Navigation Instruments TRIZ Cases

Area-signal validation checks infrastructure maps before release, preventing outdated data from guiding automated vehicles.

Fused camera and radar or lidar data classify road areas as drivable, blocked, or unknown, improving free-space assessment for driver assistance.

Local time and distance tracking lets vehicles meter driving assistance fees accurately in real time without stable network access.

Map and geospatial filtering narrows sensor frames for rare object training, cutting curation time while preserving detection accuracy.

Mapped sensor simulation identifies regions where navigation data is insufficient, enabling safer route changes for autonomous vehicles.

Sensor-driven factor graph updates correct lane line positions while driving, improving localization and reducing lane recognition errors.

Weighted matching of lane position, color, type, and curvature improves autonomous vehicle localization and map alignment.

Candidate speed profiles are scored for energy use and road-rule compliance to enable smoother, obstacle-aware lane changes.

Heading and lateral error feedback adjusts front-wheel angle for accurate reverse trajectory tracking in parking and narrow-road maneuvers.

A neon-style 3D vehicle map highlights essential roads, buildings, and vehicle position to cut clutter and speed driver recognition.

Using elevation spectrum shape instead of peak position, this case improves automotive radar object height estimation despite low resolution.

Redundant contour apexes are removed from occupancy grids so moving bodies can generate smoother, more suitable routes with less path-planning load.

Dynamic spatial maps update door and window status so indoor drones can recalculate routes and navigate changing property layouts autonomously.

Circle-based path search skirts polygon obstacles to find a flyable aircraft trajectory faster while preserving vertical and lateral margins.

Combining DWA local planning with Next Best Q-learning helps robots adapt online, cut repeated replanning, and avoid offline training data.

Edge-following data and magnetometer direction are matched to a saved pool map, enabling fast robotic pool cleaner localization without initial position data.

Bounding vectors define Apollonius regions from position and velocity uncertainty to flag aircraft hazards with fewer false alarms and less computation.

Thermal and wind lift extend engine-out gliding range, helping onboard control select a reachable landing site and flight path.

Fiducial markers and map-graph optimization help UAVs distinguish clustered charging pads without precise GPS, improving docking accuracy.

BIM semantic data lets robots localize on permanent building features, avoiding map drift and manual updates on dynamic sites.

Internal and external field maps help agricultural machines maintain precise positioning and navigation even when operating outside the field.

Combining GPS positioning with internal and external map creation helps agricultural machines navigate accurately beyond field boundaries.

Fiducial tags and re-initialization correct out-of-position drift in mobile carriers, improving navigation accuracy and reliable operation.

GAN-generated landmark images cut annotation effort and data load while training localization networks for precise mobile position determination.

Actual travel trajectories reposition passing points and route lines, cutting repeated route corrections during real-world verification.

Combining UAV and ground SEM enables accurate landfill methane leak mapping and zone-average concentration assessment with less manual surveying.

Boundary segment classification guides farm vehicle routes to avoid hazards, cut fuel use and soil compaction, and preserve workable field area.

Boundary segment classification guides agricultural vehicle routes to avoid hazards, protect surroundings, and preserve workable field area.

By separating obstacle data for localization from coverage planning, this case avoids unfilled travel areas while maintaining position estimation accuracy.

Structured light and dual autonomous pipe robots map underground assets and detect anomalies on smooth PVC interiors without service disruption.

Position estimation stays accurate by excluding outdated worksite map areas and referencing only map data that matches current field conditions.

By excluding map areas from completed or changed worksites, the assist system improves working machine position estimation in shifting field conditions.

A shared floor plan uses occupancy updates and room segmentation to keep multi-robot maps geometrically consistent with simpler sensors.

Continuous ground-air data updates let pilots replan routes around changing weather, traffic, and airspace with lower latency and workload.

Divided 2D maps linked by connection information let robots localize accurately on slopes despite height and posture changes.

Doppler radar points and inertial attitude are fused to filter moving objects, limit drift, and keep vehicle SLAM accurate in poor visibility.

A time-ordered automation summary GUI helps pilots regain situational awareness after autoland deactivation and resume manual control safely.

When satellite signals are blocked, the controller changes movement and switches with inertial navigation to maintain precise positioning.

A shared 3D map aggregates sensor feedback from AR devices, scanners, and robots to improve positioning accuracy on changing construction sites.

A time-ordered automation summary display helps pilots regain situational awareness after autoland operation and resume manual control.

Main and supplemental laser pulses control cooling during membrane vent-hole sealing to reduce surface asperity and improve seal quality.

Unequal-interval map parcels merge areas with the same characteristics, cutting robot map data size without losing navigation resolution.

Attribute-based map data selection and delivery timing help autonomous robots receive the right space data when needed for smoother navigation.

Loop closure and pose graph updates correct odometry drift, helping robots generate precise indoor maps in changing environments.

Augmented ultrasonic training data varies road surfaces, sensor poses, and sensor details to reduce bias and improve autonomous mapping accuracy.

Elevation spectrum shape features replace peak detection to estimate object height more reliably despite low radar resolution and pitch variation.

UWB beacons near the landing site replace interference-prone satellite guidance with precise vehicle landing navigation in urban areas.

Machine learning precomputes lower-emission vehicle paths and validates them for safety and efficiency without heavy real-time processing.

Differential map analysis flags local-global obstacle conflicts, then targets extra sensing to verify anomalies and improve AMD navigation.

Reduced mapping data and inertial sensing enable real-time underground localization and mapping where GNSS signals are unavailable.