✨ feat(*): 添加触发样本偏移与实发轨迹分析导出

* 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

analysis/2026042802-1/2026042802-1_analysis_summary.json

@@ -0,0 +1,21 @@
+{
+  "pcap_path": "D:\\Dev\\Codes\\rvbust-code\\FlyingShotPkg_3.15_VDA\\Rvbust\\uttc-20260428\\2026042802-1.pcap",
+  "all_command_count": 1788,
+  "trigger_count": 17,
+  "existing_runtime_actual_send_exists": true,
+  "existing_runtime_actual_send_has_io_columns": false,
+  "existing_shot_events_exists": true,
+  "pcap_specific_combined_export_preexisting": false,
+  "average_max_error_deg": 4.241583591714439,
+  "max_error_deg": 11.130744251720401,
+  "average_rms_error_deg": 2.5406136706026206,
+  "max_error_axis_counter": {
+    "J6": 9,
+    "J4": 5,
+    "J5": 1,
+    "J2": 1,
+    "J1": 1
+  },
+  "order_only_addr_mismatch_count": 14,
+  "real_addr_mismatch_count": 0
+}

analysis/2026042802-1/2026042802-1_j519_trigger_frames.csv

@@ -0,0 +1,18 @@
+frame_number,time_relative_s,sequence,last_data,write_io_type,write_io_index,write_io_mask,write_io_value,io_addrs,j1_deg,j2_deg,j3_deg,j4_deg,j5_deg,j6_deg,ext1_deg,ext2_deg,ext3_deg
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analysis/2026042802-1/2026042802-1_status_feedback_summary.json

@@ -0,0 +1,37 @@
+{
+  "pcap_path": "D:\\Dev\\Codes\\rvbust-code\\FlyingShotPkg_3.15_VDA\\Rvbust\\uttc-20260428\\2026042802-1.pcap",
+  "command_count": 1788,
+  "trigger_count": 17,
+  "command_minus_paired_status_sequence_counter": {
+    "8": 17
+  },
+  "paired_status_average_max_error_deg": 6.469219290127501,
+  "paired_status_max_error_deg": 16.123934474475888,
+  "paired_status_max_error_axis_counter": {
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+    "J6": 9,
+    "J2": 4,
+    "J4": 2
+  },
+  "best_status_average_max_error_deg": 0.14953970473465206,
+  "best_status_max_error_deg": 0.5696919293327056,
+  "best_status_max_error_axis_counter": {
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+    "J1": 3,
+    "J5": 1,
+    "J2": 2,
+    "J4": 1
+  },
+  "best_status_delta_from_paired_cycles_counter": {
+    "10": 8,
+    "9": 9
+  },
+  "best_status_delta_from_trigger_sequence_counter": {
+    "2": 8,
+    "1": 9
+  },
+  "best_status_time_after_trigger_ms_min": 71.83799999999962,
+  "best_status_time_after_trigger_ms_max": 79.96399999999859,
+  "best_status_time_after_trigger_ms_avg": 75.6613529411763,
+  "search_window_cycles": 20
+}

analysis/2026042802-1/2026042802-1_trigger_manual_compare.csv

@@ -0,0 +1,18 @@
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analysis/2026042802-1/2026042802-1_trigger_offset_6_7_8_compare.csv

@@ -0,0 +1,18 @@
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analysis/2026042802-1/2026042802-1_trigger_offset_6_7_8_summary.json

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+{
+  "rows": 17,
+  "best_offset_win_counts": {
+    "6": 17,
+    "7": 0,
+    "8": 0
+  },
+  "average_max_error_deg": {
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+    "7": 5.068092118561757,
+    "8": 5.9316839578472145
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+  "average_rms_error_deg": {
+    "6": 2.5406136706026206,
+    "7": 3.021296972079456,
+    "8": 3.5201758351699857
+  }
+}

analysis/2026042802-1/2026042802-1_trigger_status_feedback_vs_teach_points.csv

@@ -0,0 +1,18 @@
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analysis/2026042802-1/2026042802-1_trigger_vs_teach_points.csv

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+trigger_no,waypoint_index,frame_number,sequence,time_relative_s,write_io_value,io_addrs,config_addr,max_error_axis,max_error_deg,rms_error_deg,j1_actual_deg,j1_teach_deg,diff_j1_deg,j2_actual_deg,j2_teach_deg,diff_j2_deg,j3_actual_deg,j3_teach_deg,diff_j3_deg,j4_actual_deg,j4_teach_deg,diff_j4_deg,j5_actual_deg,j5_teach_deg,diff_j5_deg,j6_actual_deg,j6_teach_deg,diff_j6_deg
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+2,2,2151,1381198,6.480252,14,"[2, 3, 4]","[3, 4, 2]",J4,2.8167225076858244,1.6562461612538633,56.34336471557617,55.34775509527405,0.995609620302119,11.66723918914795,11.807039833001637,-0.13980064385368784,-6.600923538208008,-7.0090952672806885,0.40817172907268073,-68.1976089477539,-71.01433145543973,2.8167225076858244,6.058437347412109,6.012065051914967,0.04637229549714217,71.5392837524414,74.24953191606797,-2.7102481636265594
+3,3,2223,1381226,6.704225,14,"[2, 3, 4]","[3, 4, 2]",J6,11.130744251720401,5.955607695749779,53.50831985473633,55.109808014787404,-1.6014881600510762,7.412222862243652,8.759497374223619,-1.3472745119799665,-9.629739761352539,-8.518216825284897,-1.111522936067642,-32.30571365356445,-41.11725046606459,8.811536812500137,12.488306045532227,10.108488114686867,2.379817930845359,30.826189041137695,41.956933292858096,-11.130744251720401
+4,4,2336,1381269,7.048266,10,"[2, 4]","[4, 2]",J6,2.3116842904428125,1.3731583485126422,44.433006286621094,43.65359310453334,0.779413182087751,-2.1156294345855713,-1.629659559507137,-0.48596987507843425,-18.39777374267578,-17.715753611915318,-0.6820201307604634,4.8371992111206055,5.995208633582219,-1.158009422461613,33.99372482299805,32.17171770646655,1.8220071165314948,-20.26228904724121,-22.573973337684023,2.3116842904428125
+5,5,2477,1381321,7.464289,10,"[2, 4]","[4, 2]",J5,2.171828177649214,1.404704060246151,65.84967041015625,64.58244475717193,1.2672256529843224,5.695003032684326,4.262979315506351,1.4320237171779748,-14.563636779785156,-15.669217122643433,1.1055803428582767,-28.69342613220215,-29.952927185666542,1.259501053464394,27.678611755371094,29.850439933020308,-2.171828177649214,46.437156677246094,45.62652743544272,0.810629241803376
+6,6,2665,1381391,8.024277,12,"[3, 4]","[3, 4]",J2,1.408993592340284,0.8424031169595625,60.847530364990234,60.47948252708421,0.36804783790602613,24.47777557373047,23.068781981390185,1.408993592340284,-5.617043972015381,-5.01126420129949,-0.6057797707158912,34.8044319152832,36.10685486878251,-1.3024229534993097,5.786255359649658,5.525681179628412,0.260574180021246,-31.2253475189209,-31.3025636495649,0.07721613064400046
+7,7,2811,1381447,8.472317,12,"[3, 4]","[3, 4]",J6,7.1941073647892395,4.618545262978315,70.8250503540039,70.47583707168602,0.3492132823178906,10.95259952545166,16.39384887825908,-5.4412493528074215,-13.136046409606934,-13.456948007467595,0.3209015978606615,-34.68459701538086,-27.892318852946243,-6.7922781624346165,14.022420883178711,14.53566195089614,-0.5132410677174288,38.90604019165039,31.71193282686115,7.1941073647892395
+8,8,2908,1381483,8.760361,10,"[2, 4]","[4, 2]",J6,5.93988407546847,3.533132924843701,69.40516662597656,69.58246408878419,-0.1772974628076298,-20.066781997680664,-17.10571341532583,-2.9610685823548337,-8.152935981750488,-8.710589696831251,0.5576537150807628,-63.93235397338867,-58.47569441890704,-5.456659554481632,6.775912284851074,7.63059003611043,-0.8546777512593557,70.37761688232422,64.43773280685575,5.93988407546847
+9,9,3112,1381561,9.384332,10,"[2, 4]","[4, 2]",J4,2.5240706198644887,1.630593251762862,73.90592956542969,73.57125874861414,0.3346708168155459,-8.016124725341797,-6.42984524964374,-1.5862794756980572,-9.002426147460938,-9.628579811400881,0.6261536639399434,-131.3327178955078,-128.80864727564332,-2.5240706198644887,15.526206970214844,14.671082817606491,0.8551241526083526,142.41668701171875,140.00204816414424,2.4146388475745084
+10,10,3170,1381583,9.560358,10,"[2, 4]","[4, 2]",J4,3.3929965007450846,2.1059862266772753,76.72688293457031,75.56938603377543,1.1574969007948823,-16.518726348876953,-14.67930590788221,-1.8394204409947434,-7.15147066116333,-7.294156094303152,0.14268543313982196,-127.53003692626953,-130.92303342701462,3.3929965007450846,18.53788948059082,17.688361072687904,0.8495284079029162,138.7897491455078,141.89350789658602,-3.103758751078203
+11,11,3350,1381651,10.104322,10,"[2, 4]","[4, 2]",J6,2.9848240516596434,2.0134925384056306,83.71662139892578,86.0934977330502,-2.376876334124418,-13.186573028564453,-14.498333177585573,1.3117601490211204,-13.738808631896973,-13.681510793859989,-0.05729783803698396,-72.70317840576172,-69.8682955440411,-2.834882861720615,26.66758918762207,26.742681901805224,-0.07509271418315322,91.98423767089844,88.9994136192388,2.9848240516596434
+12,12,3470,1381697,10.472333,10,"[2, 4]","[4, 2]",J6,4.000958239175844,1.9902461373621054,59.78955841064453,61.720732564877665,-1.9311741542331333,-2.7094168663024902,-4.2327893098442155,1.5233724435417253,-8.933828353881836,-9.784423185760874,0.8505948318790377,-108.24857330322266,-108.38408270751574,0.13550940429308866,21.177492141723633,22.16077221865394,-0.9832800769303063,114.14110565185547,118.14206389103131,-4.000958239175844
+13,13,3627,1381756,10.944371,12,"[3, 4]","[4, 3]",J1,6.656208938967296,4.109282348845555,86.41329193115234,79.75708299218505,6.656208938967296,4.266173362731934,4.6402310571176475,-0.3740576943857139,-19.15987777709961,-15.311185536748695,-3.848692240350914,-52.24028015136719,-56.03531247084017,3.7950323194729805,29.853723526000977,26.13313788666632,3.720585639334658,55.86907196044922,52.152257172101606,3.7168147883476124
+14,14,3710,1381787,11.192339,10,"[2, 4]","[4, 2]",J6,2.93518040873289,1.5679827735552678,110.7523193359375,108.95555081237923,1.7967685235582707,0.8751887679100037,1.3707600427061273,-0.4955712747961236,-34.756534576416016,-33.59442519685133,-1.1621093795646829,-48.14332580566406,-48.36736100282485,0.2240351971607879,45.0865592956543,43.963403989432074,1.1231553062222233,87.97444152832031,85.03926111958742,2.93518040873289
+15,15,3796,1381820,11.456361,10,"[2, 4]","[4, 2]",J4,2.573146734494266,1.686689503314435,111.79177856445312,110.58484797608095,1.206930588372174,-6.225440502166748,-3.860899593758653,-2.364540908408095,-31.958988189697266,-32.39640711653168,0.4374189268344111,-53.68573760986328,-51.112590875369015,-2.573146734494266,44.76150894165039,44.2291595765054,0.532349365144988,89.33454895019531,87.62399978413751,1.7105491660578025
+16,16,3870,1381848,11.680362,10,"[2, 4]","[4, 2]",J6,3.8105220440607894,2.197782779718695,117.87474060058594,118.09595224767921,-0.2212116470932699,-19.607177734375,-17.37638727211256,-2.2307904622624406,-30.329103469848633,-31.06900084212659,0.7398973722779587,-56.600250244140625,-59.56053843778568,2.960288193645056,49.08655548095703,48.73257576059714,0.353979720359888,90.32424926757812,94.13477131163891,-3.8105220440607894
+17,18,4138,1381950,12.496365,12,"[3, 4]","[4, 3]",J4,6.848522435693766,4.03614029285736,59.93589401245117,60.28248244838438,-0.34658843593320654,-21.102140426635742,-22.938080568038327,1.8359401414025847,-9.684239387512207,-10.333247998560786,0.6490086110485791,70.64311981201172,77.49164224770549,-6.848522435693766,32.941307067871094,35.58337770883919,-2.642070640968093,-63.34855651855469,-69.66898071584893,6.320424197294244

analysis/analyze_2026042802_1_status_feedback_vs_teach_points.py

@@ -0,0 +1,363 @@
+#!/usr/bin/env python3
+"""提取 2026042802-1 抓包中的 60015 状态反馈，并和 UTTC_MS11 示教点对比。"""
+
+from __future__ import annotations
+
+import csv
+import json
+import math
+import struct
+import subprocess
+from collections import Counter
+from pathlib import Path
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_PCAP = REPO_ROOT.parent / "Rvbust" / "uttc-20260428" / "2026042802-1.pcap"
+DEFAULT_TSHARK = Path(r"D:\Zyx\Downloads\WiresharkPortable32\App\Wireshark\tshark.exe")
+OUTPUT_DIR = REPO_ROOT / "analysis" / "2026042802-1"
+CONFIG_PATH = REPO_ROOT / "Config" / "RobotConfig.json"
+SEARCH_WINDOW_CYCLES = 20
+
+
+def be_u32(data: bytes, offset: int) -> int:
+    """按大端读取 4 字节无符号整数。"""
+    return struct.unpack(">I", data[offset : offset + 4])[0]
+
+
+def be_u16(data: bytes, offset: int) -> int:
+    """按大端读取 2 字节无符号整数。"""
+    return struct.unpack(">H", data[offset : offset + 2])[0]
+
+
+def be_f32(data: bytes, offset: int) -> float:
+    """按大端读取 4 字节浮点数。"""
+    return struct.unpack(">f", data[offset : offset + 4])[0]
+
+
+def load_udp_rows(pcap: Path, tshark: Path) -> list[list[str]]:
+    """提取 UDP 60015 原始字段，后续按方向和长度拆分命令与状态。"""
+    command = [
+        str(tshark),
+        "-r",
+        str(pcap),
+        "-Y",
+        "udp.port==60015",
+        "-T",
+        "fields",
+        "-e",
+        "frame.number",
+        "-e",
+        "frame.time_relative",
+        "-e",
+        "ip.src",
+        "-e",
+        "ip.dst",
+        "-e",
+        "udp.payload",
+    ]
+    output = subprocess.check_output(command, text=True, encoding="utf-8", errors="ignore")
+    rows: list[list[str]] = []
+    for line in output.splitlines():
+        if not line.strip():
+            continue
+        parts = line.split("\t")
+        if len(parts) >= 5:
+            rows.append(parts[:5])
+    return rows
+
+
+def decode_command_records(rows: list[list[str]], client_ip: str, robot_ip: str) -> list[dict]:
+    """把 64B J519 命令帧解码成结构化记录。"""
+    records: list[dict] = []
+    for frame_no, time_rel, ip_src, ip_dst, payload_hex in rows:
+        if ip_src != client_ip or ip_dst != robot_ip:
+            continue
+
+        payload = bytes.fromhex(payload_hex)
+        if len(payload) != 64:
+            continue
+
+        records.append(
+            {
+                "frame_number": int(frame_no),
+                "time_relative_s": float(time_rel),
+                "sequence": be_u32(payload, 0x08),
+                "write_io_value": be_u16(payload, 0x18),
+                "j1_deg": be_f32(payload, 0x1C),
+                "j2_deg": be_f32(payload, 0x20),
+                "j3_deg": be_f32(payload, 0x24),
+                "j4_deg": be_f32(payload, 0x28),
+                "j5_deg": be_f32(payload, 0x2C),
+                "j6_deg": be_f32(payload, 0x30),
+            }
+        )
+    return records
+
+
+def decode_status_records(rows: list[list[str]], client_ip: str, robot_ip: str) -> list[dict]:
+    """把 132B J519 状态帧按运行时代码同口径解码。"""
+    records: list[dict] = []
+    for frame_no, time_rel, ip_src, ip_dst, payload_hex in rows:
+        if ip_src != robot_ip or ip_dst != client_ip:
+            continue
+
+        payload = bytes.fromhex(payload_hex)
+        if len(payload) != 132:
+            continue
+
+        status = payload[0x0C]
+        joints = [be_f32(payload, 0x3C + index * 4) for index in range(6)]
+        pose = [be_f32(payload, 0x18 + index * 4) for index in range(6)]
+        records.append(
+            {
+                "frame_number": int(frame_no),
+                "time_relative_s": float(time_rel),
+                "sequence": be_u32(payload, 0x08),
+                "status": status,
+                "accepts_command": bool(status & 0b0001),
+                "received_command": bool(status & 0b0010),
+                "system_ready": bool(status & 0b0100),
+                "robot_in_motion": bool(status & 0b1000),
+                "read_io_value": be_u16(payload, 0x12),
+                "timestamp": be_u32(payload, 0x14),
+                "pose_x_mm": pose[0],
+                "pose_y_mm": pose[1],
+                "pose_z_mm": pose[2],
+                "pose_w_deg": pose[3],
+                "pose_p_deg": pose[4],
+                "pose_r_deg": pose[5],
+                "j1_deg": joints[0],
+                "j2_deg": joints[1],
+                "j3_deg": joints[2],
+                "j4_deg": joints[3],
+                "j5_deg": joints[4],
+                "j6_deg": joints[5],
+            }
+        )
+    return records
+
+
+def pick_trigger_first_high_frames(records: list[dict]) -> list[dict]:
+    """由于 io_keep_cycles=2，只保留每组高电平脉冲的第一帧。"""
+    trigger_frames: list[dict] = []
+    previous_high = False
+    for record in records:
+        current_high = record["write_io_value"] > 0
+        if current_high and not previous_high:
+            trigger_frames.append(record)
+        previous_high = current_high
+    return trigger_frames
+
+
+def load_uttc_ms11_config() -> dict:
+    """读取 UTTC_MS11 的示教点和触发配置。"""
+    config = json.loads(CONFIG_PATH.read_text(encoding="utf-8"))
+    return config["flying_shots"]["UTTC_MS11"]
+
+
+def build_diff_row(prefix: str, actual_deg: list[float], teach_deg: list[float], row: dict) -> tuple[float, float, str]:
+    """向结果行写入逐轴误差，并返回聚合误差。"""
+    diffs = [actual_deg[index] - teach_deg[index] for index in range(6)]
+    abs_diffs = [abs(value) for value in diffs]
+    max_error = max(abs_diffs)
+    max_error_axis = f"J{abs_diffs.index(max_error) + 1}"
+    rms_error = math.sqrt(sum(value * value for value in diffs) / 6.0)
+
+    for joint_index in range(6):
+        joint_no = joint_index + 1
+        row[f"{prefix}_j{joint_no}_actual_deg"] = actual_deg[joint_index]
+        row[f"{prefix}_diff_j{joint_no}_deg"] = diffs[joint_index]
+
+    row[f"{prefix}_max_error_axis"] = max_error_axis
+    row[f"{prefix}_max_error_deg"] = max_error
+    row[f"{prefix}_rms_error_deg"] = rms_error
+    return max_error, rms_error, max_error_axis
+
+
+def build_trigger_status_rows(trigger_frames: list[dict], status_records: list[dict], shot_config: dict) -> list[dict]:
+    """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。"""
+    rows: list[dict] = []
+    trigger_waypoint_indices = [index for index, flag in enumerate(shot_config["shot_flags"]) if flag]
+    status_by_sequence = {record["sequence"]: record for record in status_records}
+    status_sequence_set = set(status_by_sequence)
+
+    for trigger_no, (trigger_frame, waypoint_index) in enumerate(zip(trigger_frames, trigger_waypoint_indices), start=1):
+        teach_deg = [math.degrees(value) for value in shot_config["traj_waypoints"][waypoint_index]]
+        current_status_sequence = trigger_frame["sequence"] - 8
+        current_status = status_by_sequence[current_status_sequence]
+
+        row = {
+            "trigger_no": trigger_no,
+            "waypoint_index": waypoint_index,
+            "trigger_frame_number": trigger_frame["frame_number"],
+            "trigger_time_relative_s": trigger_frame["time_relative_s"],
+            "trigger_sequence": trigger_frame["sequence"],
+            "paired_status_frame_number": current_status["frame_number"],
+            "paired_status_time_relative_s": current_status["time_relative_s"],
+            "paired_status_sequence": current_status["sequence"],
+            "paired_status_timestamp": current_status["timestamp"],
+            "paired_status_to_trigger_sequence_delta": current_status["sequence"] - trigger_frame["sequence"],
+            "paired_status_to_trigger_time_ms": (current_status["time_relative_s"] - trigger_frame["time_relative_s"]) * 1000.0,
+        }
+
+        for joint_index in range(6):
+            joint_no = joint_index + 1
+            row[f"teach_j{joint_no}_deg"] = teach_deg[joint_index]
+
+        build_diff_row(
+            "paired_status",
+            [current_status[f"j{joint_no}_deg"] for joint_no in range(1, 7)],
+            teach_deg,
+            row,
+        )
+
+        best_candidate = None
+        for delta_cycles in range(-SEARCH_WINDOW_CYCLES, SEARCH_WINDOW_CYCLES + 1):
+            candidate_sequence = current_status_sequence + delta_cycles
+            if candidate_sequence not in status_sequence_set:
+                continue
+
+            candidate = status_by_sequence[candidate_sequence]
+            diffs = [candidate[f"j{joint_no}_deg"] - teach_deg[joint_no - 1] for joint_no in range(1, 7)]
+            rms_error = math.sqrt(sum(value * value for value in diffs) / 6.0)
+            max_error = max(abs(value) for value in diffs)
+            score = (rms_error, max_error, abs(delta_cycles))
+            if best_candidate is None or score < best_candidate["score"]:
+                best_candidate = {
+                    "score": score,
+                    "delta_cycles": delta_cycles,
+                    "record": candidate,
+                }
+
+        if best_candidate is None:
+            raise RuntimeError(f"Trigger {trigger_no} 未找到候选状态帧。")
+
+        best_status = best_candidate["record"]
+        row["best_status_frame_number"] = best_status["frame_number"]
+        row["best_status_time_relative_s"] = best_status["time_relative_s"]
+        row["best_status_sequence"] = best_status["sequence"]
+        row["best_status_timestamp"] = best_status["timestamp"]
+        row["best_status_delta_from_paired_cycles"] = best_candidate["delta_cycles"]
+        row["best_status_delta_from_trigger_sequence"] = best_status["sequence"] - trigger_frame["sequence"]
+        row["best_status_time_after_trigger_ms"] = (best_status["time_relative_s"] - trigger_frame["time_relative_s"]) * 1000.0
+
+        build_diff_row(
+            "best_status",
+            [best_status[f"j{joint_no}_deg"] for joint_no in range(1, 7)],
+            teach_deg,
+            row,
+        )
+
+        rows.append(row)
+
+    return rows
+
+
+def write_csv(path: Path, rows: list[dict]) -> None:
+    """把分析结果落成 UTF-8 CSV。"""
+    if not rows:
+        raise ValueError(f"No rows to write: {path}")
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys()))
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def build_summary(command_records: list[dict], trigger_status_rows: list[dict]) -> dict:
+    """汇总命令序列偏移和状态反馈误差分布。"""
+    sequence_offsets = [row["trigger_sequence"] - row["paired_status_sequence"] for row in trigger_status_rows]
+    best_paired_cycle_offsets = [row["best_status_delta_from_paired_cycles"] for row in trigger_status_rows]
+    best_trigger_sequence_offsets = [row["best_status_delta_from_trigger_sequence"] for row in trigger_status_rows]
+    best_time_offsets = [row["best_status_time_after_trigger_ms"] for row in trigger_status_rows]
+    paired_max_errors = [row["paired_status_max_error_deg"] for row in trigger_status_rows]
+    best_max_errors = [row["best_status_max_error_deg"] for row in trigger_status_rows]
+    paired_axes = Counter(row["paired_status_max_error_axis"] for row in trigger_status_rows)
+    best_axes = Counter(row["best_status_max_error_axis"] for row in trigger_status_rows)
+
+    sequence_offset_counter = Counter()
+    trigger_frames = pick_trigger_first_high_frames(command_records)
+    status_pairs = {row["trigger_frame_number"]: row for row in trigger_status_rows}
+    for trigger_frame in trigger_frames:
+        sequence_offset_counter[trigger_frame["sequence"] - status_pairs[trigger_frame["frame_number"]]["paired_status_sequence"]] += 1
+
+    return {
+        "pcap_path": str(DEFAULT_PCAP),
+        "command_count": len(command_records),
+        "trigger_count": len(trigger_status_rows),
+        "command_minus_paired_status_sequence_counter": dict(sequence_offset_counter),
+        "paired_status_average_max_error_deg": sum(paired_max_errors) / len(paired_max_errors),
+        "paired_status_max_error_deg": max(paired_max_errors),
+        "paired_status_max_error_axis_counter": dict(paired_axes),
+        "best_status_average_max_error_deg": sum(best_max_errors) / len(best_max_errors),
+        "best_status_max_error_deg": max(best_max_errors),
+        "best_status_max_error_axis_counter": dict(best_axes),
+        "best_status_delta_from_paired_cycles_counter": dict(Counter(best_paired_cycle_offsets)),
+        "best_status_delta_from_trigger_sequence_counter": dict(Counter(best_trigger_sequence_offsets)),
+        "best_status_time_after_trigger_ms_min": min(best_time_offsets),
+        "best_status_time_after_trigger_ms_max": max(best_time_offsets),
+        "best_status_time_after_trigger_ms_avg": sum(best_time_offsets) / len(best_time_offsets),
+        "search_window_cycles": SEARCH_WINDOW_CYCLES,
+    }
+
+
+def build_manual_compare_rows(trigger_status_rows: list[dict]) -> list[dict]:
+    """整理成便于人工逐点核对的三时刻对照表。"""
+    rows: list[dict] = []
+    for row in trigger_status_rows:
+        rows.append(
+            {
+                "trigger_no": row["trigger_no"],
+                "waypoint_index": row["waypoint_index"],
+                "trigger_command_sequence": row["trigger_sequence"],
+                "trigger_command_frame": row["trigger_frame_number"],
+                "trigger_command_time_relative_s": row["trigger_time_relative_s"],
+                "trigger_current_status_sequence": row["paired_status_sequence"],
+                "trigger_current_status_frame": row["paired_status_frame_number"],
+                "trigger_current_status_time_relative_s": row["paired_status_time_relative_s"],
+                "command_leads_status_cycles": row["trigger_sequence"] - row["paired_status_sequence"],
+                "trigger_current_status_max_error_axis": row["paired_status_max_error_axis"],
+                "trigger_current_status_max_error_deg": row["paired_status_max_error_deg"],
+                "trigger_current_status_rms_error_deg": row["paired_status_rms_error_deg"],
+                "best_status_sequence": row["best_status_sequence"],
+                "best_status_frame": row["best_status_frame_number"],
+                "best_status_time_relative_s": row["best_status_time_relative_s"],
+                "best_status_delay_from_current_status_cycles": row["best_status_delta_from_paired_cycles"],
+                "best_status_delay_from_trigger_command_cycles": row["best_status_delta_from_trigger_sequence"],
+                "best_status_delay_from_trigger_command_ms": row["best_status_time_after_trigger_ms"],
+                "best_status_max_error_axis": row["best_status_max_error_axis"],
+                "best_status_max_error_deg": row["best_status_max_error_deg"],
+                "best_status_rms_error_deg": row["best_status_rms_error_deg"],
+            }
+        )
+    return rows
+
+
+def main() -> None:
+    """执行状态反馈提取、触发对齐和摘要落盘。"""
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    rows = load_udp_rows(DEFAULT_PCAP, DEFAULT_TSHARK)
+    command_records = decode_command_records(rows, client_ip="192.168.10.10", robot_ip="192.168.10.11")
+    status_records = decode_status_records(rows, client_ip="192.168.10.10", robot_ip="192.168.10.11")
+    trigger_frames = pick_trigger_first_high_frames(command_records)
+    shot_config = load_uttc_ms11_config()
+    trigger_status_rows = build_trigger_status_rows(trigger_frames, status_records, shot_config)
+    manual_compare_rows = build_manual_compare_rows(trigger_status_rows)
+    summary = build_summary(command_records, trigger_status_rows)
+
+    write_csv(OUTPUT_DIR / "2026042802-1_j519_status_feedback_all.csv", status_records)
+    write_csv(OUTPUT_DIR / "2026042802-1_trigger_status_feedback_vs_teach_points.csv", trigger_status_rows)
+    write_csv(OUTPUT_DIR / "2026042802-1_trigger_manual_compare.csv", manual_compare_rows)
+    (OUTPUT_DIR / "2026042802-1_status_feedback_summary.json").write_text(
+        json.dumps(summary, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+
+    print(json.dumps(summary, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()

analysis/analyze_2026042802_1_trigger_vs_teach_points.py

@@ -0,0 +1,250 @@
+#!/usr/bin/env python3
+"""提取 2026042802-1 抓包中的真实 J519 发包，并对比 UTTC_MS11 示教点。"""
+
+from __future__ import annotations
+
+import csv
+import json
+import math
+import struct
+import subprocess
+from collections import Counter
+from pathlib import Path
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_PCAP = REPO_ROOT.parent / "Rvbust" / "uttc-20260428" / "2026042802-1.pcap"
+DEFAULT_TSHARK = Path(r"D:\Zyx\Downloads\WiresharkPortable32\App\Wireshark\tshark.exe")
+OUTPUT_DIR = REPO_ROOT / "analysis" / "2026042802-1"
+CONFIG_PATH = REPO_ROOT / "Config" / "RobotConfig.json"
+RUNTIME_DATA_DIR = REPO_ROOT / "Config" / "Data" / "UTTC_MS11"
+
+
+def be_u32(data: bytes, offset: int) -> int:
+    """按大端读取 4 字节无符号整数。"""
+    return struct.unpack(">I", data[offset : offset + 4])[0]
+
+
+def be_u16(data: bytes, offset: int) -> int:
+    """按大端读取 2 字节无符号整数。"""
+    return struct.unpack(">H", data[offset : offset + 2])[0]
+
+
+def be_f32(data: bytes, offset: int) -> float:
+    """按大端读取 4 字节浮点数。"""
+    return struct.unpack(">f", data[offset : offset + 4])[0]
+
+
+def load_j519_command_rows(pcap: Path, tshark: Path) -> list[list[str]]:
+    """只提取 UDP 60015 的原始字段，后续再按 IP 方向筛选真实下发命令。"""
+    command = [
+        str(tshark),
+        "-r",
+        str(pcap),
+        "-Y",
+        "udp.port==60015",
+        "-T",
+        "fields",
+        "-e",
+        "frame.number",
+        "-e",
+        "frame.time_relative",
+        "-e",
+        "ip.src",
+        "-e",
+        "udp.srcport",
+        "-e",
+        "ip.dst",
+        "-e",
+        "udp.dstport",
+        "-e",
+        "udp.payload",
+    ]
+    output = subprocess.check_output(command, text=True, encoding="utf-8", errors="ignore")
+    rows: list[list[str]] = []
+    for line in output.splitlines():
+        if not line.strip():
+            continue
+        parts = line.split("\t")
+        if len(parts) >= 7:
+            rows.append(parts[:7])
+    return rows
+
+
+def decode_command_records(rows: list[list[str]], client_ip: str, robot_ip: str) -> list[dict]:
+    """把抓包中的 64B J519 命令帧解码成带 IO 信息的结构化记录。"""
+    records: list[dict] = []
+    for frame_no, time_rel, ip_src, _udp_src, ip_dst, _udp_dst, payload_hex in rows:
+        if ip_src != client_ip or ip_dst != robot_ip:
+            continue
+
+        payload = bytes.fromhex(payload_hex)
+        if len(payload) != 64:
+            continue
+
+        io_value = be_u16(payload, 0x18)
+        io_addrs = [bit + 1 for bit in range(16) if io_value & (1 << bit)]
+        targets = [be_f32(payload, 0x1C + index * 4) for index in range(9)]
+        records.append(
+            {
+                "frame_number": int(frame_no),
+                "time_relative_s": float(time_rel),
+                "sequence": be_u32(payload, 0x08),
+                "last_data": payload[0x0C],
+                "write_io_type": payload[0x13],
+                "write_io_index": be_u16(payload, 0x14),
+                "write_io_mask": be_u16(payload, 0x16),
+                "write_io_value": io_value,
+                "io_addrs": io_addrs,
+                "j1_deg": targets[0],
+                "j2_deg": targets[1],
+                "j3_deg": targets[2],
+                "j4_deg": targets[3],
+                "j5_deg": targets[4],
+                "j6_deg": targets[5],
+                "ext1_deg": targets[6],
+                "ext2_deg": targets[7],
+                "ext3_deg": targets[8],
+            }
+        )
+    return records
+
+
+def pick_trigger_first_high_frames(records: list[dict]) -> list[dict]:
+    """由于 io_keep_cycles=2，只记录每组高电平脉冲的第一帧。"""
+    trigger_frames: list[dict] = []
+    previous_high = False
+    for record in records:
+        current_high = record["write_io_value"] > 0
+        if current_high and not previous_high:
+            trigger_frames.append(record)
+        previous_high = current_high
+    return trigger_frames
+
+
+def load_uttc_ms11_config() -> dict:
+    """读取 UTTC_MS11 的示教点和触发配置。"""
+    config = json.loads(CONFIG_PATH.read_text(encoding="utf-8"))
+    return config["flying_shots"]["UTTC_MS11"]
+
+
+def build_trigger_vs_teach_rows(trigger_frames: list[dict], shot_config: dict) -> list[dict]:
+    """按 shot_flags 为 true 的 waypoint 顺序，对齐抓包触发帧和示教点。"""
+    rows: list[dict] = []
+    trigger_waypoint_indices = [index for index, flag in enumerate(shot_config["shot_flags"]) if flag]
+
+    for trigger_no, (frame, waypoint_index) in enumerate(zip(trigger_frames, trigger_waypoint_indices), start=1):
+        teach_rad = shot_config["traj_waypoints"][waypoint_index]
+        teach_deg = [math.degrees(value) for value in teach_rad]
+        actual_deg = [frame[f"j{joint_index}_deg"] for joint_index in range(1, 7)]
+        diffs = [actual_deg[index] - teach_deg[index] for index in range(6)]
+        abs_diffs = [abs(value) for value in diffs]
+        max_error = max(abs_diffs)
+        max_error_axis = f"J{abs_diffs.index(max_error) + 1}"
+        rms_error = math.sqrt(sum(value * value for value in diffs) / 6.0)
+
+        row = {
+            "trigger_no": trigger_no,
+            "waypoint_index": waypoint_index,
+            "frame_number": frame["frame_number"],
+            "sequence": frame["sequence"],
+            "time_relative_s": frame["time_relative_s"],
+            "write_io_value": frame["write_io_value"],
+            "io_addrs": frame["io_addrs"],
+            "config_addr": shot_config["addr"][waypoint_index],
+            "max_error_axis": max_error_axis,
+            "max_error_deg": max_error,
+            "rms_error_deg": rms_error,
+        }
+
+        for joint_index in range(6):
+            joint_no = joint_index + 1
+            row[f"j{joint_no}_actual_deg"] = actual_deg[joint_index]
+            row[f"j{joint_no}_teach_deg"] = teach_deg[joint_index]
+            row[f"diff_j{joint_no}_deg"] = diffs[joint_index]
+
+        rows.append(row)
+
+    return rows
+
+
+def write_csv(path: Path, rows: list[dict]) -> None:
+    """把分析结果落成 UTF-8 CSV，便于后续继续筛选和画图。"""
+    if not rows:
+        raise ValueError(f"No rows to write: {path}")
+
+    serializable_rows: list[dict] = []
+    for row in rows:
+        serializable_row: dict = {}
+        for key, value in row.items():
+            if isinstance(value, list):
+                serializable_row[key] = json.dumps(value, ensure_ascii=False)
+            else:
+                serializable_row[key] = value
+        serializable_rows.append(serializable_row)
+
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=list(serializable_rows[0].keys()))
+        writer.writeheader()
+        writer.writerows(serializable_rows)
+
+
+def build_summary(records: list[dict], trigger_rows: list[dict]) -> dict:
+    """汇总本次分析关心的导出状态和误差统计。"""
+    max_errors = [float(row["max_error_deg"]) for row in trigger_rows]
+    rms_errors = [float(row["rms_error_deg"]) for row in trigger_rows]
+    axis_counter = Counter(str(row["max_error_axis"]) for row in trigger_rows)
+
+    order_only_addr_mismatch = 0
+    real_addr_mismatch = 0
+    for row in trigger_rows:
+        io_addrs = list(row["io_addrs"])
+        config_addr = list(row["config_addr"])
+        if io_addrs != config_addr:
+            if sorted(io_addrs) == sorted(config_addr):
+                order_only_addr_mismatch += 1
+            else:
+                real_addr_mismatch += 1
+
+    return {
+        "pcap_path": str(DEFAULT_PCAP),
+        "all_command_count": len(records),
+        "trigger_count": len(trigger_rows),
+        "existing_runtime_actual_send_exists": (RUNTIME_DATA_DIR / "ActualSendJointTraj.txt").exists(),
+        "existing_runtime_actual_send_has_io_columns": False,
+        "existing_shot_events_exists": (RUNTIME_DATA_DIR / "ShotEvents.json").exists(),
+        "pcap_specific_combined_export_preexisting": False,
+        "average_max_error_deg": sum(max_errors) / len(max_errors),
+        "max_error_deg": max(max_errors),
+        "average_rms_error_deg": sum(rms_errors) / len(rms_errors),
+        "max_error_axis_counter": dict(axis_counter),
+        "order_only_addr_mismatch_count": order_only_addr_mismatch,
+        "real_addr_mismatch_count": real_addr_mismatch,
+    }
+
+
+def main() -> None:
+    """执行抓包提取、示教点对齐、CSV 导出和摘要落盘。"""
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    rows = load_j519_command_rows(DEFAULT_PCAP, DEFAULT_TSHARK)
+    records = decode_command_records(rows, client_ip="192.168.10.10", robot_ip="192.168.10.11")
+    trigger_frames = pick_trigger_first_high_frames(records)
+    shot_config = load_uttc_ms11_config()
+    trigger_rows = build_trigger_vs_teach_rows(trigger_frames, shot_config)
+    summary = build_summary(records, trigger_rows)
+
+    write_csv(OUTPUT_DIR / "2026042802-1_j519_actual_send_all_with_io.csv", records)
+    write_csv(OUTPUT_DIR / "2026042802-1_j519_trigger_frames.csv", trigger_frames)
+    write_csv(OUTPUT_DIR / "2026042802-1_trigger_vs_teach_points.csv", trigger_rows)
+    (OUTPUT_DIR / "2026042802-1_analysis_summary.json").write_text(
+        json.dumps(summary, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+
+    print(json.dumps(summary, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()

analysis/build_trigger_offset_compare_6_7_8.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""生成 2026042802-1 抓包中触发偏移 6/7/8 周期的对照表。"""
+
+from __future__ import annotations
+
+import csv
+import json
+import math
+from pathlib import Path
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+OUTPUT_DIR = REPO_ROOT / "analysis" / "2026042802-1"
+CONFIG_PATH = REPO_ROOT / "Config" / "RobotConfig.json"
+ACTUAL_SEND_CSV = OUTPUT_DIR / "2026042802-1_j519_actual_send_all_with_io.csv"
+TRIGGER_CSV = OUTPUT_DIR / "2026042802-1_j519_trigger_frames.csv"
+OUTPUT_CSV = OUTPUT_DIR / "2026042802-1_trigger_offset_6_7_8_compare.csv"
+OUTPUT_JSON = OUTPUT_DIR / "2026042802-1_trigger_offset_6_7_8_summary.json"
+
+
+def load_rows(path: Path) -> list[dict]:
+    with path.open(encoding="utf-8") as handle:
+        return list(csv.DictReader(handle))
+
+
+def to_float_list(record: dict, prefix: str = "j") -> list[float]:
+    return [float(record[f"{prefix}{index}_deg"]) for index in range(1, 7)]
+
+
+def compute_diff_metrics(actual_deg: list[float], teach_deg: list[float]) -> tuple[list[float], float, float, str]:
+    diffs = [actual_deg[index] - teach_deg[index] for index in range(6)]
+    abs_diffs = [abs(value) for value in diffs]
+    max_error = max(abs_diffs)
+    rms_error = math.sqrt(sum(value * value for value in diffs) / 6.0)
+    max_axis = f"J{abs_diffs.index(max_error) + 1}"
+    return diffs, max_error, rms_error, max_axis
+
+
+def main() -> None:
+    actual_rows = load_rows(ACTUAL_SEND_CSV)
+    trigger_rows = load_rows(TRIGGER_CSV)
+    config = json.loads(CONFIG_PATH.read_text(encoding="utf-8"))["flying_shots"]["UTTC_MS11"]
+    trigger_waypoint_indices = [index for index, flag in enumerate(config["shot_flags"]) if flag]
+
+    actual_index_by_frame = {int(row["frame_number"]): row for row in actual_rows}
+    actual_order_by_frame = {int(row["frame_number"]): idx for idx, row in enumerate(actual_rows)}
+
+    output_rows: list[dict] = []
+    offset_win_counts = {6: 0, 7: 0, 8: 0}
+
+    for trigger_no, (trigger_row, waypoint_index) in enumerate(zip(trigger_rows, trigger_waypoint_indices), start=1):
+        trigger_frame = int(trigger_row["frame_number"])
+        trigger_order = actual_order_by_frame[trigger_frame]
+        teach_deg = [math.degrees(value) for value in config["traj_waypoints"][waypoint_index]]
+
+        window_start = max(0, trigger_order - 20)
+        window_end = min(len(actual_rows) - 1, trigger_order + 20)
+        best_order = trigger_order
+        best_rms = float("inf")
+        best_max = float("inf")
+
+        for candidate_order in range(window_start, window_end + 1):
+            candidate = actual_rows[candidate_order]
+            _, max_error, rms_error, _ = compute_diff_metrics(to_float_list(candidate), teach_deg)
+            score = (rms_error, max_error, abs(candidate_order - trigger_order))
+            if (best_rms, best_max, abs(best_order - trigger_order)) > score:
+                best_order = candidate_order
+                best_rms = rms_error
+                best_max = max_error
+
+        row = {
+            "trigger_no": trigger_no,
+            "waypoint_index": waypoint_index,
+            "best_sample_order": best_order,
+            "best_frame_number": int(actual_rows[best_order]["frame_number"]),
+            "best_sequence": int(actual_rows[best_order]["sequence"]),
+            "best_time_relative_s": float(actual_rows[best_order]["time_relative_s"]),
+        }
+
+        for joint_index in range(6):
+            row[f"teach_j{joint_index + 1}_deg"] = teach_deg[joint_index]
+
+        for offset in (6, 7, 8):
+            target_order = min(len(actual_rows) - 1, best_order + offset)
+            target = actual_rows[target_order]
+            actual_deg = to_float_list(target)
+            diffs, max_error, rms_error, max_axis = compute_diff_metrics(actual_deg, teach_deg)
+
+            row[f"offset_{offset}_frame_number"] = int(target["frame_number"])
+            row[f"offset_{offset}_sequence"] = int(target["sequence"])
+            row[f"offset_{offset}_time_relative_s"] = float(target["time_relative_s"])
+            row[f"offset_{offset}_max_error_axis"] = max_axis
+            row[f"offset_{offset}_max_error_deg"] = max_error
+            row[f"offset_{offset}_rms_error_deg"] = rms_error
+            row[f"offset_{offset}_delta_from_best_ms"] = (
+                float(target["time_relative_s"]) - float(actual_rows[best_order]["time_relative_s"])
+            ) * 1000.0
+            for joint_index in range(6):
+                joint_no = joint_index + 1
+                row[f"offset_{offset}_j{joint_no}_actual_deg"] = actual_deg[joint_index]
+                row[f"offset_{offset}_diff_j{joint_no}_deg"] = diffs[joint_index]
+
+        best_offset = min(
+            (6, 7, 8),
+            key=lambda offset: (
+                row[f"offset_{offset}_rms_error_deg"],
+                row[f"offset_{offset}_max_error_deg"],
+            ),
+        )
+        row["best_of_6_7_8_offset"] = best_offset
+        offset_win_counts[best_offset] += 1
+        output_rows.append(row)
+
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    with OUTPUT_CSV.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=list(output_rows[0].keys()))
+        writer.writeheader()
+        writer.writerows(output_rows)
+
+    summary = {
+        "rows": len(output_rows),
+        "best_offset_win_counts": offset_win_counts,
+        "average_max_error_deg": {
+            str(offset): sum(row[f"offset_{offset}_max_error_deg"] for row in output_rows) / len(output_rows)
+            for offset in (6, 7, 8)
+        },
+        "average_rms_error_deg": {
+            str(offset): sum(row[f"offset_{offset}_rms_error_deg"] for row in output_rows) / len(output_rows)
+            for offset in (6, 7, 8)
+        },
+    }
+    OUTPUT_JSON.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
+    print(json.dumps(summary, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()