np.diag(np.concatenate([damp_tran, damp_rot]))
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self.laset_print_time = 0
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def step(self,dt):
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# 方向统一
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if self.state.desired_orientation.dot(self.state.arm_orientation) < 0:
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self.state.arm_orientation = -self.state.arm_orientation
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# 缓存常用计算
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arm_ori_quat = R.from_quat(self.state.arm_orientation)
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arm_ori_mat = arm_ori_quat.as_matrix()
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# 位置误差
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temp_pose_error = self.state.arm_position - self.state.desired_position
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self.state.pose_error[:3] = arm_ori_mat.T @ temp_pose_error
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# 姿态误差(四元数)
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rot_err_quat = arm_ori_quat.inv() * R.from_quat(self.state.desired_orientation)
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self.state.pose_error[3:] = -rot_err_quat.as_rotvec(degrees=False)
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# 期望加速度
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wrench_err = self.state.external_wrench_tcp - self.state.desired_wrench
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D_vel = self.D @ (self.state.arm_desired_twist - self.state.desired_twist)
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K_pose = self.K @ self.state.pose_error
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self.state.arm_desired_acc = self.M_inv @ (wrench_err - D_vel - K_pose)
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self.clip_command(self.state.arm_desired_acc, "acc")
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## 更新速度和位姿
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self.state.arm_desired_twist += self.state.arm_desired_acc * dt
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self.clip_command(self.state.arm_desired_twist, "vel")
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# 计算位姿变化
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delta_pose = np.concatenate([
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arm_ori_mat @ (self.state.arm_desired_twist[:3] * dt),
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self.state.arm_desired_twist[3:] * dt
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])
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self.clip_command(delta_pose, "pose")
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# 更新四元数
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delta_ori_quat = R.from_rotvec(delta_pose[3:]).as_quat()
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arm_ori_quat_new = arm_ori_quat * R.from_quat(delta_ori_quat)
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self.state.arm_orientation_command = arm_ori_quat_new.as_quat()
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# 归一化四元数
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self.state.arm_orientation_command /= np.linalg.norm(self.state.arm_orientation_command)
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# 更新位置
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self.state.arm_position_command = self.state.arm_position + delta_pose[:3]
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def step_traj(self,dt):
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# 方向统一
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if self.state.desired_orientation.dot(self.state.arm_orientation) < 0:
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self.state.arm_orientation = -self.state.arm_orientation
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# 缓存常用计算
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arm_ori_quat = R.from_quat(self.state.arm_orientation)
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arm_ori_mat = arm_ori_quat.as_matrix()
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# 位置误差
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temp_pose_error = self.state.arm_position - self.state.desired_position + self.state.desired_twist[:3] * dt
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self.state.pose_error[:3] = arm_ori_mat.T @ temp_pose_error
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# 姿态误差(四元数)
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angular_velocity = np.array(self.state.desired_twist[3:]) # 形状 (3,)
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# 用旋转向量（小角度近似）
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rotvec = angular_velocity * dt # 旋转向量 = 角速度 × 时间
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rot_quat = R.from_rotvec(rotvec).as_quat() # 转成四元数，形状 (4,)
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rot_err_quat = R.from_quat(rot_quat).inv() * arm_ori_quat.inv() * R.from_quat(self.state.desired_orientation)
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self.state.pose_error[3:] = -rot_err_quat.as_rotvec(degrees=False)
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# 期望加速度
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wrench_err = self.state.external_wrench_tcp - self.state.desired_wrench
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D_vel = self.D @ (self.state.arm_desired_twist - self.state.desired_twist + self.state.desired_acc*dt)
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K_pose = self.K @ self.state.pose_error
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self.state.arm_desired_acc = self.M_inv @ (wrench_err - D_vel - K_pose) + self.state.desired_acc
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self.clip_command(self.state.arm_desired_acc, "acc")
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## 更新速度和位姿
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self.state.arm_desired_twist += self.state.arm_desired_acc * dt
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self.clip_command(self.state.arm_desired_twist, "vel")
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# 计算位姿变化
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delta_pose = np.concatenate([
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arm_ori_mat @ (self.state.arm_desired_twist[:3] * dt),
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self.state.arm_desired_twist[3:] * dt
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])
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self.clip_command(delta_pose, "pose")
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# 更新四元数
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delta_ori_quat = R.from_rotvec(delta_pose[3:]).as_quat()
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arm_ori_quat_new = arm_ori_quat * R.from_quat(delta_ori_quat)
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self.state.arm_orientation_command = arm_ori_quat_new.as_quat()
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# 归一化四元数
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self.state.arm_orientation_command /= np.linalg.norm(self.state.arm_orientation_command)
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# 更新位置
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self.state.arm_position_command = self.state.arm_position + delta_pose[:3]
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if __name__ == "__main__":
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state = ArmState()
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controller = AdmittanceController("admittance",state,"/home/zyc/admittance_control/MassageControl/config/admittance.yaml")
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print(controller.name)
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print(controller.state.arm_position)
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state.arm_position = np.array([1,2,3])
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print(controller.state.arm_position)
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print(controller.M)
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print(controller.D)
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print(controller.K) |