yunxiao.zhu
f7e2bb0e7b
* 为 RobotConfig 增加 trigger_sample_index_offset_cycles 配置 * 让 DO 事件携带示教点关节角并按最接近 sample 绑定触发 * 调整运行时 IO 地址位掩码映射并补充 ShotEvents 导出 * 新增 2026042802-1 抓包分析脚本、数据产物与结论文档 * 补齐配置兼容、规划绑定和运行时触发相关测试
364 lines
16 KiB
Python
364 lines
16 KiB
Python
#!/usr/bin/env python3
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"""提取 2026042802-1 抓包中的 60015 状态反馈，并和 UTTC_MS11 示教点对比。"""
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from __future__ import annotations
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import csv
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import json
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import math
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import struct
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import subprocess
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from collections import Counter
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from pathlib import Path
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REPO_ROOT = Path(__file__).resolve().parents[1]
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DEFAULT_PCAP = REPO_ROOT.parent / "Rvbust" / "uttc-20260428" / "2026042802-1.pcap"
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DEFAULT_TSHARK = Path(r"D:\Zyx\Downloads\WiresharkPortable32\App\Wireshark\tshark.exe")
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OUTPUT_DIR = REPO_ROOT / "analysis" / "2026042802-1"
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CONFIG_PATH = REPO_ROOT / "Config" / "RobotConfig.json"
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SEARCH_WINDOW_CYCLES = 20
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def be_u32(data: bytes, offset: int) -> int:
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"""按大端读取 4 字节无符号整数。"""
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return struct.unpack(">I", data[offset : offset + 4])[0]
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def be_u16(data: bytes, offset: int) -> int:
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"""按大端读取 2 字节无符号整数。"""
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return struct.unpack(">H", data[offset : offset + 2])[0]
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def be_f32(data: bytes, offset: int) -> float:
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"""按大端读取 4 字节浮点数。"""
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return struct.unpack(">f", data[offset : offset + 4])[0]
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def load_udp_rows(pcap: Path, tshark: Path) -> list[list[str]]:
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"""提取 UDP 60015 原始字段，后续按方向和长度拆分命令与状态。"""
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command = [
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str(tshark),
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"-r",
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str(pcap),
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"-Y",
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"udp.port==60015",
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"-T",
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"fields",
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"-e",
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"frame.number",
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"-e",
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"frame.time_relative",
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"-e",
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"ip.src",
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"-e",
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"ip.dst",
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"-e",
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"udp.payload",
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]
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output = subprocess.check_output(command, text=True, encoding="utf-8", errors="ignore")
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rows: list[list[str]] = []
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for line in output.splitlines():
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if not line.strip():
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continue
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parts = line.split("\t")
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if len(parts) >= 5:
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rows.append(parts[:5])
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return rows
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def decode_command_records(rows: list[list[str]], client_ip: str, robot_ip: str) -> list[dict]:
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"""把 64B J519 命令帧解码成结构化记录。"""
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records: list[dict] = []
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for frame_no, time_rel, ip_src, ip_dst, payload_hex in rows:
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if ip_src != client_ip or ip_dst != robot_ip:
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continue
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payload = bytes.fromhex(payload_hex)
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if len(payload) != 64:
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continue
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records.append(
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{
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"frame_number": int(frame_no),
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"time_relative_s": float(time_rel),
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"sequence": be_u32(payload, 0x08),
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"write_io_value": be_u16(payload, 0x18),
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"j1_deg": be_f32(payload, 0x1C),
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"j2_deg": be_f32(payload, 0x20),
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"j3_deg": be_f32(payload, 0x24),
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"j4_deg": be_f32(payload, 0x28),
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"j5_deg": be_f32(payload, 0x2C),
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"j6_deg": be_f32(payload, 0x30),
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}
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)
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return records
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def decode_status_records(rows: list[list[str]], client_ip: str, robot_ip: str) -> list[dict]:
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"""把 132B J519 状态帧按运行时代码同口径解码。"""
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records: list[dict] = []
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for frame_no, time_rel, ip_src, ip_dst, payload_hex in rows:
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if ip_src != robot_ip or ip_dst != client_ip:
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continue
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payload = bytes.fromhex(payload_hex)
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if len(payload) != 132:
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continue
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status = payload[0x0C]
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joints = [be_f32(payload, 0x3C + index * 4) for index in range(6)]
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pose = [be_f32(payload, 0x18 + index * 4) for index in range(6)]
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records.append(
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{
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"frame_number": int(frame_no),
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"time_relative_s": float(time_rel),
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"sequence": be_u32(payload, 0x08),
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"status": status,
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"accepts_command": bool(status & 0b0001),
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"received_command": bool(status & 0b0010),
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"system_ready": bool(status & 0b0100),
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"robot_in_motion": bool(status & 0b1000),
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"read_io_value": be_u16(payload, 0x12),
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"timestamp": be_u32(payload, 0x14),
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"pose_x_mm": pose[0],
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"pose_y_mm": pose[1],
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"pose_z_mm": pose[2],
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"pose_w_deg": pose[3],
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"pose_p_deg": pose[4],
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"pose_r_deg": pose[5],
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"j1_deg": joints[0],
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"j2_deg": joints[1],
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"j3_deg": joints[2],
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"j4_deg": joints[3],
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"j5_deg": joints[4],
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"j6_deg": joints[5],
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}
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)
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return records
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def pick_trigger_first_high_frames(records: list[dict]) -> list[dict]:
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"""由于 io_keep_cycles=2，只保留每组高电平脉冲的第一帧。"""
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trigger_frames: list[dict] = []
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previous_high = False
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for record in records:
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current_high = record["write_io_value"] > 0
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if current_high and not previous_high:
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trigger_frames.append(record)
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previous_high = current_high
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return trigger_frames
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def load_uttc_ms11_config() -> dict:
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"""读取 UTTC_MS11 的示教点和触发配置。"""
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config = json.loads(CONFIG_PATH.read_text(encoding="utf-8"))
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return config["flying_shots"]["UTTC_MS11"]
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def build_diff_row(prefix: str, actual_deg: list[float], teach_deg: list[float], row: dict) -> tuple[float, float, str]:
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"""向结果行写入逐轴误差，并返回聚合误差。"""
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diffs = [actual_deg[index] - teach_deg[index] for index in range(6)]
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abs_diffs = [abs(value) for value in diffs]
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max_error = max(abs_diffs)
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max_error_axis = f"J{abs_diffs.index(max_error) + 1}"
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rms_error = math.sqrt(sum(value * value for value in diffs) / 6.0)
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for joint_index in range(6):
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joint_no = joint_index + 1
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row[f"{prefix}_j{joint_no}_actual_deg"] = actual_deg[joint_index]
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row[f"{prefix}_diff_j{joint_no}_deg"] = diffs[joint_index]
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row[f"{prefix}_max_error_axis"] = max_error_axis
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row[f"{prefix}_max_error_deg"] = max_error
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row[f"{prefix}_rms_error_deg"] = rms_error
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return max_error, rms_error, max_error_axis
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def build_trigger_status_rows(trigger_frames: list[dict], status_records: list[dict], shot_config: dict) -> list[dict]:
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"""按触发顺序对齐命令帧、当前状态帧以及最接近示教点的反馈状态帧。"""
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rows: list[dict] = []
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trigger_waypoint_indices = [index for index, flag in enumerate(shot_config["shot_flags"]) if flag]
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status_by_sequence = {record["sequence"]: record for record in status_records}
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status_sequence_set = set(status_by_sequence)
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for trigger_no, (trigger_frame, waypoint_index) in enumerate(zip(trigger_frames, trigger_waypoint_indices), start=1):
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teach_deg = [math.degrees(value) for value in shot_config["traj_waypoints"][waypoint_index]]
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current_status_sequence = trigger_frame["sequence"] - 8
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current_status = status_by_sequence[current_status_sequence]
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row = {
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"trigger_no": trigger_no,
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"waypoint_index": waypoint_index,
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"trigger_frame_number": trigger_frame["frame_number"],
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"trigger_time_relative_s": trigger_frame["time_relative_s"],
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"trigger_sequence": trigger_frame["sequence"],
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"paired_status_frame_number": current_status["frame_number"],
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"paired_status_time_relative_s": current_status["time_relative_s"],
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"paired_status_sequence": current_status["sequence"],
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"paired_status_timestamp": current_status["timestamp"],
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"paired_status_to_trigger_sequence_delta": current_status["sequence"] - trigger_frame["sequence"],
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"paired_status_to_trigger_time_ms": (current_status["time_relative_s"] - trigger_frame["time_relative_s"]) * 1000.0,
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}
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for joint_index in range(6):
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joint_no = joint_index + 1
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row[f"teach_j{joint_no}_deg"] = teach_deg[joint_index]
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build_diff_row(
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"paired_status",
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[current_status[f"j{joint_no}_deg"] for joint_no in range(1, 7)],
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teach_deg,
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row,
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)
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best_candidate = None
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for delta_cycles in range(-SEARCH_WINDOW_CYCLES, SEARCH_WINDOW_CYCLES + 1):
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candidate_sequence = current_status_sequence + delta_cycles
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if candidate_sequence not in status_sequence_set:
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continue
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candidate = status_by_sequence[candidate_sequence]
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diffs = [candidate[f"j{joint_no}_deg"] - teach_deg[joint_no - 1] for joint_no in range(1, 7)]
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rms_error = math.sqrt(sum(value * value for value in diffs) / 6.0)
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max_error = max(abs(value) for value in diffs)
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score = (rms_error, max_error, abs(delta_cycles))
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if best_candidate is None or score < best_candidate["score"]:
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best_candidate = {
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"score": score,
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"delta_cycles": delta_cycles,
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"record": candidate,
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}
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if best_candidate is None:
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raise RuntimeError(f"Trigger {trigger_no} 未找到候选状态帧。")
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best_status = best_candidate["record"]
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row["best_status_frame_number"] = best_status["frame_number"]
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row["best_status_time_relative_s"] = best_status["time_relative_s"]
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row["best_status_sequence"] = best_status["sequence"]
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row["best_status_timestamp"] = best_status["timestamp"]
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row["best_status_delta_from_paired_cycles"] = best_candidate["delta_cycles"]
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row["best_status_delta_from_trigger_sequence"] = best_status["sequence"] - trigger_frame["sequence"]
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row["best_status_time_after_trigger_ms"] = (best_status["time_relative_s"] - trigger_frame["time_relative_s"]) * 1000.0
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build_diff_row(
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"best_status",
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[best_status[f"j{joint_no}_deg"] for joint_no in range(1, 7)],
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teach_deg,
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row,
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)
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rows.append(row)
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return rows
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def write_csv(path: Path, rows: list[dict]) -> None:
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"""把分析结果落成 UTF-8 CSV。"""
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if not rows:
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raise ValueError(f"No rows to write: {path}")
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("w", newline="", encoding="utf-8") as handle:
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writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys()))
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writer.writeheader()
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writer.writerows(rows)
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def build_summary(command_records: list[dict], trigger_status_rows: list[dict]) -> dict:
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"""汇总命令序列偏移和状态反馈误差分布。"""
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sequence_offsets = [row["trigger_sequence"] - row["paired_status_sequence"] for row in trigger_status_rows]
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best_paired_cycle_offsets = [row["best_status_delta_from_paired_cycles"] for row in trigger_status_rows]
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best_trigger_sequence_offsets = [row["best_status_delta_from_trigger_sequence"] for row in trigger_status_rows]
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best_time_offsets = [row["best_status_time_after_trigger_ms"] for row in trigger_status_rows]
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paired_max_errors = [row["paired_status_max_error_deg"] for row in trigger_status_rows]
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best_max_errors = [row["best_status_max_error_deg"] for row in trigger_status_rows]
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paired_axes = Counter(row["paired_status_max_error_axis"] for row in trigger_status_rows)
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best_axes = Counter(row["best_status_max_error_axis"] for row in trigger_status_rows)
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sequence_offset_counter = Counter()
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trigger_frames = pick_trigger_first_high_frames(command_records)
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status_pairs = {row["trigger_frame_number"]: row for row in trigger_status_rows}
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for trigger_frame in trigger_frames:
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sequence_offset_counter[trigger_frame["sequence"] - status_pairs[trigger_frame["frame_number"]]["paired_status_sequence"]] += 1
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return {
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"pcap_path": str(DEFAULT_PCAP),
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"command_count": len(command_records),
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"trigger_count": len(trigger_status_rows),
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"command_minus_paired_status_sequence_counter": dict(sequence_offset_counter),
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"paired_status_average_max_error_deg": sum(paired_max_errors) / len(paired_max_errors),
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"paired_status_max_error_deg": max(paired_max_errors),
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"paired_status_max_error_axis_counter": dict(paired_axes),
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"best_status_average_max_error_deg": sum(best_max_errors) / len(best_max_errors),
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"best_status_max_error_deg": max(best_max_errors),
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"best_status_max_error_axis_counter": dict(best_axes),
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"best_status_delta_from_paired_cycles_counter": dict(Counter(best_paired_cycle_offsets)),
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"best_status_delta_from_trigger_sequence_counter": dict(Counter(best_trigger_sequence_offsets)),
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"best_status_time_after_trigger_ms_min": min(best_time_offsets),
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"best_status_time_after_trigger_ms_max": max(best_time_offsets),
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"best_status_time_after_trigger_ms_avg": sum(best_time_offsets) / len(best_time_offsets),
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"search_window_cycles": SEARCH_WINDOW_CYCLES,
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}
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def build_manual_compare_rows(trigger_status_rows: list[dict]) -> list[dict]:
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"""整理成便于人工逐点核对的三时刻对照表。"""
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rows: list[dict] = []
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for row in trigger_status_rows:
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rows.append(
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{
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"trigger_no": row["trigger_no"],
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"waypoint_index": row["waypoint_index"],
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"trigger_command_sequence": row["trigger_sequence"],
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"trigger_command_frame": row["trigger_frame_number"],
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"trigger_command_time_relative_s": row["trigger_time_relative_s"],
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"trigger_current_status_sequence": row["paired_status_sequence"],
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"trigger_current_status_frame": row["paired_status_frame_number"],
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"trigger_current_status_time_relative_s": row["paired_status_time_relative_s"],
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"command_leads_status_cycles": row["trigger_sequence"] - row["paired_status_sequence"],
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"trigger_current_status_max_error_axis": row["paired_status_max_error_axis"],
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"trigger_current_status_max_error_deg": row["paired_status_max_error_deg"],
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"trigger_current_status_rms_error_deg": row["paired_status_rms_error_deg"],
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"best_status_sequence": row["best_status_sequence"],
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"best_status_frame": row["best_status_frame_number"],
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"best_status_time_relative_s": row["best_status_time_relative_s"],
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"best_status_delay_from_current_status_cycles": row["best_status_delta_from_paired_cycles"],
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"best_status_delay_from_trigger_command_cycles": row["best_status_delta_from_trigger_sequence"],
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"best_status_delay_from_trigger_command_ms": row["best_status_time_after_trigger_ms"],
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"best_status_max_error_axis": row["best_status_max_error_axis"],
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"best_status_max_error_deg": row["best_status_max_error_deg"],
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"best_status_rms_error_deg": row["best_status_rms_error_deg"],
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}
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)
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return rows
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def main() -> None:
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"""执行状态反馈提取、触发对齐和摘要落盘。"""
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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rows = load_udp_rows(DEFAULT_PCAP, DEFAULT_TSHARK)
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command_records = decode_command_records(rows, client_ip="192.168.10.10", robot_ip="192.168.10.11")
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status_records = decode_status_records(rows, client_ip="192.168.10.10", robot_ip="192.168.10.11")
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trigger_frames = pick_trigger_first_high_frames(command_records)
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shot_config = load_uttc_ms11_config()
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trigger_status_rows = build_trigger_status_rows(trigger_frames, status_records, shot_config)
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manual_compare_rows = build_manual_compare_rows(trigger_status_rows)
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summary = build_summary(command_records, trigger_status_rows)
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write_csv(OUTPUT_DIR / "2026042802-1_j519_status_feedback_all.csv", status_records)
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write_csv(OUTPUT_DIR / "2026042802-1_trigger_status_feedback_vs_teach_points.csv", trigger_status_rows)
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write_csv(OUTPUT_DIR / "2026042802-1_trigger_manual_compare.csv", manual_compare_rows)
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(OUTPUT_DIR / "2026042802-1_status_feedback_summary.json").write_text(
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json.dumps(summary, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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print(json.dumps(summary, ensure_ascii=False, indent=2))
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if __name__ == "__main__":
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main()
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