「drone」の検索結果

Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).

日本語の概要は準備中です。原文の説明を表示しています。

Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).

日本語の概要は準備中です。原文の説明を表示しています。

Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).

日本語の概要は準備中です。原文の説明を表示しています。

Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).

日本語の概要は準備中です。原文の説明を表示しています。

Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).

日本語の概要は準備中です。原文の説明を表示しています。

Use whenever the user mentions Betaflight, FPV drone firmware, flight-controller config, PID/filter/rates tuning, or blackbox log analysis — or shares a Betaflight artifact: a CLI diff/dump (lines like 'diff all', 'set ', 'feature ', 'resource ') or a .bbl/.bfl blackbox log, even when not named as such — or runs a chirp tuning session via Sweep Studio / FPVLogForge (a chirp log ID to analyse, 'analyse log', 'résultat chirp', iterative Bode/step/Ms tuning). Covers flight symptoms (oscillations, wobble, propwash, jello, hot motors, drift), motor/ESC and RX/ELRS/CRSF setup, builds from 3" to 10" (cinewhoop, tinywhoop, longrange, X-class), version migrations (4.4 → 4.5 → 4.6), and configuring a new drone from scratch (setup wizard). Trigger even when phrased casually ("mon drone wobble", "PID help", "config FC") — under-triggering is worse than over-triggering.

日本語の概要は準備中です。原文の説明を表示しています。

Expert en cinématographie drone (DJI, planification de vol, réglementations, post-traitement, mapping)

日本語の概要は準備中です。原文の説明を表示しています。

Establish MAVLink connections between MATLAB and PX4/ArduPilot autopilots. Use when connecting to a drone, flight controller, or autopilot via MAVLink protocol over UDP. Covers dialect setup, UDP transport, timer-based heartbeat, and client discovery. Use when: "connect to PX4", "MAVLink connection", "heartbeat", "ground control station", "GCS", "connect to ArduPilot", "drone communication", "mavlinkio", "SITL".

日本語の概要は準備中です。原文の説明を表示しています。

Process drone survey data for construction sites. Generate orthomosaics, DEMs, point clouds, calculate volumes, track progress, and integrate with BIM models for comparison.

日本語の概要は準備中です。原文の説明を表示しています。

Process, analyze, and visualize geospatial data at scale. Handles drone imagery, GPS tracks, GeoJSON optimization, coordinate transformations, and tile generation. Use for mapping apps, drone data processing, location-based services. Activate on "geospatial", "GIS", "PostGIS", "GeoJSON", "map tiles", "coordinate systems". NOT for simple address validation, basic distance calculations, or static map embeds.

日本語の概要は準備中です。原文の説明を表示しています。

Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.

日本語の概要は準備中です。原文の説明を表示しています。

VISONSDK ドローン (54X / KF615 系) を PC から操縦・映像確認・顔追従するクライアント群。 iPhone 上の iOS アプリ (test.app) が制御サーバー (TCP 8080) と MJPEG 映像サーバー (HTTP 8081/stream) を提供し、USB 越しに iproxy で PC に転送する。「ドローンを飛ばす」「ドローン映像を見る」 「ドローンに顔を追わせる」「離陸/着陸/スティック操作」などで使う。

Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.

日本語の概要は準備中です。原文の説明を表示しています。

Workflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas. Use when user says \"robotics idea discovery\", \"机器人找idea\", \"embodied AI idea\", \"机器人方向探索\", \"sim2real 选题\", or wants ideas for manipulation, locomotion, navigation, drones, humanoids, or general robot learning.

日本語の概要は準備中です。原文の説明を表示しています。

Workflow 1 adaptation for robotics and embodied AI. Orchestrates robotics-aware literature survey, idea generation, novelty check, and critical review to go from a broad robotics direction to benchmark-grounded, simulation-first ideas. Use when user says "robotics idea discovery", "机器人找idea", "embodied AI idea", "机器人方向探索", "sim2real 选题", or wants ideas for manipulation, locomotion, navigation, drones, humanoids, or general robot learning.

日本語の概要は準備中です。原文の説明を表示しています。

Hunt CI/CD pipeline vulnerabilities — GitHub Actions workflow injection (pull_request_target Pwnrequest + ${{ }}-into-shell), self-hosted runner poisoning, OIDC trust-policy abuse, Jenkins script-console RCE and CVE-2024-23897 file read, GitLab CI runner-token registration, Terraform state file leakage, artifact/log secret leakage, pipeline env-var disclosure. Use when target has a public GitHub/GitLab org, exposed CI dashboards (Jenkins/TeamCity/Drone/Argo), or build artifacts/images are reachable.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when converting natural language flight commands into waypoints and timing for a drone simulator. Covers parsing commands like "Take off to X m height in Y seconds", "Hover at X m height for Y seconds", "Fly from (x,y,z) to (x',y',z') in T seconds", and "Land from X m height in Y seconds" into structured (4×n) waypoint arrays and segment mode lists.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when visualising drone simulation results. Produces three matplotlib figures — desired vs actual trajectories, instantaneous error, and cumulative absolute error — for all 5 state groups (position, orientation, velocity, angular velocity, acceleration). Saves figures to a plots/ directory automatically.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when computing 3D step-response performance metrics for point-to-point drone flight — rise time, settling time, percent overshoot, and steady-state error based on Euclidean distance to the final target. Use instead of 1D stepinfo for any flight where all three position axes move simultaneously.

日本語の概要は準備中です。原文の説明を表示しています。

Monitor construction progress using computer vision. Analyze site photos and drone imagery to track work completion, detect safety issues, and compare against BIM models.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user wants to turn a static scene, character images, aerial map, drawn path, or route-control image into an immersive first-person FPV AI video prompt, especially Seedance/Kling/Runway/Veo style one-shot videos with numbered stop markers, red-line path control, world-map flythroughs, camera route planning, variable character counts, non-human POVs such as drones, pets, robot vacuums, character references, timed interactions, dialogue, spatial audio, and negative constraints.

日本語の概要は準備中です。原文の説明を表示しています。

Turn a feature request into a minimal, file-level implementation plan before any code.

日本語の概要は準備中です。原文の説明を表示しています。

Audit precision agriculture and crop management software for yield prediction model accuracy, soil analysis integration, irrigation optimization algorithms, pest and disease detection pipelines, satellite and drone imagery processing, weather data integration.

日本語の概要は準備中です。原文の説明を表示しています。

Hunt CI/CD pipeline vulnerabilities — GitHub Actions workflow injection (pull_request_target Pwnrequest + ${{ }}-into-shell), self-hosted runner poisoning, OIDC trust-policy abuse, Jenkins script-console RCE and CVE-2024-23897 file read, GitLab CI runner-token registration, Terraform state file leakage, artifact/log secret leakage, pipeline env-var disclosure. Use when target has a public GitHub/GitLab org, exposed CI dashboards (Jenkins/TeamCity/Drone/Argo), or build artifacts/images are reachable.

日本語の概要は準備中です。原文の説明を表示しています。