mirror of
https://github.com/m1ngsama/robot_arm.git
synced 2026-03-25 19:53:49 +00:00
196 lines
No EOL
7.8 KiB
Python
196 lines
No EOL
7.8 KiB
Python
import numpy as np
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from scipy.optimize import minimize
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import serial
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import time
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import math
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import matplotlib.pyplot as plt
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class RobotArmUltimate:
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def __init__(self, port='COM3', baud=115200):
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# --- 1. 物理参数 ---
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self.L1, self.L2, self.L3, self.L4 = 70.0, 75.0, 50.0, 130.0
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self.current_servos_logic = np.array([90.0, 90.0, 90.0, 90.0])
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self.last_sent_servos = np.array([0.0, 0.0, 0.0, 0.0]) # 记录上次发送的值
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self.path_history = []
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# --- 【新增】倾斜修正参数 ---
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# 如果水平移动时往下掉，增加 OFFSET_Y (例如 2.0)
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self.OFFSET_Y = 0.0
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self.OFFSET_Z = 0.0
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# --- 2. 减震参数 ---
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self.servo_buffer = [] # 指令缓冲用于平滑滤波
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self.buffer_size = 3 # 移动平均窗口大小
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self.max_servo_speed = 30.0 # 舵机最大速度
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self.damping_factor = 0.7 # 阻尼因子
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try:
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self.ser = serial.Serial(port, baud, timeout=1)
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time.sleep(4)
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self.ser.flushInput()
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print(">>> [系统就绪] 已加载 S-Curve 平滑减震算法 + 倾斜修正。")
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self.gripper_control(70)
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except:
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print(">>> [警告] 串口未连接，进入仿真。")
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self.ser = None
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def dh_matrix(self, theta_deg, d, a, alpha_deg):
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theta, alpha = np.radians(theta_deg), np.radians(alpha_deg)
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return np.array([
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[np.cos(theta), -np.sin(theta)*np.cos(alpha), np.sin(theta)*np.sin(alpha), a*np.cos(theta)],
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[np.sin(theta), np.cos(theta)*np.cos(alpha), -np.cos(theta)*np.sin(alpha), a*np.sin(theta)],
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[0, np.sin(alpha), np.cos(alpha), d],
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[0, 0, 0, 1]
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])
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def forward_kinematics(self, s1, s2, s3, s4):
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t1, t2, t3, t4 = s1-90, 180-s2, 90-s3, s4-90
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T01 = self.dh_matrix(t1, self.L1, 0, 90)
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T12 = T01 @ self.dh_matrix(t2, 0, self.L2, 0)
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T23 = T12 @ self.dh_matrix(t3, 0, self.L3, 0)
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T34 = T23 @ self.dh_matrix(t4, 0, self.L4, 0)
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return T34[0:3, 3], t2 + t3 + t4, [np.array([0,0,0]), T01[0:3,3], T12[0:3,3], T23[0:3,3], T34[0:3,3]]
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def inverse_kinematics(self, target_xyz, target_pitch=-90, init_angles=None):
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if init_angles is None: init_angles = self.current_servos_logic
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def objective(s):
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pos, pitch, _ = self.forward_kinematics(*s)
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dist_err = np.linalg.norm(pos - target_xyz)
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pitch_err = abs(pitch - target_pitch)
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return dist_err * 2.0 + pitch_err * 0.1
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bounds = [(0, 180)] * 4
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res = minimize(objective, init_angles, bounds=bounds, method='SLSQP', tol=1e-3)
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return res.x
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def _send_and_audit(self, s, target_xyz):
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"""带有多层减震的发送函数"""
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# --- 【修改点】应用倾斜修正 ---
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# 在进入滤波前，先加上固定的偏移量
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s_corrected = s.copy()
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s_corrected[1] += self.OFFSET_Y
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s_corrected[2] += self.OFFSET_Z
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s_logic = np.array([np.clip(x, 0, 180) for x in s_corrected])
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# --- 层1: 移动平均滤波器 ---
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self.servo_buffer.append(s_logic)
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if len(self.servo_buffer) > self.buffer_size:
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self.servo_buffer.pop(0)
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s_smoothed = np.mean(self.servo_buffer, axis=0)
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# --- 层2: 速度限制 ---
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delta = s_smoothed - self.current_servos_logic
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delta_norm = np.linalg.norm(delta)
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if delta_norm > self.max_servo_speed:
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s_smoothed = self.current_servos_logic + delta * (self.max_servo_speed / delta_norm)
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# --- 层3: 阻尼因子 ---
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s_damped = self.current_servos_logic * (1 - self.damping_factor) + s_smoothed * self.damping_factor
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s_logic_final = np.array([int(round(np.clip(x, 0, 180))) for x in s_damped])
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s_hardware = np.array([s_logic_final[0], s_logic_final[1], 180 - s_logic_final[2], 180 - s_logic_final[3]])
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# --- 层4: 死区过滤 ---
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if np.sum(np.abs(s_hardware - self.last_sent_servos)) < 1.0:
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return
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if self.ser:
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cmd = f"Servo_ArmX{s_hardware[0]}\nServo_ArmY{s_hardware[1]}\n" \
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f"Servo_ArmZ{s_hardware[2]}\nServo_ArmB{s_hardware[3]}\n"
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self.ser.write(cmd.encode())
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self.last_sent_servos = s_hardware.astype(int)
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self.current_servos_logic = s_logic_final # 这里的逻辑值已经是修正后的，保证迭代平滑
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_, _, pts = self.forward_kinematics(*s_logic_final)
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self.path_history.append(pts)
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def move_line(self, start_xyz, end_xyz, p_start=-90, p_end=-90, duration=3.0):
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"""核心改进：多层S-Curve轨迹规划 + 动态自适应FPS"""
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distance = np.linalg.norm(end_xyz - start_xyz)
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if distance < 50:
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fps = 20
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elif distance < 150:
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fps = 40
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else:
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fps = 60
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steps = max(int(duration * fps), 10)
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print(f"规划平滑路径: {start_xyz} -> {end_xyz}, 距离={distance:.1f}mm, FPS={fps}")
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for i in range(steps + 1):
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t = i / steps
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smooth_t = 0.5 * (1 - math.cos(math.pi * t))
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curr_xyz = start_xyz + (end_xyz - start_xyz) * smooth_t
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curr_pitch = p_start + (p_end - p_start) * smooth_t
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best_s = self.inverse_kinematics(curr_xyz, target_pitch=curr_pitch)
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self._send_and_audit(best_s, curr_xyz)
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time.sleep(1.0 / fps)
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def gripper_control(self, angle):
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if self.ser:
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self.ser.write(f"Servo_Gripper{int(angle)}\n".encode())
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time.sleep(0.8)
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def set_damping_params(self, buffer_size=3, max_speed=30.0, damping_factor=0.7):
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self.buffer_size = buffer_size
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self.max_servo_speed = max_speed
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self.damping_factor = damping_factor
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print(f"✓ 减震参数已更新: 滤波窗口={buffer_size}, 限速={max_speed}度/帧, 阻尼={damping_factor}")
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# --- 【新增】设置修正系数的接口 ---
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def set_correction(self, offset_y=0.0, offset_z=0.0):
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self.OFFSET_Y = offset_y
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self.OFFSET_Z = offset_z
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print(f"✓ 倾斜修正已更新: Y_OFFSET={offset_y}, Z_OFFSET={offset_z}")
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def close(self):
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if self.ser: self.ser.close()
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if __name__ == "__main__":
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arm = RobotArmUltimate(port='COM3')
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# 1. 设置减震参数
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arm.set_damping_params(buffer_size=3, max_speed=30.0, damping_factor=0.7)
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# 2. 【核心调试】在这里设置修正值
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# 如果正向移动时 Z 往下掉，说明大臂（Y）低了，给它加一点
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arm.set_correction(offset_y=-10.0, offset_z=0.0)
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p_standby = np.array([110, 100, 20])
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p_pick1 = np.array([210, 110, 20])
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p_pick2 = np.array([210, -110, 20])
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p_drop = np.array([110, -100, 20])
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try:
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print("\n=== 开始平滑动作序列 (带倾斜修正) ===")
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# 1. 初始定位
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# s_init = arm.inverse_kinematics(p_standby, target_pitch=-90)
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# arm._send_and_audit(s_init, p_standby)
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# time.sleep(2)
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# 2. 减震下降
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arm.move_line(p_standby, p_pick1, p_start=-60, p_end=-90, duration=1.0)
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arm.gripper_control(120)
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# 3. 减震平移
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arm.move_line(p_pick1, p_pick2, p_start=-90, p_end=-30, duration=1.0)
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time.sleep(1)
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arm.move_line(p_pick2, p_drop, p_start=-30, p_end=-90, duration=1.0)
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arm.gripper_control(70)
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time.sleep(1)
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# 4. 减震返回待命
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arm.move_line(p_drop, p_standby, p_start=-90, p_end=-60, duration=1.0)
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time.sleep(1)
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print("\n>>> 任务结束。")
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except KeyboardInterrupt:
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pass
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finally:
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if arm.ser: arm.ser.close() |