Navigation Instruments TRIZ Cases

A virtual dynamic point guides aircraft taxiing along a target trajectory, reducing pilot workload and collision risk in poor visibility.

Selective use of adjacent directional sensors cuts SLAM calculation load and power use while preserving reliable, continuous location tracking.

Pre-generated candidate paths let remote operators quickly compare costs and constraints, then send a navigable route to a stuck vehicle.

Real-time robot position and status mapping helps operators monitor service areas, detect failures, and simulate delivery scenarios.

Routes are planned from task-specific time needs and allowable arrival windows so mobile bodies can avoid delays, overlap, and unsafe rushing.

Priority-based map updates cut loop-closing delay by generating high-importance moving-body areas first while preserving map accuracy.

Plans a collision-free tunnel route by combining map data with vehicle kinematics and boom movement limits for safe underground relocation.

Automatically calculates and displays minimum turn-back altitude with terrain clearance cues, helping crews respond accurately during engine-out emergencies.

Object trajectory matching corrects site-plan layout bias, enabling precise absolute workstation positioning without repeated recalibration.

Accelerometer-based load factor measurements let a flight computer derive glider airspeed without bulky pitot-static probes.

Transforms poses into a common reference system so driverless robots can plan continuous paths across map changes without stopping.

A single GNSS-based controller combines sensing, path calculation, and steering control to cut connectors, enclosures, and farm guidance cost.

Topology map matching lets an indoor robot localize quickly and accurately by comparing local object relationships with a pre-built full map.

A combined cockpit indicator aligns aircraft landing capability with pilot intent and flags degradation to cut workload and misinterpretation.

Cockpit mission computing uses selectable operational constraints and trajectory calculation to show real-time feasibility and reduce crew iterations.

A potential map combines performer, object, and lighting schedules to guide stage cameras along safe, unobtrusive routes.

Stored reference points and orientation cues let moveable objects adapt paths around obstacles while improving area coverage with less manual input.

Anchor points linked to SLAM graph nodes update position and orientation automatically, preserving map integrity during map fusion and updates.

Using distances to signal generators on a moving object, this case improves vehicle position estimation beyond GPS for precise relative navigation.

Real-time turn guidance combines aircraft, GNSS, route, and surface data so pilots keep all wheels on prepared taxiway surfaces.

A terrain-altitude boundary limits LiDAR ray tracing to relevant volume, enabling real-time aerial terrain mapping with less computation.

Electromagnetic anchors and map-based positioning help autonomous mowers navigate uneven garden terrain with higher path accuracy.

Imaging sensors classify working machines and update field operation maps, enabling coordination even when older equipment lacks GNSS or links.

Variable 3D unit areas keep UAV guidance accurate near routes and restricted zones while cutting map data processing load.

A location-based mission structure lets mobile robots navigate, trigger tasks, and return to base without human control.

Adaptive use of IMU, wheel, and mouse sensor data improves robot self-positioning accuracy at high speed while reducing calculation load.

Sensors, zone controllers, and a central algorithm guide modular vehicle subassemblies through assembly zones without fixed conveyors or task-specific AGVs.

Multi-resolution outside maps keep near-field navigation accurate while cutting data and route-search load for distant robotic destinations.

Confidence-based switching between satellite and environment-scan positioning keeps underground vehicles accurately tracked through tunnel transitions.

Permanent maps plus task-specific working copies reduce localization delay and keep autonomous robots accurate after uncontrolled repositioning.

Future occupancy maps help mobile robots predict moving obstacles and plan safer, less conservative paths in changing environments.

By fitting a camera motion trajectory to a reference curve, this case corrects camera-body offsets for more accurate robot pose determination.

Graph-based loop closure aligns local and global occupancy maps to correct SLAM drift and keep obstacle detection reliable.

Slow cruising lets an unmanned vehicle collect sensor data, build maps, and update routes in real time for stable operation in changing environments.

Precomputed rejection speed thresholds help pilots decide whether to abort take-off after an engine event while preserving safe stopping distance.

Ascent-based calibration aligns stereo vision altitude readings with onboard reference data to extend UAV sensing range and accuracy.

Accumulated positioning error triggers base-station correction so unmanned vehicles can maintain route coverage and reach operation-grade precision.

HD map-based forward path prediction filters external objects by driving relevance, cutting sensor load while preserving ADAS safety coverage.

Warp points and inverse-distance interpolation align old and new lane maps despite segmentation mismatches and localized roadway changes.

Correlating drive data across nearby road segments builds sparse navigation maps that cut storage load while preserving route accuracy.

Mobile agents transfer platoon control after event detection, enabling continuous building search, mapping, and real-time incident awareness.

Grid-map fusion of road-user perception and static environment data predicts multimodal object positions with stable complexity in dense traffic.

Coordinates old and new navigation maps across multiple work machines by checking active orders before switching to updated map data.

Dual position estimation lets an autonomous running device keep navigating when markers are missing or lighting reduces visual localization accuracy.

Dynamic gain adjustment keeps boundary wire signals within thresholds, improving mobile position detection under motor and ambient EMI.

Pre-collected scanner maps add vertical constraints so LiDAR and navigation pose calibration is more accurate for high-precision mapping.