import sys import numpy as np from scipy.spatial.transform import Rotation as R from .arm_state import ArmState from .base_controller import BaseController from pathlib import Path sys.path.append(str(Path(__file__).resolve().parent.parent)) from tools.yaml_operator import read_yaml import time class HybridAdmitController(BaseController): def __init__(self, name, state: ArmState,config_path) -> None: super().__init__(name, state) self.load_config(config_path) self.laset_print_time = 0 def load_config(self, config_path): config_dict = read_yaml(config_path) if self.name != config_dict['name']: raise ValueError(f"Controller name {self.name} does not match config name {config_dict['name']}") # 姿态调节器 # 位控 2x2矩阵 self.Kp_R = np.diag(np.array(config_dict['Kp_R'])) self.Ki_R = np.diag(np.array(config_dict['Ki_R'])) self.Kd_R = np.diag(np.array(config_dict['Kd_R'])) mass_rot = np.array(config_dict['mass_z']) stiff_rot = np.array(config_dict['stiff_z']) desired_xi = np.array(config_dict['desired_xi']) damp_rot = np.array(config_dict['damp_z']) self.D_z_min = 10.0 self.D_z_max = 100.0 self.D_z_lambda = 5.0 self.M_z = np.diag(mass_rot) self.K_z = np.diag(stiff_rot) self.D_z = np.diag(damp_rot) # 位控 2x2矩阵 self.Kp = np.diag(np.array(config_dict['Kp'])) self.Ki = np.diag(np.array(config_dict['Ki'])) self.Kd = np.diag(np.array(config_dict['Kd'])) self.pose_integral_error = np.zeros(6) def step(self,dt): # 方向统一 if self.state.desired_orientation.dot(self.state.arm_orientation) < 0: self.state.arm_orientation = -self.state.arm_orientation # 缓存常用计算 arm_ori_quat = R.from_quat(self.state.arm_orientation) arm_ori_mat = arm_ori_quat.as_matrix() # 位置误差 temp_pose_error = self.state.arm_position - self.state.desired_position self.state.pose_error[:3] = arm_ori_mat.T @ temp_pose_error # 姿态误差(四元数) rot_err_quat = arm_ori_quat.inv() * R.from_quat(self.state.desired_orientation) self.state.pose_error[3:] = -rot_err_quat.as_rotvec(degrees=False) # 期望加速度 wrench_err = self.state.external_wrench_tcp - self.state.desired_wrench # z方向力导纳控制 self.state.arm_desired_acc[2] = 1/self.M_z * (wrench_err[2] - self.D_z * (self.state.arm_desired_twist[2] - self.state.desired_twist[2]) - self.K_z * self.state.pose_error[2]) # print("self.state.arm_desired_acc[2]:",self.state.arm_desired_acc[2],self.M_z,wrench_err[2],self.K_z,self.D_z) self.pose_integral_error += self.state.pose_error * dt # 位控制 self.state.arm_desired_acc[:2] = -self.Kd @ self.state.arm_desired_twist[:2] - self.Kp @ self.state.pose_error[:2] - self.Ki @ self.pose_integral_error[:2] # 姿态pid self.state.arm_desired_acc[3:] = -self.Kd_R @ self.state.arm_desired_twist[3:] - self.Kp_R @ self.state.pose_error[3:] - self.Ki_R @ self.pose_integral_error[3:] self.clip_command(self.state.arm_desired_acc, "acc") ## 更新速度和位姿 self.state.arm_desired_twist += self.state.arm_desired_acc * dt self.clip_command(self.state.arm_desired_twist, "vel") # 计算位姿变化 delta_pose = np.concatenate([ arm_ori_mat @ (self.state.arm_desired_twist[:3] * dt), self.state.arm_desired_twist[3:] * dt ]) self.clip_command(delta_pose, "pose") # 更新四元数 delta_ori_quat = R.from_rotvec(delta_pose[3:]).as_quat() arm_ori_quat_new = arm_ori_quat * R.from_quat(delta_ori_quat) self.state.arm_orientation_command = arm_ori_quat_new.as_quat() # 归一化四元数 self.state.arm_orientation_command /= np.linalg.norm(self.state.arm_orientation_command) # 更新位置 self.state.arm_position_command = self.state.arm_position + delta_pose[:3] def step_traj(self,dt): # 方向统一 if self.state.desired_orientation.dot(self.state.arm_orientation) < 0: self.state.arm_orientation = -self.state.arm_orientation # 缓存常用计算 arm_ori_quat = R.from_quat(self.state.arm_orientation) arm_ori_mat = arm_ori_quat.as_matrix() # 位置误差 temp_pose_error = self.state.arm_position - self.state.desired_position + self.state.desired_twist[:3] * dt self.state.pose_error[:3] = arm_ori_mat.T @ temp_pose_error # 姿态误差(四元数) angular_velocity = np.array(self.state.desired_twist[3:]) # 形状 (3,) # 用旋转向量（小角度近似） rotvec = angular_velocity * dt # 旋转向量 = 角速度 × 时间 rot_quat = R.from_rotvec(rotvec).as_quat() # 转成四元数，形状 (4,) rot_err_quat = R.from_quat(rot_quat).inv() * arm_ori_quat.inv() * R.from_quat(self.state.desired_orientation) self.state.pose_error[3:] = -rot_err_quat.as_rotvec(degrees=False) # 期望加速度 wrench_err = self.state.external_wrench_tcp - self.state.desired_wrench # z方向力导纳控制 self.state.arm_desired_acc[2] = 1/self.M_z * (wrench_err[2] - self.D_z * (self.state.arm_desired_twist[2] - self.state.desired_twist[2] + self.state.desired_acc[2]*dt) - self.K_z * self.state.pose_error[2]) + self.state.desired_acc[2] self.pose_integral_error += self.state.pose_error * dt # 位控制 self.state.arm_desired_acc[:2] = -self.Kd @ self.state.arm_desired_twist[:2] - self.Kp @ self.state.pose_error[:2] - self.Ki @ self.pose_integral_error[:2] # 姿态pid self.state.arm_desired_acc[3:] = -self.Kd_R @ self.state.arm_desired_twist[3:] - self.Kp_R @ self.state.pose_error[3:] - self.Ki_R @ self.pose_integral_error[3:] self.clip_command(self.state.arm_desired_acc, "acc") ## 更新速度和位姿 self.state.arm_desired_twist += self.state.arm_desired_acc * dt self.clip_command(self.state.arm_desired_twist, "vel") # 计算位姿变化 delta_pose = np.concatenate([ arm_ori_mat @ (self.state.arm_desired_twist[:3] * dt), self.state.arm_desired_twist[3:] * dt ]) self.clip_command(delta_pose, "pose") # 更新四元数 delta_ori_quat = R.from_rotvec(delta_pose[3:]).as_quat() arm_ori_quat_new = arm_ori_quat * R.from_quat(delta_ori_quat) self.state.arm_orientation_command = arm_ori_quat_new.as_quat() # 归一化四元数 self.state.arm_orientation_command /= np.linalg.norm(self.state.arm_orientation_command) # 更新位置 self.state.arm_position_command = self.state.arm_position + delta_pose[:3]